WK 8 Annotated Bibliography Assignment

Please see attachment for instructions and directions. All document needed are attached as well as a simple of how it needs to be done.

Assignment: Literature Review: The Use of Clinical Systems to Improve

Outcomes and Efficiencies

New technology—and the application of existing technology—only appears in healthcare settings after careful and significant research. The stakes are high, and new clinical systems need to offer evidence of positive impact on outcomes or efficiencies.

Nurse informaticists and healthcare leaders formulate clinical system strategies. As these strategies are often based on technology trends, informaticists and others have then benefited from consulting existing research to inform their thinking.

In this Assignment, you will review existing research focused on the application of clinical systems. After reviewing, you will summarize your findings.

To Prepare:

· Review the Resources and reflect on the impact of clinical systems on outcomes and efficiencies within the context of nursing practice and healthcare delivery.

· Conduct a search for recent (within the last 5 years) research focused on the application of clinical systems. The research should provide evidence to support the use of one type of clinical system to improve outcomes and/or efficiencies, such as “the use of personal health records or portals to support patients newly diagnosed with diabetes.”

· Identify and select 4 peer-reviewed research articles from your research.

The Assignment: (4-5 pages not including the title and reference page)

In a 4- to 5-page paper, synthesize the peer-reviewed research you reviewed. Format your Assignment as an Annotated Bibliography. Be sure to address the following:

· Identify the 4 peer-reviewed research articles you reviewed, citing each in APA format.

· Include an introduction explaining the purpose of the paper.

· Summarize each study, explaining the improvement to outcomes, efficiencies, and lessons learned from the application of the clinical system each peer-reviewed article described. Be specific and provide examples.

· In your conclusion, synthesize the findings from the 4 peer-reviewed research articles.

· Use APA format and include a title page.

Assignment must include

My field of practice is ENT (Ear, Nose and Throat and the assignment must be related with the allergy field. Please see attachments for the articles to be use related to my current nursing field

Please see the example attached on how to complete and do this assignment.

Original Paper

Adherence to Prescribed E-Diary Recording by Patients With
Seasonal Allergic Rhinitis: Observational Study

Marco Di Fraia1,2, MD; Salvatore Tripodi3,4,5, MD; Stefania Arasi1,6, MD, PhD; Stephanie Dramburg1, MD; Sveva

Castelli1, MD; Danilo Villalta7, MD; Francesca Buzzulini7, MD; Ifigenia Sfika3, MD; Valeria Villella3, MD; Ekaterina

Potapova1, MSc; Serena Perna1, MSc; Maria Antonia Brighetti8, PhD; Alessandro Travaglini8, MA; Pierluigi Verardo9,

MA; Simone Pelosi5, MA; Anna Maria Zicari2, MD; Paolo Maria Matricardi1, MD
1Department of Pediatric Pulmonology, Immunology and Intensive Care Medicine, Charité University Medicine, Berlin, Germany
2Department of Pediatrics, Sapienza University of Rome, Rome, Italy
3Pediatric Allergology Unit, Sandro Pertini Hospital, Rome, Italy
4Allergology Service, Policlinico Casilino, Rome, Italy
5TPS Production, Rome, Italy
6Pediatric Allergology Unit, Department of Pediatric Medicine, Bambino Gesù Children’s Research Hospital, Rome, Italy
7Department of Immunology-Allergy, Santa Maria degli Angeli Hospital, Pordenone, Italy
8Department of Biology, University of Rome Tor Vergata, Rome, Italy
9Center of Aerobiology, Agenzia Regionale per la Protezione Ambientale, Pordenone, Italy

Corresponding Author:
Paolo Maria Matricardi, MD
Department of Pediatric Pulmonology, Immunology and Intensive Care Medicine
Charité University Medicine
Augustenburger Platz 1
Berlin, 13353
Germany
Phone: 49 30 450 566 406
Email: paolo.matricardi@charite.de

Abstract

Background: Complete diagnosis and therapy of seasonal allergic rhinoconjunctivitis require evidence that exposure to the
sensitizing pollen triggers allergic symptoms. Electronic clinical diaries, by recording disease severity scores and pollen exposure,
can demonstrate this association. However, patients who spontaneously download an e-diary app show very low adherence to
their recording.

Objective: The objective of our study was to assess adherence of patients with seasonal allergic rhinitis to symptom recording
via e-diary explicitly prescribed by an allergist within a blended care approach.

Methods: The @IT-2020 project is investigating the diagnostic synergy of mobile health and molecular allergology in patients
with seasonal allergic rhinitis. In the pilot phase of the study, we recruited Italian children (Rome, Italy) and adults (Pordenone,
Italy) with seasonal allergic rhinitis and instructed them to record their symptoms, medication intake, and general conditions daily
through a mobile app (Allergy.Monitor) during the relevant pollen season.

Results: Overall, we recruited 101 Italian children (Rome) and 93 adults (Pordenone) with seasonal allergic rhinitis. Adherence
to device use slowly declined during monitoring in 3 phases: phase A: first week, ≥1267/1358, 90%; phase B: second to sixth
week, 4992/5884, 80% to 90%; and phase C: seventh week onward, 2063/2606, 70% to 80%. At the individual level, the adherence
assessed in the second and third weeks of recording predicted with enough confidence (Rome: Spearman ρ=0.75; P<.001; Pordenone: ρ=0.81; P<.001) the overall patient adherence to recording and was inversely related to postponed reporting (ρ=–0.55; P<.001; in both centers). Recording adherence was significantly higher during the peak grass pollen season in Rome, but not in Pordenone.

Conclusions: Adherence to daily recording in an e-diary, prescribed and motivated by an allergist in a blended care setting,
was very high. This observation supports the use of e-diaries in addition to face-to-face visits for diagnosis and treatment of
seasonal allergic rhinitis and deserves further investigation in real-life contexts.

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(J Med Internet Res 2020;22(3):e16642) doi: 10.2196/16642

KEYWORDS

mobile health; e-Diary; precision medicine; pollen; seasonal allergic rhinitis; blended care

Introduction

Background
Seasonal allergic rhinoconjunctivitis (SAR) affects patients
exposed to pollens to which they are sensitized. The etiological
diagnosis and therapy of SAR require a demonstration that
exposure to the sensitizing pollen triggers allergic symptoms
[1]. Objectively, this link is established by a positive outcome
to nasal allergen provocation tests [2] or allergen exposure in
pollen chambers [3]. Unfortunately, both these tests are costly
and time consuming and are mostly used in clinical trials [4].
In clinical daily life, a causality between pollen exposure and
symptoms is often assessed by a careful retrospective clinical
history [5]. However, recall biases make the diagnosis based
on retrospective data somewhat imprecise, especially in patients
apparently sensitized to multiple pollens that share the same
pollination periods [6], which is a frequent setting in
Mediterranean countries [7].

This diagnostic problem can be partially solved through a
prospective clinical history, based on the patient’s daily
recording of symptoms and medication intake in a clinical diary
[8]. Indeed, the trajectories of daily symptom scores or a
combined symptom and medication score (CSMS) are free from
recall bias and can be matched with daily concentration counts,
obtained in parallel, of the potentially eliciting pollen sources
[9,10]. While traditional and time-consuming clinical diaries
on paper records are rarely used, electronic clinical diaries
(e-diaries) have become increasingly prevalent [11,12]. E-diaries
are apps consisting of short questionnaires filled in daily by the
patient, usually on his or her mobile phone or tablet computer
[11-13]. Recording e-diaries is easy and quick, and the software
automatically provides daily scores, time trajectories, and
descriptive reports [8,10-16].

Several e-diaries are available for pollen allergies in European
countries, and some of them have also been used in trials or
observational studies [8,10,12,14-19]. In most of the study
settings, the app was directly downloaded by the patients, with
no or only occasional intervention by their allergist [14-17].
The observational studies were characterized by large population
size (more than 9000 participants) and big datasets (112,054
registered visual analog scale [VAS] data) [14], balanced by a
poor mean adherence (<10%) to daily recording [14,15].

Objective
We hypothesized that the patients’ adherence to recording of
e-diaries would be significantly increased if the rationale and
the use of the e-diary were personally explained by an allergist
to the patient (blended approach). To test this hypothesis, we
examined the rate and cofactors of adherence to recording of
an e-diary among Italian patients with SAR participating in the
@IT-2020 project, a study of combined molecular diagnostics
and mobile health for allergen immunotherapy in patients with
SAR.

Methods

@IT-2020 Project
The pilot study of the @IT-2020 project was carried out in 2
Italian centers differing significantly in terms of environmental
setting and patient characteristics.

Climate and Study Area
Pordenone, Italy, is a city with about 50,000 inhabitants, which

extends over an area of 38 km2 [20]. Pordenone is 600 km north
of Rome and the territory is located in northeastern Italy, about
50 km from the Adriatic Sea, in the Po-Veneto plain south of
the Carnic Pre-Alps, in the continental biogeographical region
[21]. It has a mean annual temperature of 13.1°C and mean
rainfall of 1292 mm [22].

Rome, Italy, has 3 million inhabitants in an area of almost 1300

km2 [20] and is 20 km from the Tyrrhenian Sea. Rome is located
in the Mediterranean biogeographical region [21] with mean
annual temperature of 15.7°C and mean rainfall of 798 mm
(Rome Monte Mario) [22].

Study Population
Between November 2016 and February 2017, we recruited 101
children aged 10 to 18 years at Ospedale Sandro Pertini in Rome
and 93 adults aged over 18 years at Ospedale Santa Maria degli
Angeli in Pordenone. Criteria for eligibility were (1) being
followed up for at least one year for allergic rhinoconjunctivitis
(objectively confirmed by skin prick tests or in vitro
immunoglobulin E tests, or both) due to outdoor aeroallergens
(pollen or spores), (2) residing within 30 km of the
aerobiological station of the study center, (3) having no intention
to change residence in the 6 months after recruitment, and (4)
being able to use a mobile phone (by the patient or the patient’s
parents). Exclusion criteria were (1) previous allergen
immunotherapy for any outdoor allergen, and (2) any other
severe nonatopic chronic disease. All participants (in the case
of children, their parents or guardians) provided informed
written consent to the clinical investigations.

Study Design
Recruited patients underwent a first clinical assessment (T0),
including clinical questionnaires, during which they were
instructed on the use of the Allergy.Monitor (Technology Project
and Software [TPS] Production, Rome, Italy) mobile app to
monitor their symptoms and medication intake during the
following study period. According to the timing of retrospective
symptoms and skin prick test results, participants were assigned
an individual monitoring period during the suspected high
season of the putative eliciting pollen. During this period,
participants were asked to monitor their eye, nose, and lung
symptoms, as well as their effect on daily activities and daily
medication intake, and report them via Allergy.Monitor. After
the monitoring period, all participants underwent a second

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clinical assessment (T1), including a repetition of the initial
clinical questionnaires focused on the past pollen season,
internationally validated by the International Study of Asthma
and Allergies in Childhood [23], the Allergic Rhinitis and its
Impact on Asthma (ARIA) initiative [24,25], and the Global
Initiative for Asthma [26]. The study design and procedures
had been approved by the ethics committee of each participating
center.

Skin Prick Tests
Skin prick tests were performed using a standard panel of
commercial extracts (ALK-Abelló, Milan, Italy) of outdoor and
indoor aeroallergens (Alternaria, Bermuda grass, birch, cat
dander, cypress, dog dander, hazel, house dust mite, mugwort,
olive tree, plane tree, ragweed, Russian thistle, timothy grass,
and pellitory-of-the-wall). Histamine 0.1 mg/mL and glycerol
solution were used as positive and negative controls,
respectively. Morrow Brown needles were used to prick the
skin and the wheal reactions were read after 15 minutes. A wheal
equal to or greater than 3 mm after subtraction of the negative
control was regarded as positive.

Pollen Counts and

Pollen Periods

The pollen count data, acquired from March 1 to September 30,
2016, were provided by the pollen stations of Rome (Tor
Vergata University) and Pordenone (Agenzia Regionale per la
Protezione dell’Ambiente del Friuli Venezia Giulia). Pollen
was collected using a VPPS 2000 pollen sampler (Lanzoni srl,

Bologna, Italy), and data were acquired as reported in Standard
UNI CEN/TS 16868:2015 [27]. Pollen periods were determined
(1) according to the 2017 European Academy of Allergy and
Clinical Immunology (EAACI) position paper on pollen
exposure times [28] (EAACI criteria), and (2) by adapting these
criteria to the pollen situation in Italy (local criteria).

Allergy.Monitor
Allergy.Monitor is a mobile app designed for daily reporting
of symptoms and medication intake related to allergic rhinitis
or asthma. In this study, medical doctors, on the basis of clinical
history, defined a time frame (prescription period; Figure 1)
for each patient, in which he or she was encouraged to fill in a
daily questionnaire regarding his or her symptoms and
medication intake. The system offers a bidirectional interaction
between physician and patient via email, chat, and text
messaging. Patients not entering their data for 2 consecutive
days received an automatic alert message on their mobile phone
or by email; after 4 days without reporting, the alert was
followed by a phone call from the physician or nurse. The
patient could insert data referring to 1 day only on the same day
or on the following one (postponed reporting). For each
participant, adherence to prescription was calculated as the
number of actual reporting days / prescription period ×100;
adherence was calculated as the number of actual reporting
days / reporting period ×100; and postponed reporting was
calculated as the number of postponed reporting days / actual
reporting days ×100.

Figure 1. Graphical representation of definitions used in this study. The box line represents the monitored period (each box is a specific day) of a
hypothetical participant. In this example the medical doctor, according to the individual participant’s clinical history, invited the patient to fill in the
e-diary questionnaire for 54 days (prescription period). The patient started to record symptoms 6 days after the prescribed beginning day (delayed
reporting start) and finished recording symptoms 5 days before the prescribed ending day (advanced reporting end). Thus, the reporting period lasted
43 days, during which the participant did not fill in the e-diary questionnaire for 7 days (missing reporting days). Overall, the participant filled in the
e-diary questionnaire for 36 days (reporting days).

Symptom and Medication Scores
We used the following symptom and medication scores in this
study: Rhinoconjunctivitis Total Symptom Score (RTSS; score
0-18) [29]; CSMS (score 0-6) [30]; and VAS (score 0-10) [31].

RTSS and CSMS were calculated automatically by the
Allergy.Monitor app, for every reporting day, on the basis of 4
questions on nasal symptoms (sneezing, rhinorrhea, nasal
pruritus, nasal congestion), 2 on ocular symptoms (itchy eyes,
watery eyes), and 3 questions on medication intake

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(antihistaminic drugs, local corticosteroids, systemic
corticosteroids). The severity of each of the symptoms was also
measured by the patient using 4 different emoticons, each
representing a distinct severity grade (no symptoms, mild,
moderate, or severe). Overall severity was also measured by a
VAS in response to the question “How do you feel in relation
to your allergic symptoms today?”

Statistics
We summarized data as numbers (n) and frequencies (%) if they
were categorical and as mean or median and standard deviation
or interquartile range if quantitative. We examined all described
analyses for each of the study centers (Rome and Pordenone).
We evaluated the prevalence of atopic sensitization (skin prick
test ≥3 mm) to airborne allergens. For every pollen period
considered, we calculated adherence values (see above for
definition) for each participant and compared their means using
a nonparametric Friedman test for repeated measures. We
adjusted the P of multiple comparison by the Bonferroni
correction. We studied adherence trends over time considering
the time (in days) that had passed since the first day of the
reporting period. We used the Spearman rank correlation
coefficient to investigate the relationship between total
adherence (%), postponed reporting (%), and adherence achieved
between the seventh and the 21st reporting day (%). Mean
CSMS scores by time were computed for the local whole season.

We considered P<.05 to be statistically significant. Statistical analyses were performed with R version 3.2.3 (R Foundation).

Results

Study Population
Overall, 101 children (Rome) and 93 adults (Pordenone) with
mean (SD) ages of 13.7 (SD 2.8) and 34.3 (14.4), respectively,
met the inclusion criteria (Table 1). Male sex was slightly more
frequent in both populations: 62.4% (63/101) for Rome and
56% (52/93) for Pordenone. At T0, according to the ARIA
questionnaire, the population in Pordenone was characterized
by a higher prevalence of moderate to severe (intermittent and
persistent) allergic rhinitis than in Rome (90/93, 97% vs 51/101,
50.5%, respectively). At T1, this difference was less evident
(64/75, 85% vs 68/91, 75%). The prevalence of allergic asthma
was similar in both groups (Rome: 28/101, 27.7%; Pordenone:
24/93, 26%), whereas the Rome population seemed to be more
affected by oral allergy syndrome, urticaria, atopic dermatitis,
and anaphylaxis (Table 1). Grass pollen was the most relevant
allergen in both study populations. Positive skin prick test
reactions to olive tree and cypress were more frequent in Rome,
while sensitization to birch was more prevalent in Pordenone.
Sensitization to indoor allergens was equally prevalent in both
populations.

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Table 1. Characteristics of the study population.

Pordenone (n=93)Rome (n=101)Characteristic

52 (56)63 (62.4)Males, n (%)

34.3 (14)13.7 (2.8)Age (years), mean (SD)

Allergic rhinitis, n (%)

15 (8-22)6 (4-8)Age at onset (years), median (IQRa)

ARIAb classification at T0

1 (1)19 (18.8)Mild intermittent

2 (2)31 (30.7)Mild persistent

17 (18)11 (10.9)Moderate to severe intermittent

73 (79)40 (39.6)Moderate to severe persistent

ARIA classification at T1c, n (%)

2 (3)6 (7)Mild intermittent

9 (12)17 (19)Mild persistent

13 (17)4 (4)Moderate to severe intermittent

51 (68)64 (70)Moderate to severe persistent

Other allergic comorbidities, n (%)

24 (26)28 (27.7)Allergic asthma

23 (25)32 (32.3)Oral allergic syndrome

8 (9)19 (19.2)Urticaria or angioedema

11 (12)28 (28.3)Atopic dermatitis

1 (1)4 (4.0)Gastrointestinal disorders

1 (1)10 (10.1)Anaphylaxis episode

2 (2)5 (5.1)Other

aIQR: interquartile range.
bARIA: Allergic Rhinitis and its Impact on Asthma.
cStudy population at T1: Rome, n=91; Pordenone, n=75.

Pollen Periods

The graphical representation of grass pollen counts (grains/m3)
highlighted differences between the 2 cities. The maximum

grass pollen count in Rome (199 grains/m3) was higher than in

Pordenone (145 grains/m3), and the grass pollination period
was longer in Rome. Grass pollen periods in 2016 differed
significantly if calculated according to EAACI criteria or local
criteria (Table 2). While we used EAACI criteria for their
reproducibility and standardization, the application of locally
adapted criteria resulted in shorter and less fragmented periods.

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Table 2. Grass pollen period criteria and duration, by study center.

Pordenone (n=93)Rome (n=101)CriteriaPollen period

Adherence
mean (95% CI)
(%)

Cumulative
duration
(days), n

No. of
time inter-

valsa

Adherence
mean (95% CI)
(%)
Cumulative
duration
(days), n

No. of time

intervalsa

86.3 (83.5-89.2)132282.1 (79.3-84.9)13225 days (out of 7 consecutive days) each

with ≥3 pollen grains/m3 and with a
sum of ≥30 pollen grains/m3

EAACI whole sea-

sonb

86.2 (83.1-89.2)72281.9 (79.1-84.8)9715 days (out of 7 consecutive days) each

with ≥10 pollen grains/m3 and with a
sum of ≥100 pollen grains/m3

Local whole sea-

sonc

90.4 (87.5-93.3)13281.0 (77.6-84.4)2853 consecutive days, each with ≥50
pollen grains/m3

EAACI peak sea-

sonb

90.3 (87.6-92.9)24281.0 (78.0-84.1)5513 days (out of 5 consecutive days) each

with ≥50 pollen grains/m3
Local peak seasonc

89.3 (86.4-92.1)23980.9 (77.9-84.0)4518Days with at least 50 pollen grains/m3EAACI high daysb

aSee Figure 1 for specifications of time periods.
bEuropean Academy of Allergy and Clinical Immunology (EAACI) criteria [28].
cAdaptation of EAACI criteria to the local scenario.

Adherence to E-Diary Recording
The mean prescription period was longer in Rome than in
Pordenone (76.2, 95% CI 70.4-82.0 vs 53.9, 95% CI 50.1-57.7
days, respectively). The pattern was similar for the mean
reporting period (Rome: 70.6, 95% CI 64.9-74.4 vs Pordenone:
48.2, 95% CI 44.6-51.7 days) (Figure 2). Mean adherence levels
were 85.7% (SD 13.9) in Pordenone and 82.3% (SD 13.7) in
Rome. The analysis of mean adherence values by reporting day
showed a similar trend for both participating study centers. In

Rome, the adherence trend by reporting day displayed 3 different
phases: phase A, a first phase of 6 days with an adherence 93.1%
(564/606); phase B, a second phase of approximately 40 days,
during which the adherence fluctuated around 83.65%
(2834/3388); and phase C, a final phase of slowly declining
adherence, oscillating around 78.55% (1952/2485). Pordenone’s
adherence trend by reporting day followed the same evolution
for phases A and B. Due to a shorter pollen season and mean
prescribed period, we did not investigate phase C in Pordenone
(Figure 2).

Figure 2. Adherence (%) by reporting day and study center. It is possible to describe three phases (indicated by light background color): the first phase
(A), lasting 6 days, during which adherence fell from 100% to 90%; the second phase (B), lasting approximately 20 days, during which adherence
fluctuated until reaching 88%; and the final phase (C), during which it slowly declined to 80%. RM: Rome; PN, Pordenone.

Interestingly, the total adherence was directly proportional to
the adherence assessed between the seventh and 21st reporting
days (Spearman ρ=0.75; P<.001 and ρ=0.81; P<.001 for Rome

and Pordenone, respectively) (Figure 3) and inversely related,
although with less intensity, to postponed reporting (ρ=–0.55;
P<.001 for both Rome and Pordenone) (Figure 4). In both

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populations, the mean RTSS, evaluating symptoms of the eyes
and nose, showed a parallel trend with the mean VAS scores
assessing the general disease-related impairment. Also, the
CSMS followed a similar trend but with less distinct variance
(Figure 5).

Mean adherence values differed only slightly in Rome during
the different pollen periods (Figure 6, part A). By contrast,
adherence values were significantly higher in Pordenone during
the peak pollen season and the high day (Figure 6, part B).

Figure 3. Correlation between adherence achieved between the seventh and the 21st reporting days and total reporting period adherence, by study
center: (A) Rome (n=101); (B) Pordenone (n=93).

Figure 4. Correlation between postponed reporting (%) and total reporting period adherence (%) by study center: (A) Rome (n=101); (B) Pordenone
(n=93).

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Figure 5. Mean visual analog scale (VAS) score, Rhinoconjunctivitis Total Symptom Score (RTSS), and combined symptom and medication score
(CSMS) by time considering the local whole season of grass pollen in (A) Rome (n=101) and (B) Pordenone (n=93; see Figure 2 and Table 2 for
definitions).

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Figure 6. Mean (95% CI) adherence to recording of the e-diary during the pollen season in children affected by seasonal allergic rhinitis in (A) Rome
and (B) Pordenone. Adherence was calculated for each patient considering the total reporting period and according to whole, peak, and high days of
pollen periods defined by the European Academy of Allergy and Clinical Immunology (EAACI) and local criteria. See Figure 1 and Table 2 for criteria.
“a” indicates that nonparametric Friedman test for repeated measures was applied and only statistically significant P values of multiple comparisons,
adjusted by Bonferroni correction, are highlighted.

Discussion

Principal Findings
In this bicenter study, we investigated the adherence of Italian
patients with SAR to symptom and medication monitoring via
an e-diary prescribed by their doctor in the context of an
observational study. We found that adherence to recording was
(1) very high (>80%) in the first 7 weeks of monitoring, (2)
predicted by the adherence in the first 3 weeks of the monitoring
period, (3) inversely associated with the frequency of delayed
e-diary compilation, and (4) higher during the peak pollen
season.

The trajectories of the mean adherence to recording were highly
similar in both study populations, notwithstanding their
differences in geographical location (northern vs central Italy)
and age (adults vs children). Moreover, we observed only a few
patients with very low adherence to e-diary recording, that is,
failing to register their symptoms during more than 60% of days
within their monitoring period (not shown).

This level of adherence is at great variance from levels published
in previous studies on e-diaries in patients with allergic rhinitis
who had not been specifically instructed and advised by a doctor
to use an app. With this approach, the Mobile Airways Sentinel
Network observational pilot study among 2871 allergic users
from 15 countries reported an adherence to symptom recording
of only 9.5% after 14 days of recording [14]. A follow-up project
among 9122 users from 22 countries showed that only 16.4%
of the users were still recording their symptoms after 14 days
[15].

Digital technologies have been shown to be a very useful tool
for the assessment of real-life data among big patient groups
[14-17]. While the patient-initiated use of an e-diary app may

be very helpful in highly motivated patients looking for
self-management opportunities, it seems that this scenario
applies to only a minority of the users spontaneously
downloading, installing, and using an e-diary app for allergic
rhinitis [14,15]. However, our results showed that in a blended
care approach combining face-to-face visits with internet-based
support technologies, patients are keen and able to correctly use
an e-diary when contacted and instructed to do so by their
allergist. It has to be underlined, though, that our patients were
participating in an observational clinical study and we do not
know whether their high adherence would have been also
maintained in the context of routine clinical practice. This
hypothesis deserves to be tested in a real-life or surveillance
study.

The adherence to e-diary recording of the patients in Rome was
slightly, but significantly, higher during the grass pollen peak
season, when allergic symptoms were also more severe. This
observation may be easily explained by increased awareness
and motivation linked to symptom severity. This outcome should
be taken into account when considering the use of e-diaries
outside the pollen season or in patients with very mild
symptoms. With regard to monitoring scores, we demonstrated
that the overall VAS score reliably reflected the results of the
RTSS and CSMS, which confirms the usefulness of VASs for
digital symptom assessment as previously shown in other studies
[31,32].

Of great relevance is, in our opinion, that the overall adherence
of a patient to e-diary recording over a period of 2 or more
months can already be predicted with enough confidence in the
second and third weeks of monitoring. Patients at risk of poor
adherence could therefore be identified and receive
supplementary information and education, thus facilitating a
higher compliance.

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Limitations
First, our study population consisted of Italian patients only, so
that our results now require further evaluation in different
cultural contexts. To this end, we are examining the outcomes
of a similar study performed in 7 southern European and
Mediterranean countries. Second, we cannot comment on
possible outcome improvements, as the study did not include
any control group. Third, we limited our monitoring period to
a maximum of 90 days; we do not know whether the patients’
adherence to recording would have remained high enough
beyond this time frame. Fourth, our results and proposal cannot
be applied to SAR patients not using a mobile phone. Fifth, we
did not evaluate potential adverse effects of the use of an e-diary,

such as excessive attention to disease or even facilitation of
anxiety and obsessive disturbances.

Conclusion
Our study showed that adherence to the daily symptom and
medication monitoring via an e-diary was maintained at a high
level up to 2 months by SAR patients properly informed and
educated by their allergist. This outcome underlines the strength
of a blended care approach and needs now to be confirmed in
a real-life clinical allergy setting. Our results contribute to
reinforce positive expectations for a proper use of mHealth
technology in monitoring patients with SAR for diagnostic and
therapeutic purposes.

Acknowledgments
We thank Ms Theresa Lipp for English language revision.

SA was supported by the EAACI Fellowship Award of the European Academy of Allergy and Clinical Immunology. The study
was supported by an unrestricted grant from EUROIMMUN (no. 118583). The Informatics Platform AllergyCARD and the app
Allergy.Monitor were kindly provided by TPS Production, Rome, Italy. We acknowledge support from the German Research
Foundation and the Open Access Publication Funds of Charité – Universitätsmedizin Berlin.

Conflicts of Interest
PMM reports grants and personal fees from EUROIMMUN AG, during the conduct of the study; and grants and personal fees
from Thermo Fisher Scientific, and personal fees from Hycor Biomedical Inc, outside the submitted work. ST is cofounder of
TPS Production. S Pelosi reports personal fees from TPS Production. FB reports personal fees from Abbvie. The remaining
authors declare no conflict of interest.

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Abbreviations
ARIA: Allergic Rhinitis and its Impact on Asthma
CSMS: combined symptom and medication score
EAACI: European Academy of Allergy and Clinical Immunology
RTSS: Rhinoconjunctivitis Total Symptoms Score
SAR: seasonal allergic rhinoconjunctivitis
VAS: visual analog scale

Edited by G Eysenbach; submitted 10.10.19; peer-reviewed by D Ryan, J Ivancevich; comments to author 30.10.19; revised version
received 02.12.19; accepted 16.12.19; published 16.03.20

Please cite as:
Di Fraia M, Tripodi S, Arasi S, Dramburg S, Castelli S, Villalta D, Buzzulini F, Sfika I, Villella V, Potapova E, Perna S, Brighetti
MA, Travaglini A, Verardo P, Pelosi S, Zicari AM, Matricardi PM
Adherence to Prescribed E-Diary Recording by Patients With Seasonal Allergic Rhinitis: Observational Study
J Med Internet Res 2020;22(3):e16642
URL: https://www.jmir.org/2020/3/e16642
doi: 10.2196/16642
PMID: 32175909

©Marco Di Fraia, Salvatore Tripodi, Stefania Arasi, Stephanie Dramburg, Sveva Castelli, Danilo Villalta, Francesca Buzzulini,
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Full Text | Scholarly Journal

Identifying Risk of Future Asthma Attacks Using UK Medical Record Data: A Respiratory Effectiveness Group Initiative

Blakey, John D; Price, David B; Pizzichini, Emilio; Popov, Todor A; Dimitrov, Borislav D; et al.
Journal of Allergy and Clinical Immunology. In Practice; Amsterdam Vol. 5, Iss. 4, (Jul 1, 2017): 1015-1024. DOI:10.1016/j.jaip.2016.11.007

https://resolver.ebscohost.com/openurl?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-
8&rfr_id=info:sid/ProQ%3Ahealthcompleteshell&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.jtitle=Journal+of+Allergy+and+Clinical+Immunology.+In+Practice&
07-
01&rft.volume=5&rft.issue=4&rft.spage=1015&rft.isbn=&rft.btitle=&rft.title=Journal+of+Allergy+and+Clinical+Immunology.+In+Practice&rft.issn=22132198&rft_id=info:doi/10.1

Abstract

Background

Asthma attacks are common, serious, and costly. Individual factors associated with attacks, such as poor symptom control, are not robust predictors.

Objective

We investigated whether the rich data available in UK electronic medical records could identify patients at risk of recurrent attacks.

Methods

We analyzed anonymized, longitudinal medical records of 118,981 patients with actively treated asthma (ages 12-80 years) and 3 or more years of data. Potential risk factors
during 1 baseline year were evaluated using univariable (simple) logistic regression for outcomes of 2 or more and 4 or more attacks during the following 2-year period.
Predictors with significant univariable association (P< .05) were entered into multiple logistic regression analysis with backward stepwise selection of the model including all significant independent predictors. The predictive accuracy of the multivariable models was assessed.

Results

Independent predictors associated with future attacks included baseline-year markers of attacks (acute oral corticosteroid courses, emergency visits), more frequent reliever
use and health care utilization, worse lung function, current smoking, blood eosinophilia, rhinitis, nasal polyps, eczema, gastroesophageal reflux disease, obesity, older age, and
being female. The number of oral corticosteroid courses had the strongest association. The final cross-validated models incorporated 19 and 16 risk factors for 2 or more and 4
or more attacks over 2 years, respectively, with areas under the curve of 0.785 (95% CI, 0.780-0.789) and 0.867 (95% CI, 0.860-0.873), respectively.

Conclusions

Routinely collected data could be used proactively via automated searches to identify individuals at risk of recurrent asthma attacks. Further research is needed to assess the
impact of such knowledge on clinical prognosis.

Full Text

What is already known about this topic? Asthma attacks are common, serious, and costly. Individual factors associated with attacks, such as poor symptom control, are not
robust predictors. Adequately powered studies are required to progress toward a multivariable predictor.

What does this article add to our knowledge? This large study shows that a combination of risk factors from routine medical record data can identify individuals at high risk
of subsequent recurrent asthma attacks.

How does this study impact current management guidelines? Routine data from electronic medical records could be used to assess individuals’ risks of recurrent asthma
attacks, and to guide targeted management of modifiable risk factors.

Asthma is a common and heterogeneous disease with a wide variety of presentations and clinical courses.1 However, in all subtypes there is the potential for abrupt clinical and

lung function deteriorations termed asthma attacks (or severe exacerbations).2 A common cause of unscheduled health care utilization,3 asthma attacks are associated with

substantial physical4 and psychological morbidity,5 and major direct and indirect health care costs.6

Asthma management strategies and action plans have focused largely on symptom control, with less attention to risk stratification schemes and prevention. This focus on
symptom management may have contributed to the incidence of asthma attacks and deaths remaining relatively constant, whereas there have been substantial improvements

in other disease areas (eg, cardiovascular disease) for which risk-centered strategies using objective measures have been developed.3,7

Although poor control of asthma symptoms is associated with risk of future attacks, it is not a robust predictor in isolation.8,9 Moreover, there may be a pronounced discordance

between daily symptoms and the risk of attack in a substantial proportion of individuals.1,10 Asthma treatments may be selected by some clinicians for their effect on symptoms

but not on future risk of exacerbations (eg, theophylline), whereas other treatments may be chosen for the opposite profile (eg, mepolizumab).11 Assessing risk could therefore

reduce the potential for inappropriate undertreatment or overtreatment, as well as have the positive effect of facilitating shared decision making.12

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Available guidelines do discuss future risk,13,14 and there are a large number of publications that report single or grouped risk factors for asthma attacks.13-15 A simple risk

questionnaire based on such published risk factors16 has generated substantial public interest. This risk assessment tool has been intended primarily as a conduit to health
promotion opportunities but also highlights a range of risk factors-from smoking status and extent of reliever use to hospitalization history-that need to be evaluated in a single
study alongside biomarkers. The relative effect size of these risk factors and their interaction is currently not well characterized, but establishing these elements is an essential
step toward the production of a validated risk assessment tool for use in routine practice.

One study suggested that the implementation of practice-based asthma risk registries is feasible in routine clinical care, but a validated risk assessment tool was not used.17

More recently, a risk score for asthma attacks has been developed from a large clinical trial data set.18 However, enrolled patients were preselected to have uncontrolled asthma
symptoms and at least 1 attack the previous year; thus, the external validity of the risk score is uncertain when applied to the wider population of patients treated for asthma in

routine clinical practice, both because most of these patients would not meet typical trial eligibility criteria19 and because the ecology of care in clinical trials is difficult to
replicate in general practice.

All individuals in the United Kingdom have their electronic medical records centralized at their primary care practice, where information from secondary care and hospitalizations
is also aggregated. Our objective was to identify routinely collected characteristics from electronic medical records to develop a multivariable prediction model for multiple
asthma attacks over a 2-year outcome period. We hypothesized that the rich data available in longitudinal medical records of UK patients (including previously identified risk
factors) could reliably identify patients who subsequently experienced recurrent attacks. We aimed to produce estimates of effect size for risk factors when considered in
combination.

Methods

Data source and study population

The Optimum Patient Care Research Database (OPCRD) is a quality-controlled, respiratory-focused database containing anonymous data from general practices throughout the

United Kingdom and approved for clinical research by the Health Research Authority of the UK National Health Service (REC reference no. 15/EM/0150).20 At the time of the
study, the OPCRD contained longitudinal medical record data of more than 1.7 million patients from more than 400 UK general practices. The anonymized point-of-care records
for each patient include demographic information, disease diagnoses as Read codes, prescriptions issued during consultations or as renewals, test results, and information
transcribed from secondary care visits and hospitalizations.

This study was an initiative of the Respiratory Effectiveness Group, an investigator-led, not-for-profit, real-life respiratory research and advocacy initiative.21 The study was
conducted in line with recommendations for observational research, including an a priori research plan, study registration, commitment to publish, and an independent steering
committee not remunerated for participation (please see this article’s Online Repository at www.jaci-inpractice.org). Written informed consent was not necessary because data
were anonymous; however, patients had been given the option to prohibit use of their anonymized data for research use.

Twelve- to 80-year-old patients with an asthma diagnostic Read code recorded before study start, active asthma, and at least 3 years of continuous data were included in the
study population. Active asthma was defined as 2 or more prescriptions for asthma drugs during study year 1 (short-acting β2 agonist, inhaled corticosteroids [ICSs], long-
acting β2 agonist [LABA], fixed-dose ICS/LABA combination, leukotriene receptor antagonist, and/or theophylline), as well as no Read code for resolved asthma during the 3-
year study period. Those with a concurrent diagnosis of chronic obstructive pulmonary disease (chronic obstructive pulmonary disease Read code) recorded at any time in the
database (ever-recorded) were excluded from the analyses.

Study design

This was a historical, follow-up cohort study of patients with asthma, using longitudinal OPCRD data from February 2005 through September 2014. The study period thus began

after the 2004 institution of the UK Quality and Outcomes Framework,22 an initiative that provides financial incentives for annual review of patients with asthma in primary care
and promotes regular coding of symptoms, peak flow, and smoking status.

We examined the most recent 3 years of continuous data for each patient, including 1 year of data for baseline characterization and 2 years of outcome data. Anonymized
individual patient data, including patients’ demographic characteristics, comorbidities, attack history, and current therapy were extracted from routine electronic clinical patient
records in primary care practice management systems.

Candidate predictors were selected on the basis of literature review and expert opinion (Table I).23,24

Model building

The primary end point was the occurrence of an asthma attack (severe exacerbation), as defined by the European Respiratory Society/American Thoracic Society,25 namely, an
asthma-related hospitalization, emergency department attendance, or an acute respiratory presentation resulting in a course of oral corticosteroids (OCSs). Multiple events
occurring within a 2-week window were considered as a single attack.

Univariable logistic regression analysis was used to identify individual characteristics that were predictive of 2 different binary outcomes: (1) 2 or more (yes/no) asthma attacks
during the 2-year outcome period and (2) 4 or more (yes/no) asthma attacks during the 2-year outcome period. Collinear associations between potentially related predictors
were assessed using Spearman rank-order correlation coefficients. The values of variables were rank-ordered for calculating these correlation coefficients, and relationships with
rank correlation coefficients greater than 0.30 were defined as being collinear.

All predictors with a significant univariable association (P < .05) were entered into a multiple logistic regression analysis with backward selection of the model, performed manually on the basis of significant P values. For the variables that were found to be collinear, we repeated the multiple regression analyses, substituting the second variable of the pair for the first (eg, number of acute OCS courses for number of asthma attacks) and selected the variable leading to the lowest Akaike information criterion of the model.

Because not all patients had recorded values for all predictors, we categorized predictors and included a separate category to indicate absence of available data for the following
variables: body mass index, smoking status, percent predicted peak expiratory flow, and blood eosinophil count.

Model performance and internal validation

The ability of the model to distinguish patients with multiple asthma attacks from other patients with asthma was assessed by its discrimination performance calculating the C
statistic (area under the receiver operating characteristic curve). The C statistic CIs were generated by bootstrapping with 1000 resamples. Other performance measures,
including sensitivity, specificity, and positive and negative predictive values, were plotted for different cutoff points of the estimated risk of multiple asthma attacks as calculated
by the models in plots generated using R package ROCR version 1.0-5.

Potential optimism in estimated discrimination performance and overfitting of the models was evaluated using bootstrapping with 100 resamples and by cross-validation with a
random split of the data as 70% for model development (sample set) and 30% for performance testing (test set).

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Calibration analysis was performed and results were presented by plots showing the correlation of the mean observed risk with mean predicted risk among 500 groups
encompassing all patients in the study (n = 118,981).

Results

Of 338,482 patients in the OPCRD with an asthma diagnosis and 3 consecutive years of data, 132,717 (39%) patients aged 12 to 80 years had active asthma (see Figure E1 in
this article’s Online Repository at www.jaci-inpractice.org). We excluded patients with an ever-recorded chronic obstructive pulmonary disease diagnosis (n = 13,736; 10%),
leaving 118,981 patients in the total study population.

Key patient characteristics are summarized in Table II. The mean age at start of the study was 45 ± 18 years, 67,534 (57%) patients were women, 35,544 (30%) were obese,
and 19,022 (16%) were current smokers. Most patients (n = 104,345; 88%) were prescribed ICS, either as monotherapy (n = 61,358; 52%) or in combination with a LABA (n
= 42,987; 36%); 40% (n = 47,652) were prescribed high-dose ICS at their last prescription (>=400 μg/d fluticasone-equivalent). Seventeen percent of patients (n = 20,711)

had at least 1 OCS course prescribed in the baseline year. (Table E1 in this article’s Online Repository at www.jaci-inpractice.org depicts distributions of all other candidate
predictors at baseline.)

During the subsequent 2-year outcome period, one-quarter of patients (n = 30,234; 25%) experienced 1 or more, 12,736 (11%) experienced 2 or more, and 3,198 (3%)

experienced 4 or more asthma attacks (Table III).

Model building

All candidate predictors recorded in the baseline period, with the exception of beta-blocker prescriptions, were significantly associated with the risk of frequent asthma attacks

(2 or more or 4 or more) during the outcome period (see Table E2 in this article’s Online Repository at www.jaci-inpractice.org). Descriptions of collinear associations among risk
factors are given in this article’s Online Repository at www.jaci-inpractice.org.

The final multivariable (multifactor) models contained 19 independent predictors for 2 or more attacks (Table IV) and 16 predictors for 4 or more attacks (Table V), of which the
number of acute OCS courses in the baseline year had the strongest association.

Older age, female sex, current smoking, and obesity were significant risk predictors for both outcomes, as were blood eosinophilia, higher mean daily short-acting β2 agonist
dose, and leukotriene receptor antagonist or LABA prescriptions in the baseline year. Comorbidities significantly contributing to risk prediction of both outcomes were active
rhinitis and a history of nasal polyps or anaphylaxis. The odds of frequent attacks were increased for patients with more frequent primary care consultations and for those with

baseline-year markers of asthma attacks, such as acute OCS courses or emergency department attendance (Tables IV and V). The odds of 2 or more or 4 or more attacks were
significantly lower for patients with lower medication possession ratio.

Model performance and internal validation

The overall C statistic was 0.785 (95% CI, 0.780-0.789) for the ability of the model to distinguish patients who experienced 2 or more asthma attacks in the 2-year outcome

period (see Figure E2 in this article’s Online Repository at www.jaci-inpractice.org). The model performed better in predicting 4 or more attacks with a C statistic of 0.867 (0.860-

0.873) (see Figure E3 in this article’s Online Repository at www.jaci-inpractice.org). We found no indication of relevant optimism in estimated model performance or overfitting of
the model in this large data set (data not shown).

Calibration plots showed good correlation between the probabilities of having multiple asthma attacks in the outcome period as estimated by the models and the observed

outcome frequencies, although higher predicted risks, observed in relatively small proportions of the population, were slightly overestimated (Figure 1).

As forecasted by the multivariable model, 3% (n = 3497) of the population had a 50% or more predicted risk of experiencing 2 or more asthma attacks in the next 2 years; and
58% (n = 2019) of these individuals actually experienced 2 or more attacks in the outcome period (positive predictive value at the cutoff point). The negative predictive value
was 91% at that cutoff point.

Only 246 (0.2%) patients had a 50% or more predicted risk of experiencing 4 or more asthma attacks and 54% (n = 133) experienced 4 or more attacks in the outcome period.
Only 3% (n = 3065) of the patients with a lower predicted risk experienced 4 or more attacks (negative predictive value 97%).

Table VI illustrates the predicted risk calculation for 4 hypothetical patients with asthma.

Discussion

A combination of risk factors from longitudinal medical records of UK patients was effective in predicting which individuals subsequently experienced recurrent attacks, and in
particular in predicting the high-risk patients who experienced 4 or more attacks over a 2-year period. This large database study has confirmed that asthma attacks are common
in an unselected UK population, with 25% of patients experiencing 1 or more attacks during the 2-year outcome period. The risk factors we identified are largely consistent with
previous findings.

This study has strengths in its large sample size and the range of factors considered concurrently (for post hoc power calculations, see this article’s Online Repository at
www.jaci-inpractice.org Online Repository). Asthma is a common and important disease with a variety of presentations and underlying mechanisms; therefore, multiple factors
should be included in any risk prediction model. Previous studies have evaluated individual risk factors or limited numbers of risk factors to predict asthma attacks, for example,

those representing subacute lack of asthma control.26 Questionnaire-based methods of predicting risk have been studied as well.27 Instead, the risk factors we identified are all
collected from routine electronic patient data, suggesting that an informatics-based approach to risk stratification is possible, with lists of high-risk patients being automatically
generated for the attention of the clinical team, for example, by alerts placed on the clinical records. Moreover, the present study also formally describes the potential predictive
ability of the risk model developed and lends itself to the development of an individualized Web-based assessment tool as used in other disease areas, such as for cardiovascular

risk assessment.28

The risk factors included in our model have been identified in previous studies including the recent UK National Review of Asthma Deaths29; these include previous asthma

attacks, asthma severity as described by level of treatment, current symptom control, nasal disease, and generally hazardous comorbidities (smoking, obesity).13,30 Obesity
may predispose to asthma attacks through the effect of extrathoracic restriction from adipose tissue and from the effect of adipokines on overall immune function and airway

inflammation.31 In addition, there may be a common genetic predisposition to both asthma and obesity.32,33

For those individuals with available blood cell counts, blood eosinophil counts (>0.4 x 109/L) were also associated with frequent asthma attacks. This finding is consistent with a

recent large database study investigating the dose-response relationship between blood eosinophils and exacerbation risk.34 Furthermore, this work expands on and

complements a study published earlier this year.35 Although of a similar design, that study investigated a narrower range of risk factors over a shorter follow-up period (1 year)
for the subpopulation of patients who had a blood eosinophil count; the findings therefore may not be representative of the wider population of individuals with asthma.

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In this general population of people treated for asthma, 51% filled less than 60% of their prescription refills during the baseline year, and the odds of multiple attacks were
lower among those with lower medication possession ratios than among patients with medication possession ratios of 80% to 100%. We can speculate that perhaps individuals

with milder asthma took their treatment less regularly (eg, over a pollen season) and this was an effective strategy for them.36 In their systematic review of medication

adherence and risk of asthma attacks, Engelkes et al37 reported that some studies found an association between low adherence (expressed as medication possession ratio) and
low risk of attack, perhaps because of self-titration according to level of control or of heterogeneity in treatment response. Others have reported variations in adherence over

time.38 Up to a third of people treated for asthma do not have objective supportive evidence of asthma when tested for airway dysfunction and inflammation.39 Therefore, it
may be that some individuals in this study were not regularly collecting medication because they did not have active asthma symptoms, and they were also at very low risk for
asthma attacks. Conversely, individuals who have experienced a recent attack and have less stable asthma may be concordant with inhaled therapy but still remain at a higher
risk of attack.

Given the population we studied and the method of data collection, these real-life findings are directly applicable to patients treated for asthma in the United Kingdom. This is in
contrast to the limited inclusion criteria of most randomized controlled trials, which often exclude up to 95% of typical patients seen in general practice, such as smokers and

those with comorbidities.19 The generalizable nature of these findings has the potential to inform future changes in practice and thus have an early clinical impact.

As with any observational study, these findings do not provide mechanistic insight into how the identified factors increase future risk. Moreover, several other potential risk
factors would have been of interest to consider, including allergen exposure, inhaler technique assessment, and socioeconomic status, but these were not readily available from
the database. Although the study population is dispersed across the country, it is unclear whether the findings would be applicable outside the UK National Health Service
framework and its largely white population in terms of relative magnitude of effects. In addition, this type of data carry the potential for underrecording of secondary care
attendances: asthma attacks that require emergency department attendance are not invariably recorded in primary care notes because recording requires a manual step. This
potential for missing outcomes could result in underestimating the attack rate or biasing the predictors toward those associated with more moderate exacerbations that do not
require hospitalization.

Our study period (February 2005 to September 2014) began after the 2004 institution of the UK Quality and Outcomes Framework, which has improved data recording in

electronic patient records through financial incentives.22,40,41 Within that period, we analyzed the most recent 3-year interval of data for eligible patients to include their most
current available data. The prescription data used in this study were drawn from the electronic record of prescriptions issued at the time of a consultation (eg, for acute illness
or change in regular medication) or as renewals that continued existing chronic prescriptions. Although there is currently no UK-wide system that links prescribing and
dispensing data for primary care, several sources cite the reliability of prescribing data in another similar UK primary care database, the General Practice Research Database
(now the Clinical Practice Research Datalink), noting that there is good agreement between General Practice Research Database prescribing data and national dispensing

data.42,43 Moreover, in the United Kingdom, pharmacists must dispense medications as prescribed.

We are developing a simple risk scoring tool as an example of the type of individualized information that could be available to people with asthma and their health care providers
in the near future, or that could be automatically applied to routine electronic medical records where computer-based clinical record-keeping is used. During the development of

the model, the extent of missing data varied from 6% for smoking status to 34% for blood eosinophil count, as recorded in Table II. For those variables with missing data, we
were able to include a “missing data” category in the risk model, thereby enabling clinicians to use the risk calculator even when some data are missing, a common situation in
real life.

This study provides clinically relevant measures of the relative importance of risk factors for recurrent asthma attacks. Additional work will be required to validate the model in
other data sets, and prospectively for patients in different settings, and to develop these findings into questions or data queries to create a reliable tool for clinical practice.
Further analyses will be required to explore potential time-to-event measures and also to ascertain which are the most important predictors in the models. Prospective trials will
be required to assess the implementation of such models in clinical practice and the effect on asthma-related outcomes of risk-based decision making, at both individual and
group levels.

Acknowledgments

We thank Ian D. Pavord, Hilary Pinnock, Gene Colice, Alexandra Dima, Janet Holbrook, Cindy Rand, Iain Small, and Sam Walker for their valuable contributions to discussions
about the study design. We thank Anne Burden, Vasilis Nikolaou, Victoria Thomas, and Maria Batsiou for contributions to the data elaboration and statistical analyses.

Appendix

Methods

The study was conducted in line with recommendations for observational research, including an a priori research plan, study registration, commitment to publish, and an

independent steering committee not remunerated for participation.E1,E2 The study protocol was approved by the Anonymised Data Ethics Protocols and Transparency
Committee, the independent scientific advisory committee for the OPCRD, and was registered with the European Network of Centres for Pharmacoepidemiology and

Pharmacovigilance (http://www.encepp.eu/encepp/viewResource.htm?id=6303).E3

The Charlson comorbidity index scoreE4 in the baseline year was categorized as 0, 1 to 4, 5 to 9, and 10 or more, with comorbidity weights taken from the Hospital

Standardised Mortality Ratios.E5

Post hoc power calculations showed that the large study population of 118,981 patients provided sufficient statistical power (>=80%; α = 0.05) to detect an association with an
odds ratio of 1.10 for the risk of 2 or more asthma attacks, assuming a risk of 11% in patients without the characteristic of the predictor and a prevalence of the characteristic
of at least 8%. For the risk of 4 or more asthma attacks, the study population size would allow detecting an odds ratio of 1.17, assuming a risk of 3.0% in patients without the
characteristic for predictors with a prevalence of at least 9%.

Results

Additional patient demographic and clinical characteristics are presented in Table E1.E6

Univariable analyses

All the potential baseline risk factors tested in univariable analyses with the exception of beta-blocker prescriptions (yes/no) were significantly associated with the presence of

asthma attacks (>=2 or >=4 attacks) in the follow-up period (study years 2 and 3; Table E2).E6-E9

Multivariable analyses

Age was collinear with gastroesophageal reflux disease (GERD) diagnosis (active/ever) and/or GERD drugs, cardiovascular disease diagnosis, and prescriptions for statins.

Acute OCS courses were collinear with acute OCS courses with evidence of lower respiratory consultation, antibiotic courses (with evidence of lower respiratory consultation),
acute respiratory events, and severe exacerbations (baseline year).

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ICS adherence was collinear with number of ICS prescriptions and inhalers, ICS average daily dose, ICS prescribed and actual duration, and number of SABA prescriptions and
inhalers.

ICS prescribed dose was collinear with ICS device type.

Rhinitis diagnosis (active) was collinear with rhinitis diagnosis (ever), rhinitis diagnosis (active)/drugs, and rhinitis diagnosis (ever)/drugs.

Eczema diagnosis (active) was collinear with eczema (ever).

GERD diagnosis (active) was collinear with GERD diagnosis (ever).

Primary care consultations were collinear with diabetes diagnosis, asthma consultations, Charlson comorbidity index score, paracetamol prescriptions, antibiotic courses, and
asthma control status.

Nonsteroidal anti-inflammatory drug prescriptions were collinear with paracetamol prescriptions.

During the stepwise backward logistic regression, heart failure and anxiety/depression were dropped from the final model for 2 or more attacks; and diagnoses of GERD
(active), heart failure, eczema (active), and anxiety/depression, as well as prescriptions for nonsteroidal anti-inflammatory drugs were dropped from the final model for 4 or
more attacks.

Table ICandidate predictors assessed for inclusion in the modelsBAI, Breath-actuated inhaler; BMI, body mass index; BTS, British Thoracic Society; DPI, dry powder inhaler; ED,
emergency department; FP, fluticasone propionate; GERD, gastroesophageal reflux disease; LRTI, lower respiratory tract infection; LTRA, leukotriene receptor antagonist; MDI,
metered-dose inhaler; NSAIDs, nonsteroidal anti-inflammatory drugs; PEF, peak expiratory flow; SABA, short-acting β2 agonist; Theo, theophylline.

Variable Description

Sex Male or female

Age In years at the start of the 3-y study period

BMI Last recorded, in kg/m2; categorized as underweight (<18.5), normal (18.5-24.9), overweight (25-29.9), or obese (>=30)

Smoking status Last recorded, categorized as never smoker, current smoker, or ex-smoker

Charlson comorbidity index
Score in the baseline year, categorized as 0, 1-4, 5-9, >=10 (comorbidity weights taken from Hospital Standardised Mortality Ratios, version
9)22,23

Comorbidities∗
Recorded ever or active: eczema, allergic and nonallergic rhinitis, nasal polyps, anaphylaxis diagnosis, anxiety/depression diagnosis, diabetes
(type 1 or 2), GERD, cardiovascular disease, ischemic heart disease, heart failure, psoriasis

Comedications In baseline year, prescription (yes/no) for paracetamol, NSAIDs, beta-blockers, statins

% predicted PEF Recorded ever, expressed as percentage of predicted normal, categorized as unknown, <60%, 61%-79%, and >=80%

Blood eosinophil count Last recorded, in 109cell/L, categorized as <=0.4 or >0.4

BTS step†

Step 1 Inhaled SABA as needed

Step 2 ICS or LTRA

Step 3 Add LABA to ICS or use high-dose ICS (>=400 μg/d FP equivalent)

Step 4 Add LTRA/Theo to [ICS + LABA] or add LABA/LTRA/Theo to high-dose ICS

Step 5 Add OCS

Average daily dose of
SABA/ICS

Cumulative dose of SABA/ICS prescribed in baseline year, expressed in μg/d albuterol or FP equivalent and divided by 365.25

Prescribed daily ICS dose Dose of ICS prescribed at last prescription of baseline year in μg/d, FP equivalents

ICS medication possession
ratio

ICS refill rate during the baseline year: sum of number of days per pack (number of actuations per pack/number of actuations per day)/365.25

ICS device type In baseline year: categorized as no ICS, MDI, BAI, or DPI

Spacer use with ICS pMDI Recorded in baseline year (yes/no)

Oral corticosteroid use Any maintenance prescription for corticosteroids in baseline year (yes/no)

Prior asthma education Recorded ever (yes/no)

Primary care consults Number of primary care consultations, categorized as 0, 1-5, 6-12, >=13

Primary care consults for
asthma

Number of primary care consultations with an asthma-related Read code

Antibiotics with lower
respiratory consult

Number of consultations that resulted in antibiotic prescription (included to capture asthma events that may have been misclassified as LRTI)

Acute respiratory events

Number of events in the baseline year, defined as asthma-related hospitalization or ED attendance or an acute course of OCS or antibiotics
prescription with lower respiratory consultation

Acute OCS courses Number of acute courses of OCS in baseline year, categorized as 0, 1, >=2

Acute OCS courses with lower
respiratory consult

Number of OCS courses with Read code for lower respiratory consultation in baseline year, categorized as 0, 1, >=2

Antibiotics courses Number of antibiotics prescriptions with Read code for lower respiratory consultation in baseline year, categorized as 0, 1, >=2

Hospital
attendance/admission

Number of asthma-related‡ ED, inpatient, and outpatient attendance/admission in baseline year

Asthma attacks Number of asthma-related‡ hospital ED attendance, inpatient admission, or acute OCS course

Table IIPatients’ demographic and clinical characteristics during the baseline yearED, Emergency department; GERD, gastroesophageal reflux disease; LTRA, leukotriene receptor
antagonist; NSAIDs, nonsteroidal anti-inflammatory drugs; PEF, peak expiratory flow; SABA, short-acting β2 agonist.Data are n (%) unless otherwise noted.

Variable All patients (n = 118,981)

Male sex∗ 51,447 (43)

Age at study start (y), mean ± SD∗ 45 ± 18

12-18 13,452 (11)

19-34 21,381 (18)

35-54 44,375 (37)

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55-80 39,773 (33)

Body mass index∗

Underweight 3,480 (3)

Normal 35,400 (30)

Overweight 36,608 (31)

Obese 35,544 (30)

Unknown 7,949 (7)

Smoking status∗

Current smokers 19,022 (16)

Ex-smokers 26,758 (22)

Nonsmokers 65,489 (55)

Unknown smoking status 7,712 (6)

Recorded comorbidity†

Rhinitis diagnosis, active∗ 3,567 (3)

Rhinitis diagnosis/therapy, active 36,312 (31)

Nasal polyps, ever∗ 3,933 (3)

Eczema diagnosis, active∗ 4321 (4)

Anaphylaxis diagnosis, ever∗ 512 (0.4)

GERD diagnosis, active∗ 1,444 (1)

Anxiety or depression diagnosis, ever 5,812 (5)

>=1 prescription during baseline

NSAIDs∗ 27,862 (23)

%predicted PEF, median (IQR)∗ 80 (68-91)

<=60% 13,808 (12)

61%-79% 33,850 (28)

>=80% 47,780 (40)

Unknown 23,543 (20)

Blood eosinophil count∗

<=0.4 x 109/L 64,803 (55)

>0.4 x 109/L 13,184 (11)

Missing 40,994 (34)

Mean daily SABA dose (μg/d)∗‡

0 11,992 (10)

1-200 50,467 (42)

201-400 29,866 (26)

>400 26,656 (22)

Last ICS dose prescribed in baseline year (μg/d)‡

0 14,636 (12)

<400 56,693 (48)

>=400 47,652 (40)

ICS medication possession ratio∗§

>0%-39.9% 37,723 (32)

40%-59.9% 23,374 (20)

60%-79.9% 9,385 (8)

80%-100% 15,493 (13)

>100% 18,370 (15)

No ICS prescribed 14,636 (12)

>=1 prescription during baseline

LTRA∗ 6,995 (6)

LABA (standalone)∗ 8,253 (7)

Acute OCS courses∗

0 98,270 (83)

1 14,554 (12)

>=2 6,157 (5)

Primary care consultation∗

0 5,618 (5)

1-5 56,023 (47)

6-12 40,074 (34)

>=13 17,266 (14)

>=1 Asthma-related ED admission∗ 696 (0.6)

Asthma attacks¶

0 97,583 (82)

1 15,058 (13)

2 4,202 (4)

>=3 2,138 (2)

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Table IIINumber of asthma attacks (severe exacerbations) in the baseline and outcome years for 118,981 patients with asthmaThe category “Years 2 & 3 combined” includes
those patients who had a single exacerbation in year 2 and/or in year 3.

Asthma attacks Year 1 Year 2 Year 3 Years 2 & 3 combined

>=1, n (%) 21,398 (18.0) 20,132 (16.9) 17,984 (15.1) 30,234 (25.4)

>=2, n (%) 6,340 (5.3) 6,169 (5.2) 5,517 (4.6) 12,736 (10.7)

>=4, n (%) 770 (0.6) 732 (0.6) 681 (0.6) 3,198 (2.7)

Table IVIndependent baseline predictors (year 1) of 2 or more asthma attacks during the 2-y follow-up period as identified in the final multivariable modelED, Emergency
department; GERD, gastroesophageal reflux disease; LTRA, leukotriene receptor antagonist; MPR, medication possession ratio; NSAID, nonsteroidal anti-inflammatory drug; OR,
odds ratio; PEF, peak expiratory flow; ref, reference category; SABA, short-acting β2 agonist.Collinearity of variables is described in this article’s Online Repository at www.jaci-
inpractice.org.

Year 1 predictors Adjusted OR (95% CI) P value∗

Age (y)

12-18 (ref) 1.00 <.001

19-34 1.27 (1.14-1.40)

35-54 1.43 (1.29-1.57)

55-80 1.47 (1.33-1.62)

Sex, female 1.35 (1.29-1.41) <.001

Body mass index, normal (ref) 1.00 <.001

Underweight 1.10 (0.95-1.27)

Overweight 1.16 (1.09-1.22)

Obese 1.27 (1.21-1.34)

Unknown 0.96 (0.86-1.08)

Smoking status, nonsmoker (ref) 1.00 <.001

Current smoker 1.17 (1.11-1.24)

Ex-smoker 1.01 (0.96-1.06)

Unknown 1.02 (0.93-1.11)

Rhinitis diagnosis, active† 1.14 (1.03-1.27) .015

Eczema diagnosis, active 1.13 (1.02-1.25) .017

GERD diagnosis, active 1.29 (1.11-1.50) .017

Nasal polyps, ever 1.60 (1.46-1.76) <.001

Anaphylaxis diagnosis, ever 1.66 (1.29-2.13) <.001

NSAID prescription, >=1 1.13 (1.08-1.18) <.001

PEF % predicted, >=80% (ref) 1.00 <.001

<=60% 1.62 (1.52-1.27)

61%-79% 1.21 (1.15-1.27)

Unknown 1.25 (1.17-1.33)

Blood eosinophil count, <=0.4 x 109/L (ref) 1.00 <.001

>0.4 x109/L 1.21 (1.14-1.29)

Missing 0.88 (0.83-0.93)

Mean SABA dose (μg/d),‡ 0 (ref) 1.00 <.001

1-200 1.05 (0.97-1.14)

201-400 1.28 (1.16-1.39)

>400 1.63 (1.45-1.77)

LTRA prescription, >=1 2.05 (1.92-2.18) <.001

LABA prescription (stand alone), >=1 1.21 (1.13-1.30) <.001

ICS MPR (%), 80%-100% (ref) 1.00 <.001

>0%-39.9% 0.88 (0.82-0.94)

40%-59.9% 0.88 (0.82-0.95)

60%-79.9% 0.94 (0.86-1.02)

>=100% 0.92 (0.86-0.98)

No ICS prescribed 0.65 (0.59-0.71)

Acute OCS courses, 0 (ref) 1.00 <.001

1 3.34 (3.37-3.71)

>=2 9.50 (8.94-10.08)

Asthma-related ED admission, >=1 1.76 (1.45-2.13) <.001

Primary care consultations, 0 (ref) 1.00 <.001

1-5 1.29 (1.13-1.48)

6-12 1.66 (1.45-1.90)

>=13 2.05 (1.78-2.36)

Table VIndependent baseline predictors (year 1) of 4 or more asthma attacks during the 2-y follow-up period as identified in the final multivariable modelED, Emergency
department; LTRA, leukotriene receptor antagonist; MPR, medication possession ratio; OR, odds ratio; PEF, peak expiratory flow; ref, reference category; SABA, short-acting β2
agonist.Collinearity of variables is described in this article’s Online Repository at www.jaci-inpractice.org.

Year 1 predictors Adjusted OR (95% CI) P value∗

Age (y), 12-18 (ref) 1.0 <.001

19-34 1.13 (0.91-1.40)

35-54 1.45 (1.19-1.77)

55-80 1.61 (1.31-1.97)

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Sex, female 1.31 (1.20-1.43) <.001

Body mass index, normal (ref) 1.0 <.001

Underweight 0.89 (0.65-1.22)

Overweight 1.18 (1.06-1.31)

Obese 1.27 (1.15-1.41)

Unknown 0.95 (0.76-1.20)

Smoking status, nonsmoker (ref) 1.0 <.001

Current smoker 1.29 (1.16-1.43)

Ex-smoker 1.02 (0.93-1.12)

Unknown 1.19 (1.01-1.39)

Rhinitis diagnosis, active† 1.24 (1.03-1.49) .023

Nasal polyps, ever 1.65 (1.42-1.93) <.001

Anaphylaxis diagnosis, ever 1.77 (1.17-2.68) .007

PEF % predicted, >=80% (ref) 1.0 <.001

<=60% 1.67 (1.50-1.86)

61%-79% 1.29 (1.17-1.43)

Unknown 1.26 (1.10-1.43)

Blood eosinophil count, <=0.4 x 109/L (ref) 1.0 <.001

>0.4 x 109/L 1.37 (1.24-1.53)

Missing 0.95 (0.86-1.05)

Mean SABA dose (μg/d),‡ 0 (ref) 1.0 <.001

1-200 0.89 (0.76-1.05)

201-400 1.13 (0.96-1.33)

>400 1.68 (1.43-1.97)

LTRA prescription, >=1 2.22 (2.01-2.45) <.001

LABA prescription (standalone), >=1 1.15 (1.03-1.30) .018

ICS MPR (%), 80%-100% (ref) 1.00 <.001

>0%-39.9% 0.81 (0.71-0.92)

40%-59.9% 0.90 (0.79-1.02)

60%-79.9% 1.01 (0.87-1.17)

>=100% 0.95 (0.84-1.07)

No ICS prescribed 0.71 (0.59-0.84)

Acute OCS courses, 0 (ref) 1.0 <.001

1 4.34 (3.94-4.79)

>=2 15.49 (14.09-17.04)

Asthma-related ED admissions, >=1 2.01 (1.55-2.62) <.001

Primary care consultations, 0 (ref) 1.0 <.001

1-5 0.94 (0.71-1.23)

6-12 1.39 (1.06-1.82)

>=13 1.81 (1.38-2.39)

Table VIPredicted risk (over 2 y) as calculated for 4 hypothetical patients with asthmaED, Emergency department; LTRA, leukotriene receptor antagonist; MPR, medication
possession ratio; NSAIDs, nonsteroidal anti-inflammatory drugs; PEFR, peak expiratory flow rate; SABA, short-acting β2 agonist.

Patient description
Risk of
>=2
attacks

Risk of
>=4
attacks

A 35-y-old woman who is obese, takes NSAIDs, and uses a lot of her SABA (mean, >400 μg/d)[list][list_item]Nonsmoker, PEFR >=80%, no comorbidities,
no OCS courses the prior year, 80%-100% MPR, 1-5 primary care consultations, no blood eosinophilia[/list_item][/list]

8.9% 1.1%

A 56-y-old man at step 4 who has a PEFR of 65% predicted and an incident finding of a high blood eosinophil count[list][list_item]Nonsmoker, normal
weight, no comorbidities, no OCS courses the previous year, 80%-100% MPR, 1-5 primary care consultations, SABA mean dose 1-200 μg/d[/list_item]
[/list]

4.7% 0.7%

An 18-y-old woman with rhinitis and eczema who has had 2 attacks in the last year and is on LTRA[list][list_item]Nonsmoker, PEFR >=80%, normal
weight, no other comorbidities, 80%-100% MPR, 6-12 primary care consultations, SABA mean dose 1-200 μg/d, no blood eosinophilia[/list_item][/list]

49.7% 17.1%

A 23-y-old man who smokes, has had a couple of ED attendances in the last year, and takes 25% of his ICS[list][list_item]PEFR >=80%, normal weight,
no comorbidities, >=2 OCS courses, 6-12 primary care consultations, SABA mean dose 1-200 μg/d, no blood eosinophilia[/list_item][/list]

38.8% 12.0%

Table E1Additional patient demographic and clinical characteristics during the baseline yearBAI, Breath-actuated inhaler; BTS, British Thoracic Society; DPI, dry powder inhaler;
FDC, fixed-dose combination; FP, fluticasone propionate; LTRA, leukotriene receptor antagonist; NSAIDs, nonsteroidal anti-inflammatory drugs; PEF, peak expiratory flow; pMDI,
pressurized metered-dose inhaler; SABA, short-acting β2 agonist.Data are n (%) unless otherwise noted.

Variable All patients (n = 118,981)

Charlson comorbidity index score

0 54,974 (46)

1-4 58,034 (49)

5-9 3,351 (3)

>=10 2,622 (2)

Recorded comorbidity∗

Rhinitis diagnosis, active† 3,567 (3)

Rhinitis diagnosis/therapy, active 36,312 (31)

Rhinitis diagnosis, ever 30,644 (26)

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Rhinitis diagnosis/therapy, ever 81,991 (69)

Nasal polyps, ever† 3,933 (3)

Eczema diagnosis, active† 4,321 (4)

Eczema diagnosis, ever 32,213 (27)

GERD diagnosis, active† 1,444 (1)

GERD diagnosis/therapy, active 23,861 (20)

GERD diagnosis, ever 9,640 (8)

GERD diagnosis/therapy, ever 40,593 (34)

Diabetes (type 1 or 2), ever 15,105 (13)

Cardiovascular disease, ever 29,688 (25)

Ischemic heart disease, ever 6,208 (5)

Heart failure, ever 873 (0.7)

Asthma education, ever 47,356 (40)

Mean daily ICS dose (μg/d)‡

0 14,636 (12)

<400 87,543 (74)

>=400 16,802 (14)

>=1 prescription during baseline

Paracetamol 28,166 (24)

Beta-blockers 3,334 (3)

Statins 18,159 (15)

BTS step§

No therapy 0 (0)

Step 1 13,761 (12)

Step 2 39,222 (33)

Step 3 27,837 (23)

Step 4 36,004 (30)

Step 5 2,144 (2)

Not assignable 13 (0.01)

ICS or FDC inhaler device type, last prescription

pMDI 69,604 (59)

DPI 28,920 (24)

BAI 5,821 (5)

No ICS 14,636 (12)

Spacer device prescribed with ICS pMDI 6,212 (9)

Acute OCS courses with lower respiratory consultation

0 115,117 (97)

1 3,436 (3)

>=2 428 (0.4)

Primary care consultation for asthma

0 37,367 (31)

1 51,115 (43)

>=2 30,499 (26)

Acute respiratory events

0 81,387 (68)

1 24,538 (21)

>=2 13,056 (11)

Antibiotics with lower respiratory consult

0 90,247 (76)

1 19,692 (17)

>=2 9,042 (7)

Asthma limiting daily activities, n with data 35,526

Yes 7,784 (22)

Asthma limiting night-time activities, n with data 36,250

Yes 6,261 (17)

Asthma is causing daytime symptoms, n with data 43,762

Yes 27,690 (63)

Table E2Results of univariable logistic regression analyses of asthma attack frequency (n = 118,981)BAI, Breath-actuated inhaler; BMI, body mass index; DPI, dry powder
inhaler; ED, emergency department; FP, fluticasone propionate; GINA, Global Initiative for Asthma; LRTI, lower respiratory tract infection; LTRA, leukotriene receptor
antagonist; MDI, metered-dose inhaler; OR, odds ratio; PEF, peak expiratory flow; SABA, short-acting β2 agonist.

Year 1 predictors
Asthma attacks within the 2-y outcome
period

<2, n (%) >=2, n (%)
OR (95%
CI)

<4, n (%) >=4, n (%)
OR (95%
CI)

Sex, female 58,816 (55) 8718 (68) 1.75 (1.69-1.82) 65,282 (56) 2252 (70) 1.85 (1.72-2.00)

Age (y)

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12-18 12,753 (12) 699 (6) 1.0 13,312 (12) 140 (4) 1.0

19-34 19,520 (18) 1861 (14) 1.74 (1.59-1.90) 20,984 (18) 397 (12) 1.80 (1.48-2.18)

35-54 39,459 (37) 4916 (39) 2.27 (2.09-2.47) 43,134 (37) 1241 (39) 2.74 (2.29-3.26)

55-80 34,513 (32) 5260 (41) 2.78 (2.56-3.02) 38,353 (33) 1420 (44) 3.52 (2.96-4.19)

Body mass index

Normal 32,339 (30) 3061 (24) 1.0 34,685 (30) 715 (22) 1.0

Underweight 3,221 (3) 259 (2) 0.85 (0.75-0.97) 3,430 (3) 50 (2) 0.71 (0.53-0.94)

Overweight 32,700 (31) 3908 (31) 1.26 (1.20-1.33) 35,629 (31) 979 (31) 1.33 (1.21-1.47)

Obese 30,536 (29) 5008 (39) 1.73 (1.65-1.82) 34,200 (29) 1344 (42) 1.91 (1.74-2.09)

Unknown 7,449 (7) 500 (4) 0.71 (0.64-0.78) 7,839 (7) 110 (3) 0.68 (0.56-0.83)

Smoking status

Nonsmoker 59,015 (56) 6474 (51) 1.0 63,941 (55) 1548 (48) 1.0

Current smoker 16,655 (16) 2367 (19) 1.30 (1.23-1.36) 18,394 (16) 628 (20) 1.41 (1.28-1.55)

Ex-smoker 23,631 (22) 3127 (24) 1.21 (1.15-1.26) 25,951 (22) 807 (25) 1.28 (1.18-1.40)

Unknown 6,944 (6) 768 (6) 1.01 (0.93-1.09) 7,497 (6) 215 (7) 1.19 (1.03-1.37)

Charlson comorbidity index score

0 50,250 (47) 4724 (37) 1.0 53,925 (47) 1049 (33) 1.0

1-4 50,853 (48) 7181 (56) 1.50 (1.44-1.56) 56,116 (48) 1918 (60) 1.76 (1.63-1.89)

5-9 2,876 (3) 475 (4) 1.76 (1.59-1.95) 3,218 (3) 133 (4) 2.12 (1.77-2.56)

>=10 2,266 (2) 356 (3) 1.67 (1.49-1.88) 2,524 (2) 98 (3) 1.99 (1.62-2.46)

Asthma education 42,009 (40) 5347 (42) 1.11 (1.07-1.15) 45,900 (40) 1456 (46) 1.27 (1.18-1.37)

PEF % predicted

<=60 11,045 (11) 2763 (22) 2.87 (2.72-3.02) 12,931 (11) 877 (27) 3.90 (3.54-4.30)

61-79 29,804 (28) 4046 (32) 1.56 (1.48-1.63) 32,797 (28) 1053 (33) 1.85 (1.69-2.03)

>=80 43,945 (41) 3835 (30) 1.0 46,964 (41) 816 (26) 1.0

Unknown 21,451 (20) 2092 (16) 1.12 (1.06-1.18) 23,091 (20) 452 (14) 1.13 (1.00-1.26)

Blood eosinophil count (x 109/L)

<=0.4 56,856 (54) 7947 (62) 1.0 62,834 (54) 1969 (62) 1.0

>0.4 11,271 (10) 1913 (15) 1.21 (1.15-1.28) 12,608 (11) 576 (18) 1.46 (1.33-1.60)

Unknown 38,118 (36) 2876 (23) 0.54 (0.52-0.56) 40,341 (35) 653 (20) 0.52 (0.47-0.57)

Rhinitis diagnosis, active∗ 3,060 (3) 507 (4) 1.39 (1.27-1.54) 3,415 (3) 152 (5) 1.64 (1.39-1.94)

Rhinitis diagnosis/drugs, active 31,073 (29) 5239 (41) 1.69 (1.63-1.76) 34,792 (30) 1520 (48) 2.11 (1.96-2.26)

Rhinitis diagnosis, ever∗ 26,921 (25) 3723 (29) 1.22 (1.17-1.27) 29,633 (26) 1011 (32) 1.34 (1.25-1.45)

Rhinitis diagnosis/drugs, ever 72,082 (68) 9909 (78) 1.66 (1.59-1.74) 79,389 (69) 2602 (81) 2.0 (1.83-2.19)

Eczema diagnosis, active 3,741 (4) 580 (5) 1.31 (1.19-1.43) 4,159 (4) 162 (5) 1.43 (1.22-1.68)

Eczema diagnosis, ever 28,570 (27) 3643 (29) 1.09 (1.05-1.14) 31,281 (27) 932 (29) 1.11 (1.03-1.20)

GERD diagnosis, active 1,188 (1) 256 (2) 1.82 (1.58-2.08) 1,373 (1.2) 71 (2.2) 1.89 (1.49-2.41)

GERD diagnosis/drugs, active 19,890 (19) 3,971 (32) 1.97 (1.89-2.05) 22,667 (20) 1194 (37) 2.45 (2.28-2.63)

GERD diagnosis, ever 8,130 (8) 1510 (12) 1.62 (1.53-1.72) 9,217 (8) 423 (13) 1.76 (1.59-1.96)

GERD diagnosis/drugs, ever 34,421 (32) 6172 (48) 1.96 (1.89-2.04) 38,853 (34) 1740 (54) 2.36 (2.20-2.54)

Cardiovascular disease, ever 25,616 (24) 4072 (32) 1.48 (1.42-1.54) 28,590 (25) 1098 (34) 1.59 (1.48-1.72)

Ischemic heart disease diagnosis, ever 5,297 (5) 911 (7) 1.47 (1.36-1.58) 5,948 (5) 260 (8) 1.63 (1.44-1.86)

Diabetes diagnosis, ever 12,983 (12) 2122 (17) 1.44 (1.37-1.51) 14,496 (13) 611 (19) 1.65 (1.51-1.81)

Heart failure diagnosis, ever 731 (0.7) 142 (1.1) 1.63 (1.36-1.95) 835 (0.7) 38 (1.2) 1.66 (1.19-2.29)

Anxiety or depression diagnosis, ever 4,909 (5) 903 (7) 1.57 (1.46-1.69) 5,558 (5) 254 (8) 1.71 (1.50-1.95)

Nasal polyps, ever 3,159 (3) 774 (6) 2.11 (1.95-2.29) 3,672 (3) 261 (8) 2.71 (2.38-3.09)

Anaphylaxis diagnosis, ever 414 (0.4) 98 (0.8) 1.98 (1.59-2.47) 482 (0.4) 30 (0.9) 2.27 (1.57-3.29)

Beta-blockers 2,964 (3) 370 (3) 1.04 (0.93-1.16) 3,248 (2.8) 86 (2.7) 0.96 (0.77-1.19)

Nonsteroidal anti-inflammatory drugs 23,930 (23) 3932 (31) 1.54 (1.47-1.60) 26,859 (23) 1003 (31) 1.51 (1.40-1.63)

Paracetamol 23,519 (22) 4647 (36) 2.02 (1.94-2.10) 26,841 (23) 1325 (41) 2.34 (2.18-2.52)

Statins 15,709 (15) 2450 (19) 1.37 (1.31-1.44) 17,531 (15) 628 (20) 1.37 (1.25-1.49)

Preventer device

No ICS 13,697 (13) 939 (7) 0.59 (0.55-0.63) 14,434 (12) 202 (6) 0.55 (0.47-0.63)

MDI 62,370 (59) 7234 (57) 1.0 67,861 (59) 1743 (55) 1.0

BAI 5,385 (5) 436 (3) 0.70 (0.63-0.77) 5,745 (5) 76 (2) 0.52 (0.41-0.65)

DPI 24,793 (23) 4127 (33) 1.44 (1.38-1.50) 27,743 (24) 1177 (37) 1.65 (1.53-1.78)

% ICS medication possession ratio

>0-39.9 34,519 (32) 3204 (25) 0.57 (0.54-0.61) 37,098 (32) 625 (19) 0.42 (0.38-0.47)

40-59.9 20,930 (20) 2444 (19) 0.72 (0.68-0.77) 22,775 (20) 599 (19) 0.66 (0.59-0.74)

60-79.9 8,144 (8) 1241 (10) 0.94 (0.87-1.01) 9,029 (8) 356 (11) 0.98 (0.86-1.13)

80-100 13,328 (12) 2165 (17) 1.0 14,896 (13) 597 (19) 1.0

>=100 15,627 (15) 2743 (22) 1.08 (1.02-1.15) 17,551 (15) 819 (26) 1.16 (1.05-1.30)

No ICS prescribed 13,697 (13) 939 (7) 0.42 (0.39-0.46) 14,434 (12) 202 (6) 0.35 (0.30-0.41)

ICS prescriptions

0 13,697 (13) 939 (8) 1.0 14,434 (13) 202 (6) 1.0

1-3 46,896 (44) 3999 (31) 1.24 (1.16-1.34) 50,142 (43) 753 (24) 1.07 (0.92-1.26)

>=4 45,652 (43) 7798 (61) 2.49 (2.32-2.67) 51,207 (44) 2243 (70) 3.13 (2.71-3.62)

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ICS inhalers

0 13,697 (13) 939 (8) 1.0 14,434 (13) 202 (6) 1.0

1-3 38,220 (36) 3100 (24) 1.18 (1.10-1.28) 40,761 (35) 559 (18) 0.98 (0.84-1.16)

>=4 54,328 (51) 8697 (68) 2.34 (2.18-2.52) 60,588 (52) 2437 (76) 2.87 (2.49-3.34)

ICS prescribed dose†

0 13,697 (13) 939 (7) 1.0 14,434 (13) 202 (6) 1.0

<400 52,506 (49) 4187 (33) 1.16 (1.08-1.25) 55,906 (48) 787 (25) 1.01 (0.86-1.18)

>=400 40,042 (38) 7610 (60) 2.77 (2.58-2.98) 45,443 (39) 2209 (69) 3.47 (3.00-4.02)

ICS average daily dose†

0 13,697 (13) 939 (7) 1.0 14,434 (13) 202 (6) 1.0

<400 79,400 (75) 8143 (64) 1.49 (1.39-1.60) 85,778 (74) 1765 (55) 1.47 (1.27-1.71)

>=400 13,148 (12) 3654 (29) 4.05 (3.76-4.37) 15,571 (13) 1231 (38) 5.65 (4.86-6.56)

ICS actual duration (d)

<=100 43,117 (41) 3374 (26) 1.0 45,837 (40) 654 (21) 1.0

101-219 34,177 (32) 4454 (35) 1.67 (1.59-1.75) 37,503 (32) 1128 (35) 2.11 (1.92-2.33)

>=220 28,951 (27) 4908 (39) 2.17 (2.07-2.27) 32,443 (28) 1416 (44) 3.06 (2.79-3.36)

ICS prescribed duration (d)

<=200 37,484 (35) 3116 (24) 1.0 39,966 (35) 634 (20) 1.0

201-319 33,676 (32) 4025 (32) 1.44 (1.37-1.51) 36,725 (32) 976 (30) 1.67 (1.52-1.86)

>=320 35,085 (33) 5595 (44) 1.92 (1.84-2.01) 39,092 (34) 1588 (50) 2.56 (2.34-2.82)

SABA prescriptions

0 11,051 (10) 941 (7) 1.0 11,783 (10) 209 (6) 1.0

1-3 55,143 (52) 4897 (39) 1.04 (0.97-1.12) 59,060 (51) 980 (31) 0.94 (0.81-1.09)

>=4 40,051 (38) 6898 (54) 2.03 (1.88-2.17) 44,940 (39) 2009 (63) 2.52 (2.19-2.92)

SABA inhalers

0 11,051 (11) 941 (7) 1.0 11,783 (10) 209 (7) 1.0

1-3 45,978 (43) 3925 (31) 1.00 (0.93-1.08) 49,151 (43) 752 (24) 0.86 (0.74-1.01)

>=4 49,216 (46) 7870 (62) 1.88 (1.75-2.02) 54,849 (47) 2237 (70) 2.29 (1.99-2.66)

SABA dose†

0 11,051 (10) 941 (7) 1.0 11,783 (10) 209 (7) 1.0

1-200 46,452 (44) 4015 (32) 1.02 (0.94-1.09) 49,692 (43) 775 (24) 0.88 (0.76-1.03)

201-400 26,490 (25) 3376 (27) 1.50 (1.39-1.62) 29,057 (25) 809 (25) 1.57 (1.35-1.84)

>400 22,252 (21) 4404 (35) 2.33 (2.16-2.51) 25,251 (22) 1405 (44) 3.14 (2.71-3.63)

LTRA prescriptions

0 101,223 (95) 10,763 (85) 1.0 109,546 (95) 2440 (76) 1.0

>=1 5,022 (5) 1973 (15) 3.69 (3.49-3.91) 6,237 (5) 758 (24) 5.46 (5.01-5.95)

LABA prescriptions

0 99,401 (94) 11,327 (89) 1.0 107,950 (93) 2778 (87) 1.0

>=1 6,844 (6) 1409 (11) 1.81 (1.70-1.92) 7,833 (7) 420 (13) 2.08 (1.87-2.32)

Spacer use

No 100,138 (94) 11,523 (91) 1.0 108,812 (94) 2849 (89) 1.0

Yes 6,107 (6) 1213 (10) 1.73 (1.62-1.84) 6,971 (6) 349 (11) 1.91 (1.71-2.14)

BTS step therapy (missing n = 13)§

1 12,983 (12) 778 (6) 1.0 13,615 (12) 146 (5) 1.0

2 36,863 (35) 2359 (19) 9.91 (8.85-11.10) 38,869 (34) 353 (11)
21.32 (17.5-
25.9)

3 25,354 (24) 2483 (20) 9.28 (8.42-10.23) 27,335 (24) 502 (16)
25.18 (21.7-
29.3)

4 29,689 (28) 6315 (50) 6.07 (5.51-6.68) 34,207 (30) 1797 (56)
12.45 (10.8-
14.3)

5 1,345 (1) 799 (7) 2.79 (2.55-3.06) 1,745 (2) 399 (12) 4.35 (3.97-4.9)

Asthma attacks

<2 103,116 (97) 9525 (75) 1.0 110,875 (96) 1766 (55) 1.0

>=2 3,129 (3) 3211 (25) 11.11 (10.5-11.7) 4,908 (4) 1432 (45)
18.32 (17.0-
19.8)

<4 106,055 (99.8) 12 156 (95) 1.0 115 409 (99.7)

2802 (88) 1.0

>=4 190 (0.2) 580 (5) 26.6 (22.6-31.4) 374 (0.3) 396 (12) 43.6 (37.7-50.5)

Acute respiratory events

0 77,098 (73) 4289 (34) 1.0 80,779 (70) 608 (19) 1.0

1 20,828 (20) 3710 (29) 3.20 (3.06-3.36) 23,791 (21) 747 (23) 4.17 (3.74-4.65)

>=2 8,319 (8) 4737 (37)
10.20 (9.77-
10.74)

11,213 (10) 1843 (58)
21.84 (19.9-
23.9)

Acute OCS courses

0 92,120 (87) 6150 (48) 1.0 97,310 (84) 960 (30) 1.0

1 11,106 (10) 3448 (27) 4.65 (4.44-4.87) 13,717 (12) 837 (26) 6.18 (5.63-6.80)

>=2 3,019 (3) 3138 (25) 15.57 (14.7-16.5) 4,756 (4) 1401 (44)
29.86 (27.4-
32.6)

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Acute OCS courses (with lower respiratory
consultation)

0 103,833 (98) 11284 (89) 1.0 112,499 (97) 2618 (82) 1.0

1 2,252 (2.1) 1184 (9) 4.84 (4.50-5.21) 3,004 (2.6) 432 (13) 6.18 (5.55-6.88)

>=2 160 (0.2) 268 (2) 15.41 (12.7-18.8) 280 (0.2) 148 (5) 22.7 (18.5-27.8)

Antibiotics (with lower respiratory consultation)

0 83,644 (79) 6603 (52) 1.0 88,966 (77) 1281 (40) 1.0

1 16,441 (15) 3251 (26) 2.51 (2.39-2.62) 18,886 (16) 806 (25) 2.96 (2.71-3.24)

>=2 6,160 (6) 2882 (23) 5.93 (5.64-6.24) 7,931 (7) 1111 (35) 9.73 (8.95-10.6)

Asthma-related ED admissions‡

0 105,760 (99) 12525 (98) 1.0 115,177 (99) 3108 (97) 1.0

>=1 485 (1) 211 (2) 3.67 (3.12-4.33) 606 (1) 90 (3) 5.51 (4.40-6.89)

Asthma consultations

0 34,564 (32) 2803 (22) 1.0 36,776 (32) 591 (18) 1.0

1 46,704 (44) 4411 (35) 1.16 (1.11-1.22) 50,219 (43) 896 (28) 1.11 (1.00-1.23)

>=2 24,977 (24) 5522 (43) 2.73 (2.59-2.86) 28,788 (25) 1711 (54) 3.70 (3.36-4.07)

Primary care consultations

0 5,363 (5) 255 (2) 1.0 5,557 (5) 61 (2) 1.0

1-5 52,274 (49) 3749 (29) 1.51 (1.32-1.72) 55,348 (48) 675 (21) 1.11 (0.85-1.44)

6-12 34,909 (33) 5165 (41) 3.11 (2.73-3.54) 38,760 (33) 1314 (41) 3.09 (2.39-3.99)

>=13 13,699 (13) 3567 (28) 5.47 (4.80-6.24) 16,118 (14) 1148 (36) 6.49 (5.01-8.41)

Asthma is limiting daily activities¶ 6,388 (6) 1396 (11) 2.14 (1.99-2.29) 7,327 (6) 457 (14) 2.88 (2.55-3.27)

Asthma is limiting night activities¶ 5,199 (5) 1062 (8) 1.83 (1.69-1.98) 5,905 (5) 356 (11) 2.46 (2.16-2.80)

Asthma is causing daytime symptoms¶ 24,270 (23) 3420 (27) 1.63 (1.52-1.74) 26,754 (63) 936 (29) 2.26 (1.96-2.60)

GINA controlǁ

Not available 30,739 (29) 3520 (28) 33,363 (29) 896 (28)

Controlled 12,833 (17) 881 (10) 1.0 13,562 (16) 152 (7) 1.0

Partly controlled 53,889 (71) 6376 (69) 1.72 (1.60-1.85) 58,765 (71) 1500 (65) 2.27 (1.93-2.69)

Uncontrolled 8,784 (12) 1959 (21) 3.25 (2.98-3.53) 10,093 (12) 650 (28) 5.75 (4.81-6.87)

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Details

Subject Patients;
Medical records;
Electronic health records;
Asthma;
Smoking;
Clinical medicine;
Studies;

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23 M.E. Charlson, P. Pompei, K.L. Ales, C.R. MacKenzie, A new method of classifying prognostic comorbidity in longitudinal studies: development and validation, J Chronic Dis,
Vol. 40, 1987, 373-383

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29 Royal College of Physicians. Why asthma still kills: the National Review of Asthma Deaths (NRAD) Confidential Enquiry Report. May 2014. Available from:
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30 J.D. Blakey, S. Zaidi, D.E. Shaw, Defining and managing risk in asthma, Clin Exp Allergy, Vol. 44, 2014, 1023-1032

31 E. Melen, B.E. Himes, J.M. Brehm, N. Boutaoui, B.J. Klanderman, J.S. Sylvia, Analyses of shared genetic factors between asthma and obesity in children, J Allergy Clin
Immunol, Vol. 126, 2010, 631-637.e1-8

32 O. Sideleva, B.T. Suratt, K.E. Black, W.G. Tharp, R.E. Pratley, P. Forgione, Obesity and asthma: an inflammatory disease of adipose tissue not the airway, Am J Respir Crit
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38 L.K. Williams, E.L. Peterson, K. Wells, B.K. Ahmedani, R. Kumar, E.G. Burchard, Quantifying the proportion of severe asthma exacerbations attributable to inhaled
corticosteroid nonadherence, J Allergy Clin Immunol, Vol. 128, 2011, 1185-1191.e2

39 D. Shaw, R. Green, M. Berry, S. Mellor, B. Hargadon, M. Shelley, A cross-sectional study of patterns of airway dysfunction, symptoms and morbidity in primary care asthma,
Prim Care Respir J, Vol. 21, 2012, 283-287

40 J.S. Taggar, T. Coleman, S. Lewis, L. Szatkowski, The impact of the Quality and Outcomes Framework (QOF) on the recording of smoking targets in primary care medical
records: cross-sectional analyses from The Health Improvement Network (THIN) database, BMC Public Health, Vol. 12, 2012, 329

41 J.K. Quint, H. Mullerova, R.L. DiSantostefano, H. Forbes, S. Eaton, J.R. Hurst, Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research
Datalink (CPRD-GOLD), BMJ Open, Vol. 4, 2014, e005540

42 T. Walley, A. Mantgani, The UK General Practice Research Database, Lancet, Vol. 350, 1997, 1097-1099

43 R.L. Tannen, M.G. Weiner, D. Xie, Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and
randomised controlled trial findings, BMJ, Vol. 338, 2009, b81

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Rhinitis;
Health risk assessment;
Cardiovascular disease;
Risk factors;
Primary care;
Gastroesophageal reflux;
Eosinophilia;
Eczema;
Chronic obstructive pulmonary disease;
Polyps;
Electronic medical records;
Respiratory function;
Databases

Location United Kingdom–UK
Identifier / keyword Asthma; Attack; Control; Medical record; Observational; Risk factor
Title Identifying Risk of Future Asthma Attacks Using UK Medical Record Data: A Respiratory Effectiveness Group Initiative
Author Blakey, John D; Price, David B; Pizzichini, Emilio; Popov, Todor A; Dimitrov, Borislav D; Postma, Dirkje S; Josephs, Lynn K; Kaplan, Alan;

Papi, Alberto; Kerkhof, Marjan; Hillyer, Elizabeth V; Chisholm, Alison; Thomas, Mike
Publication title Journal of Allergy and Clinical Immunology. In Practice; Amsterdam
Volume 5
Issue 4
Pages 1015-1024
Publication year 2017
Publication date Jul 1, 2017
Section Original Article
Publisher Elsevier Limited
Place of publication Amsterdam
Country of publication United Kingdom, Amsterdam
Publication subject Medical Sciences–Allergology And Immunology
ISSN 22132198
e-ISSN 22132201
Source type Scholarly Journal
Language of publication English
Document type Journal Article
DOI http://dx.doi.org/10.1016/j.jaip.2016.11.007
ProQuest document ID 1917932140
Document URL https://www.proquest.com/scholarly-journals/identifying-risk-future-asthma-attacks-using-uk/docview/1917932140/se-2
Copyright Copyright Elsevier Limited Jul 1, 2017
Last updated 2020-03-30
Database ProQuest One Academic

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https://www.proquest.com/10.1016/j.jaip.2016.11.007

Implementation and Effect of a Novel

Electronic Medical Record Format for

Patient Allergy Information

Masaharu NAKAYAMA, MD, PhD
a, b, 1

and Ryusuke INOUE, MD, PhD
b

a
Medical Informatics, Tohoku University Graduate School of Medicine, Miyagi, Japan.

b Medical IT Center, Tohoku University Hospital, Miyagi, Japan

Abstract. Adverse drug events (ADEs) are critical. Approximately 10% of fatal
ADEs are believed to be allergic reactions. Therefore, sharing patient allergy
information is beneficial to medical staff members in avoiding potentially lethal
complications. We previously performed a nationwide study of patient allergy
information in Japanese hospitals. The report showed that most of the responding
hospitals needed a standard format for reporting the information. To establish this,
we implemented a novel format for recording patient allergy information into the
hospital information system at Tohoku University Hospital; this format was created
through vigorous discussion among medical staff members with a variety of
specialties, including doctors, nurses, pharmacists, nutritionists, and medical safety
managers. In this study, we followed the amount of inputted allergy information and
the number of incidents involving medication after implementation. The amount of
allergy information inputted increased slightly. Although incidents involving
medication also increased slightly, ADEs due to allergy significantly decreased.

We

believe that our findings will be useful in helping to determine the optimal
characteristics of drug allergy information and to improve the dissemination of
information regarding potential allergens and subsequent ADEs.

Keywords. Allergy, Patient profile, Adverse drug event, Medical Safety

1. Introduction

The prevention of adverse drug events (ADEs) is important for patient safety.
1,2

We

previously performed a questionnaire-based study to describe the current status of data

collection for allergy information in the Electronic Medical Record (EMR) and

Computerized Physician Order Entry system (CPOE) in 76 large Japanese hospitals. The

report demonstrated that most of the responding hospitals claimed that they are either

preparing their own versions or still in the discussion phase. A patient profile standard

for correctly handling allergy information should be determined. We then implemented

a novel format for inputting patient allergy information into the hospital information

system at Tohoku University Hospital. This occurred after vigorous discussion among

medical staff members with a variety of specialties, including doctors, nurses,

pharmacists, and medical safety managers. In this study, we aim to test a new standard

format for recording such information.

1
Corresponding author: 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8574, Japan; E-mail:

nakayama@cardio.med.tohoku.ac.jp

Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth
A. Ugon et al. (Eds.)
© 2018 European Federation for Medical Informatics (EFMI) and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
doi:10.3233/978-1-61499-852-5-

51

51

2. Methods

2.1. A national survey regarding allergy information

We conducted a nationwide survey that included a 43-item questionnaire regarding the

handling of allergy information at 213 hospitals throughout Japan, each with 600 or more

beds, between October 2012 and March 2013. These included 50 public university

hospitals, 40 private university hospitals, 59 national/public/municipal hospitals, and 64

private hospitals. Of the 213 hospitals, 76 (35.7%) responded to the survey: 29 public

university, 11 private university, 18 national/public/municipal, and 18 private hospitals.

The mean (± standard deviation) number of beds in the responding hospitals was 796 ±

191. EMR was previously implemented in two-thirds of the hospitals responding to this

survey, whereas the others relied upon a computerized provider order entry (CPOE)

system with paper-based medical records.

2.2. Implementation of the template of allergy information

Between June 2013 and December 2014, a multi-professional medical team, including

five doctors, three nurses, three pharmacists, and two nutritionists, was recruited; the

team held discussions to design a practical and informative patient profile that could

easily integrated into EMR. The content of the profile was elaborated upon by the main

factors that our previous study showed. The profile was easily integrated into the EMR

system of the Tohoku University Hospital, and it has been in use in all departments since

May 2015.

2.3. Measurements of incidents involving medical error

After the implementation, we tracked the number of incidents involving medication

between January 2015 and March 2017. The incidents were measured by the medical

safety committee at Tohoku University Hospital. A chi-square test was performed for

statistical examinations.

3. Results

3.1. Description of the degree of allergic reaction and alert level in the EMR in our
national survey of allergy information

Our previous nationwide survey, which included a 43-item questionnaire, showed the

current status of handling patient allergy information in Japan. For example, in most

hospitals, the name of drugs related to allergies was not only selected from a list but also

inputted directly as text. Medical staff members tended to describe allergy symptoms as

accurately and as detailed as possible, even if the provided information contained

ambiguity. The level of ADE severity was not documented in 72% of the responding

hospitals. Automatic registration of analogous drugs that are the most frequent

candidates for allergy reaction, such as penicillin, iodinated contrast media, and aspirin,

was not possible in 86% of the responding hospitals. In 70% of the responding hospitals,

allergy information was linked to an alert system to prevent errors in prescriptions and

M. Nakayama and R. Inoue / Implementation and Effect of a Novel Electronic Medical Record Format52

injection orders. Among the other hospitals, 12% ranked the ADE severity in two

categories (i.e. heavy or mild) and 15% ranked them in multiple categories (Figure 1a).

However, the alert parameters corresponded to the severity of the allergy in only 7% of

the responding hospitals (Figure 1b).

Figure 1. The results of our previous survey regarding connection severity of allergic reactions and alert level.

3.2. Implementation of the template of allergy information

Figure 2 depicts a screenshot of the information in the hospital information system. We

implemented a practical and informative patient profile that was easily integrated into

EMR. The content of the profile was improved using the main factors that our previous

study showed. In this profile format, the severity of reaction can be selected from the list:

mild, moderate, and severe. An alert level can be selected to prevent prescription and

injection errors. The alert level parameters (limitations) are allergy severity, which is

graded as “prohibited,” “alert,” or “suspended.” An order for a drug linked to

�prohibited” is basically impossible to obtain, but this can be overridden in cases where

the medical benefit outweighs the risk or there is a procedure to reduce the severity of

ADE, such as steroid therapy for iodinated contrast dye allergy.

Figure 2. A screen shot of patient allergy information on EMR.

M. Nakayama and R. Inoue / Implementation and Effect of a Novel Electronic Medical Record Format 53

3.3. Change in the number of input data occurrences and incidents of medical error
after implementation

Figure 3 showed the number of input data occurrences for allergy information after

implementation. The average number of input occurrences gradually increased despite

the fact that the number of items pertaining to allergy information were greater than

before. Information regarding severity and alert level were also maintained. In figure 4,

the number of incidents were measured in Fiscal year (FY) 2015 (April 2015 to March

2016) and FY2016. Although incidents pertaining to medication also slightly increased,

ADEs due to allergy significantly decreased.

Figure 3. The number of input occurrences for allergy information.

Figure 4. The number of incidents involving medicine and those due to allergy between FY 2015 and FY 2016.

M. Nakayama and R. Inoue / Implementation and Effect of a Novel Electronic Medical Record Format54

4. Discussion

The present study explained a format for recording allergy information that we developed

in EMR with the collaboration of a multi-professional medical team and system

engineers and the consequent results of the number of input data occurrences and

incidents regarding medication. The new format has been accepted among medical staff

members, and this seemed to be effective in decreasing incidents of ADE due to allergy.

A format for allergy information with an effective alert system is needed for patient

safety in the EHR era. Computerized decision support system alerts that warn against the

incorrect administration of inappropriate drugs is expected to decrease the risk of ADEs.
3

However, these alerts are often overridden despite their potential benefits.
4,5

One of the

reasons is an excess of alerts with low predictive value for true drug allergies; these alerts

occur due to incorrect data entry.
6,7

Previous studies have identified poor medical record

documentation as the basis of ADEs
8,9

despite that fact that the inclusion of allergy

information in EHRs was reported at 64.4%.
10

In a previous study, most hospitals showed

that information about adverse drug reactions and contraindicated medicine were

recorded in the same form. However, the former informs about events that have happened

in the patient in the past, and the latter is information that does not pertain to events that

have happened to the patient, but reports those that may possibly occur because of disease

or other medications. Since we think that the frequent override of alerts in EMR

performed by doctors results in unreliable information, we separated them. In addition,

we added information about certainty and severity to ensure reliability of information.

In conclusion, we developed a format for allergy information that allows medical

professionals to include detailed information for effective alerts regarding incorrect

medication. We hope this proposal will be helpful in establishing a standard format for

allergy information that is useful in preventing allergy-related medication errors.

Acknowledgment
This study was supported by a Grant-in-aid from the Japanese Ministry of Health, Labour and Welfare, Tokyo,
Japan (12103386).

References

[1] J. Lazarou, B.H. Pomeranz, P.N. Corey, Incidence of adverse drug reactions in hospitalized patients: a
meta-analysis of prospective studies, JAMA 279(1998),1200-1205.

[2] P. Kanjanarat, A.G. Winterstein, T. Johns, et al., Nature of preventable adverse drug events in hospitals: a
literature review, Am J Health Syst Pharm 60(2003),1750-1759.

[3] G.J. Kuperman, A. Bobb, T.H. Payne, et al., Medication-related clinical decision support in computerized
provider order entry systems: a review., J Am Med Inform Assoc 14(2007),29-40.

[4] L.K. Taylor, R. Tamblyn. Reasons for physician non-adherence to electronic drug alerts. Stud Health
Technol Inform 107(2004),1101-1105.

[5] T.C. Hsieh, G.J. Kuperman, T. Jaggi, et al., Characteristics and consequences of drug allergy alert
overrides in a computerized physician order entry system, J Am Med Inform Assoc 11(2004),482-491.

[6] J.S. Stultz, and M.C. Nahata, Computerized clinical decision support for medication prescribing and
utilization in pediatrics, J Am Med Inform Assoc, 19(2012),942-953.

[7] A.J.I. Forster, A. Jennings, C. Chow, C. Leeder, C. van Walraven, A systematic review to evaluate the
accuracy of electronic adverse drug event detection, J Am Med Inform Assoc, 19(2012),31-8.

[8] M.C. Bouwmeester, N. Laberge, J.F. Bussieres, et al., Program to remove incorrect allergy documentation
in pediatrics medical records., Am J Health Syst Pharm 58(2001),1722-1727.

[9] A. Radford, S. Undre, N.A. Alkhamesi, et al., Recording of drug allergies: are we doing enough?, J Eval
Clin Pract 13(2007),130-137.

[10] R. Gonzalez-Gregori. et al., Allergy alerts in electronic health records for hospitalized patients. Ann
Allergy Asthma Immunol. 109(2012),137-140.

M. Nakayama and R. Inoue / Implementation and Effect of a Novel Electronic Medical Record Format 55

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REVIEW Open Access

Digital technologies for an improved
management of respiratory allergic
diseases: 10 years of clinical studies using
an online platform for patients and
physicians
Salvatore Tripodi1,2,3*† , Andrea Giannone4†, Ifigenia Sfika3, Simone Pelosi2, Stephanie Dramburg4,
Annamaria Bianchi5, Antonio Pizzulli6, Jakob Florack4, Valeria Villella3, Ekaterina Potapova4 and
Paolo Maria Matricardi4

Abstract

Background: Digital health technologies carry the great potential of assisting physicians in making well-informed
diagnostic and therapeutic decisions. In allergy care, electronic clinical diaries have been recently used to
prospectively collect patient data and improve diagnostic precision.

Objective: This review summarizes the clinical and scientific experience we gathered over 10 years of using a
digital platform for patients suffering from seasonal allergic rhinitis.

Methods: The mobile application and back-office of

AllergyMonitor

(TPS software production, Rome, Italy) enable
patients to record their daily allergy symptoms as well as drug and immunotherapy intake plus possible side effects
in a customizable way. The results can be accessed by the patient and attending physician as concise reports via a
smartphone or computer. This technology has been used in several clinical studies and routine practice since 2009.

Results: Our studies showed that A) the etiological diagnosis of SAR may be supported by matching prospectively
registered symptoms with pollen counts; B) it is possible to perform a short-term prediction of SAR-symptoms at
individual level; C) the adherence to daily symptom monitoring can remain high (> 80%) throughout several weeks
when prescribed and thoroughly explained by the treating doctor; D) the use of mobile technology can improve
adherence to symptomatic drugs as well as allergen-specific immunotherapy and E) the choice of the correct
symptom-severity-score is critical at patient level, but not at group level.

Conclusion: The studies and clinical practice based on the use of AllergyMonitor have proven the reliability and
positive impact of a digital platform including an electronic diary (eDiary) on the diagnostic precision of SAR in
poly-sensitized patients as well as patient adherence to both, drug therapy and allergen immunotherapy.

Keywords: Mobile health, E-diary, Pollen, Precision medicine, Digital

© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.

* Correspondence: salvatore.tripodi@gmail.com
†Tripodi Salvatore and Giannone Andrea contributed equally to this work.
1Allergology Service Policlinico Casilino, Via Casilina, 1049, 00169 Rome, Italy
2TPS software solutions, Rome, Italy
Full list of author information is available at the end of the article

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105
https://doi.org/10.1186/s13052-020-00870-z

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mailto:salvatore.tripodi@gmail.com

Introduction

Digital health
According to the World Health Organization (WHO),
digital health or eHealth (short for “electronic health”) is
defined as the cost-effective and secure use of information
and communication technologies (ICTs) for health and
health-related fields. mHealth (or “mobile health”), as a
component of eHealth, involves the provision of health
services and information via mobile technologies, such as
mobile phones, tablet computers and Personal Digital As-
sistants (PDAs) [1]. As stated during the 71st World
Health Assembly in Geneva (2018), mobile wireless tech-
nologies have the potential to revolutionize the interaction
of citizens with national health services. The use of simple
and easily accessible digital technologies can improve
quality and coverage of care, increase the access to health
information and services, raise awareness and promote
positive changes in health behaviors to prevent the onset
of acute and chronic diseases [2–4]. As the comprehensive
implementation of digital health programmes forms a
considerable challenge, the WHO Director General
encouraged Member States to identify standardized
approaches for applying digital health in their health
systems and services. Several aspects of traditional health
care will be changed by this digital health revolution: (a)
the point of care will no longer be the clinic or laboratory,
but the patient; (b) the approach to care will be based on
the individual patient instead of patient groups with simi-
lar diseases; (c) the traditional hierarchy between doctor
and patient (the former as an authority) based on pre-
scriptions and orders will be replaced by a partnership-
like collaboration (doctor as a guide); (d) patients’ data will
be determined as personal property, not that of any insti-
tution; (e) decisions will be based on the analysis of big
data sets in addition to the doctors’ experience; (f) the
costs of care will be diminished [5].

Digital health in Allergology
Digital Health may also have a very positive impact on the
management of allergic patients. As stated in a position
paper by the American College of Allergy, Asthma and
Immunology (ACAAI), allergic patients benefit from tele-
medicine, for example through a better patient-doctor
collaboration, easy access and adherence to allergists’
consultation as well as simplified prescription procedures.
This positive impact is especially important for patients
living in rural or remote areas. However, the authors also
point out the need for improved regulations, certification
programs, high attention to data protection, and the devel-
opment of adequate reimbursement systems [6]. Recently,
a Task Force of the European Academy of Allergy and
Clinical Immunology (EAACI) published a position paper
on “The Role of Mobile Health Technologies in Allergy
Care” [7]. The study group examined over 130 allergy-

related apps and reported on the role of mHealth
technologies in the area of allergic rhinoconjunctivitis,
asthma, atopic dermatitis, chronic urticaria as well as food
allergies, anaphylaxis, drug, and venom allergies [7].

Apps for allergic rhinitis
Although many apps are dedicated to the management
and monitoring of allergic rhinitis, only few have been
used in studies published in peer-reviewed international
journals [7, 8]. A very large collaborative network focused
on rhinitis and its treatment is accumulating evidence
through the worldwide use of MASK-Air (MASK standing
for Mobile Airways Sentinel Network). This electronic
clinical diary assesses nasal, ocular and lung symptoms, as
well as work impairment and global health via a visual
analogue scale (VAS) [9]. MASK-Air has already accumu-
lated real-life data from a large number of patients world-
wide, whose analysis has led to innovative knowledge on
productivity at work, innovative patterns of treatment,
and new allergic disease phenotypes [10]. A model of indi-
vidualized prediction of allergic rhinitis symptoms, named
Patient’s Hay-fever Diary (PHD), has been developed in
Austria [11]. By combining input from the patients (symp-
toms and medications) along with environmental informa-
tion, an improved management of the disease is pursued
through symptom forecasting [11].
In this review, we summarize the clinical and research

experience that our group has gathered over the last decade
with the platform “AllergyMonitor”, an eDiary for allergic
rhinitis and allergen immunotherapy whose first version
was developed in Rome, Italy, in 2009. In the following
sections, we shall illustrate the structure and content of the
digital platform, show exemplary reports of clinical cases,
an illustration of the scientific studies based on AllergyMo-
nitor, the perspective of studies in progress and the implica-
tions for allergy practice in real-life settings.

AllergyMonitor
AllergyMonitor: targets, structure, functions
Allergymonitor (TPS software production, Rome, Italy) is
an online service developed in 2009 with the aim of
enabling the recording of clinical symptoms, drug con-
sumption and adherence to allergen-specific sublingual
immunotherapy (SLIT) as well as monitoring efficacy of
SLIT or subcutaneous immunotherapy (SCIT) by patients
with allergic rhino-conjunctivitis and/or asthma. The
system, available to everyone and straightforward to use,
consists of two parts: a patient app (front end) and a web-
site for the attending doctor (back-office), and the whole
system is free during the actual Covid-19 pandemic. The
app, that patients can freely download form Google Play
and Apple Store, is available in different languages. On a
daily basis, the user is requested to fill a short and visually
enhanced questionnaire about his/her symptoms of the

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 2 of 11

eyes, nose and lungs, as well as a visual analogue scale on
his/her general allergic condition. Once activated by the
doctor via the back-office, the app user is also enabled to
record his/her daily medication intake, adherence to sub-
lingual immunotherapy and potentially occurring side
effects. In order to provide a summary and feedback to
the user, all entered data can be easily accessed within the
app in summarized graphs showing the evolution of
symptoms over time (Fig. 1).
Via his/her back-office, the doctor is able to access a

breakdown of all recorded data as well as individual pa-
tient reports accessible as different symptom (and medica-
tion) scores (Rhinoconjunctivitis Total Symptom Score
(RTSS), average adjusted symptom score (AdSS), rescue

medication score (RMS), average combined score (ACS))
and matched to local pollen monitoring data, which is re-
trieved using validated methodologies. More specifically:
pollen is collected in pollen traps, analyzed by aerobiolo-
gists, and this data is incorporated in AllergyMonitor. The
data import is done by email, weekly through an auto-
matic system in Italy, and manually for other countries
where AllergyMonitor is being used. A messaging system
between doctor and patient based on e-mail, chat or SMS
(short message service) facilitates direct communication.
An automatic alert system points out missed days of re-
cording to both, front end and back-office users. Keeping
in line with an approach of blended care, the back-office
enables the doctor to configure each patient’s front end

Fig. 1 Screenshot examples of the front end of AllergyMonitor app. On a daily basis, the user fills a short and visually enhanced questionnaire
about his symptoms of the eyes, nose and lungs, as well as a visual analogue scale on his/her general condition. Once activated by the doctor
via the back-office, the user is also enabled to record his/her daily medication intake, adherence to sublingual immunotherapy and potentially
occurring side effects. In order to provide a summary and feedback to the user, all entered data can be easily accessed within the app in
summarized graphs showing the evolution of symptoms over time

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 3 of 11

individually by entering for example symptomatic drugs
or adding an immunotherapy intake and side effects
monitoring (Fig. 2).

Clinical report
On the basis of data registered via the front end (app),
the software generates a printable report for the app user
(Fig. 3). The report is divided into several sections:

1. Doctor’s prescription – recommended monitoring
period, pharmacotherapy, allergen-specific
immunotherapy;

2. Symptoms vs pollen counts – Graphs illustrating
the time-trends of selected symptom severity scores
and pollen counts.

3. Medication diary – A table illustrating the intake
of drugs and/or SLIT during the monitoring period.

4. Statistical summary – A series of indexes
summarizing the patient’s adherence to symptom
recording, as well as drug and SLIT intake.

5. Space for the doctor’s comments – marked
empty space for comments and notes from the
treating physician.

The report produced by AllergyMonitor can be printed
and given by the patient to the physician of his/her choice,
but also directly sent by mail from the app to the doctor.

The doctor can then base further clinical examinations
and diagnostic or therapeutic prescriptions also on the
data prospectively acquired by the patient during the
monitoring period. The volume, reliability and precision
of the information provided by the eDiary may represent a
valuable and time-efficient add-on to the information
retrospectively collected during an often short interview,
frequently done months after the relevant clinical episodes
and possibly influenced by a strong recall bias.

Scientific studies
Since 2010, our group has used AllergyMonitor in a
series of clinical studies on allergic rhinitis. The results
of these have illustrated how the system can be deployed
to support the etiological diagnosis, symptom prediction,
adherence to therapy, and decision on AIT prescription
for patients presenting with seasonal allergic diseases.

1. Etiological diagnosis of seasonal allergic rhinitis
(SAR) – The analysis of the allergic rhinitis
symptom severity scores during pollen exposure
can be used to evaluate the clinical relevance of a
patient’s sensitization to a specific pollen. This
statement has been exemplified by describing two
patients suffering from SAR with similar diagnostic
challenges [12]. In both patients, no clear-cut
decision could be reached based on a traditional

Fig. 2 Screenshots of the doctor’s AllergyMonitor back-office. Via his/her back-office, the doctor is able to access a breakdown of all recorded
data as well as individual patient reports accessible as different symptom (and medication) scores and matched to local pollen monitoring data.
A messaging system between doctor and patient based on e-mail, chat or SMS (short message service) facilitates direct communication. The
back-office enables the doctor to configure each patient’s front end individually by entering for example symptomatic drugs or adding an
immunotherapy intake and side effects monitoring

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 4 of 11

allergological evaluation (clinical history, SPT,) plus
molecular IgE assessment against relevant genuine
and cross-reacting allergenic molecules (Ole e 1,
Phl p1, Phl p 5). However, the prospective and
consistent recording of nasal and conjunctival symp-
toms during the pollination period contributed funda-
mentally to the identification of the trigger-pollen (olive

for patient one (Fig. 4a), grass pollen for patient two
(Fig. 4b)). To our knowledge, this was the first report of
an etiological diagnosis of pollen allergy substantiated
by a smartphone app. The comparison of symptom
severity scores (RTSS in this case) with pollen concen-
tration data may therefore guide the doctor in the
choice of the correct immunotherapy composition [12].

Fig. 3 AllergyMonitor report: an example referring to a pediatric patient. The software generates a printable report for the user. The report is
divided into several sections, as follows: a) doctor’s prescription: recommended monitoring period, pharmacotherapy, allergen-specific immune-
therapy; b) symptoms vs pollen counts: graphs illustrating the time-trends of selected symptom severity scores and pollen counts; c) medication
diary: table illustrating the intake of drugs and/or SLIT during the monitoring period; d) statistical summary: a series of indexes summarizing the
patient’s adherence to symptom recording, as well as drug and SLIT intake; e) space for the doctor’s comments: marked empty space for
comments and notes from the treating physician

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 5 of 11

2. Short-term prediction of allergic symptoms –
We used AllergyMonitor to test the efficiency of a
model to forecast symptoms of pollen-related SAR at
individual patient level. We analyzed prospectively
recorded symptom and medication data (April to June
2010–2011) of 21 Italian children affected by allergic
rhinoconjunctivitis. Using the average combined score
(ACS) of symptoms and medication, we found that the
short-term forecast of seasonal allergic rhinitis
symptoms is possible even in highly poly-sensitized
patients in geographic areas with complex pollen
exposure. We further concluded that predictive models
must be tailored to the individual patient’s allergic
susceptibility. This may lead to a better use of anti-
symptomatic drugs, especially considering their targeted
intake before the expected raise of symptoms [13].

3. Adherence to eDiary compilation – Several e-
Diaries are available for pollen allergies in European
countries, some of them also having been used in
trials or observational studies [10, 14–18]. In most
of the study settings, the respective app was directly
downloaded by patients, with no or only occasional
intervention of the allergist [18–20]. Although the
use of mobile technologies permits an unprecedent-
edly easy collection of big data sets independent
from geographic location and social differences,
some observational studies were characterized by a
poor adherence of their users to data recording,
sometimes even dropping below 10% after only 2
weeks [10, 19]. As the role of the attending phys-
ician has been shown to be of great importance for
medication-compliance in patients [21], we
wondered whether this also holds true for the

adherence to digital symptom diaries. In an Italian
bi-center study involving 101 children and 93
adults, patients were instructed very clearly on the
use of AllergyMonitor and received personal
reminders via phone in addition to automated alert
messages in case of missed recording [22]. After
completing the individualized monitoring periods,
we could observe an overall adherence of ≥90%
within the first week, with a decline to 80–90%
between week 2 and 6 and then finally dropping to
70–80% after week 7 (Fig. 5). Interestingly, the
individual adherence level in week 2 and 3 was able
to predict a patient’s overall adherence to
monitoring with enough confidence (Spearman’s p
-0.55, P < .001 in both centers). We concluded that adherence to daily recording of an eDiary, provided that it is prescribed and eagerly motivated by a physician in a blended care setting, is very high.

4. Adherence to drug therapy – As an important
cause for treatment failure in asthma and rhinitis is
suboptimal adherence to local corticosteroids [23],
we hypothesized that the use of a monitoring app
with a reminder system might be able to optimize
also medication-compliance and by this the clinical
management of respiratory allergic diseases. The
need to take medications regularly to obtain max-
imum effect even when asymptomatic is a particular
problem for chronic diseases with episodic symp-
tom occurrence, such as seasonal allergic rhinitis
(hay fever). The reasons for suboptimal adherence
are complex, but the key to successful management
is good education both in the rationale for treat-
ment and inhaler technique. Telemedicine has

Fig. 4 Trajectories of symptom severity vs pollen counts in two pediatric patients (a: patient 1; b: patient 2) from Ascoli Piceno with allergic
rhinitis, and similar allergic profile, according to SPT and CRD. Data on severity of symptoms – collected with AllergyMonitor – have been
reported as Rhinoconjunctivitis Total Symptom Score (RTSS). Pollen counts (grains/m3) were obtained from the local pollen trap. Reprinted with
permission from [12]

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 6 of 11

found its way into most corners of health care, but
there is relatively little published on its potential
role in allergic disease. Therefore, we have
undertaken an original study looking at the value of
telemonitoring on adherence to daily treatment
with topical corticosteroids in children with severe
hay fever [15]. The study demonstrated an
improvement in both adherence to daily drug
medication and disease knowledge. No
improvement was seen in disease control, but pollen
counts were low during the study period (Fig. 6).

5. Adherence to Sublingual Immunotherapy – The
only disease-modifying treatment option for allergic
rhinoconjunctivitis and asthma so far is an allergen-

specific immunotherapy [24], which is mostly
administered as repeated subcutaneous injections or
the daily intake of sublingual tablets/drops. One of
the most relevant problems linked to the long-term
daily administration of sublingual immunotherapy
(SLIT) is poor compliance and a high dropout rate.
Only 50 and 20% of the patients starting the
treatment with SLIT continue its daily administra-
tion in the second and third year of treatment,
respectively [25]. When comparing long-term
adherence of a small group of patients undergoing
SLIT with usual care support versus a group of
patients receiving SLIT plus digital adherence
monitoring via AllergyMonitor, we observed a clear

Fig. 5 Adherence (%) by reporting day and study center. It is possible to describe three phases (indicated by light background color): the 1st
phase (a), lasting 6 days, during which adherence falls from 100 to 90%; the 2nd phase (b), lasting approximately 20 days, during which
adherence fluctuates until reaching 88%; the 3rd phase (c) during which it declines to 80%. Reprinted with permission from [22]

Fig. 6 Impact of a eDiary on (a) medication adherence and (b) knowledge on disease. a) Adherence to daily medication with nasal corticosteroid
(Mometasone) in children with Seasonal Allergic Rhinitis following usual care or being monitored with AllergyMonitor. b) Frequency of correct
answers to knowledge test taken before and after the recording of symptoms connected to bits of information on allergic rhinoconjunctivitis
provided via AllergyMonitor after every registration. Reprinted with permission from [15]

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 7 of 11

reduction in the drop-out rate in the second year of ther-
apy among 28 patients with digital support [26]. (Fig. 7)

6. Comparison of disease severity scores – To
assess the impact of different methodological
approaches on the interpretation of digitally and
prospectively collected data, we used several
different symptom severity scores to analyze the
data sets of two pediatric cohorts. In brief, 76
children with SAR from Ascoli Piceno (Italy) and
29 grass pollen allergic participants from Berlin
(Germany), were asked to monitor their daily
symptoms via the app during a period of 2 months
within the local grass pollen season. We then
prospectively compared six different severity scores
for allergic rhinitis (AR) against pollen counts at
both population and individual level (Fig. 8) [14],
namely the Rhinoconjunctivitis Total Symptom
Score (RTSS), the Adjusted Rhinoconjunctivitis
Total Symptom Score (method: last observation
carried forward) adjRTSS [LOCF], Adjusted
Rhinoconjunctivitis Total Symptom Score (method:
worst case) adjRTSS [WC] (rhinoconjunctivitis total
symptom score [worst case]), the Rhino-
conjunctivitis Allergy-Control-SCORE (RC-ACS©)
the average combined score (ACS), and the average
adjusted symptom score (AdSS). We found that the
disease severity scores for SAR tend to provide
similar results at population level but often produce
heterogeneous slopes in individual patients. On this
basis, we concluded that the choice of the disease
severity score might have only a low impact on the

outcome of a large clinical trial, but it may be cru-
cial for the management of individual patients [14].

Clinical routine and future perspectives
The use of AllergyMonitor in routine clinical practice
started in 2009 in the Pediatric Allergy Unit of the Pertini
Hospital in Rome. During 10 years of activity, about 9500
patients seeking care for allergic rhinitis in this hospital
have used the eDiary. On the other hand, about 130 aller-
gists and pediatricians in 10 countries have prescribed the
use of the app among their patients for clinical and/or re-
search purposes. Individual user feedback from doctors
shows, that the most appreciated benefits of prospectively
collected clinical data plus pollen counts is the increased
diagnostic precision especially for poly-sensitized patients
but also the improved adherence to SLIT (Fig. 9). As one
of the main benefits of AllergyMonitor is the interaction
and feedback from the doctor, the initiative to introduce
AllergyMonitor into the patients’routine comes predomin-
antly from the medical teams.
To evaluate the combined impact of molecular IgE re-

sults and mobile health technologies on the precision of
SAR diagnosis, our group has recently integrated Allergy-
Monitor in a more complex, still experimental clinical de-
cision support system (CDSS). This CDSS is based on
several steps of the diagnostic workup: collection of clin-
ical history, retrospective pollen calendar, determination
of allergic sensitization with allergen extracts, component-
resolved diagnostics (that already has a very important
role for the allergic diagnostic precision), clinical monitor-
ing via eDiary, and parallel pollen count data. Algorithms

Fig. 7 Impact of a eDiary on adherence to SLIT. Adherence to SLIT medication in children with Seasonal Allergic Rhinitis following usual care or
being monitored with AllergyMonitor. Reprinted with permission from [26]

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 8 of 11

then visualize the clinical and diagnostic picture of a
patient by interpreting all entered data according to inter-
national guidelines for every individual step. The impact
of this CDSS on the physicians’ diagnostic and therapeutic
decisions has been evaluated in an Italian pilot study
(@IT.2020) as well as an international multicenter project
(@IT.2020MC) involving 815 patients and over 150
doctors from nine study centers in seven Southern
European countries [27].

Conclusions and perspectives

The studies and clinical practice based on the use of
AllergyMonitor have proven the reliability of prospective

digital data collection via eDiary as well as its impact on
patient adherence to both, drug therapy and allergen
immunotherapy. The role of the attending physician is
fundamental, not only for an optimal adherence to
digital technologies, but also in a collaborative setting of
blended care. Over time, the interaction between doctors
and patients will progressively change with the increas-
ing use of digital opportunities. The possibility of
expanding the use of eDiaries and other mHealth plat-
forms into forecasting, through the translation of gath-
ered data into a way of preventing individual patient
exposure to unfavorable conditions such as high pollen
counts, elevated air pollution levels, anti-symptomatic

Fig. 8 Parallel evaluation of multiple disease severity scores. Trajectories of normalized mean daily values of six disease severity scores in (a) 76
Italian, and (b) 29 German children with grass pollen-related seasonal allergic rhinitis, during the grass pollen season. Reprinted with permission
from [14]

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 9 of 11

drug intake, is another important aspect to be taken into
account. In order to implement these technologies re-
sponsibly in clinical practice to improve patient partici-
pation and care, studies and regulatory infrastructure are
needed as acknowledged by international organizations
such as the WHO. Given the current pandemic setting and
unprecedent situation wordlwide, the impact and urgency of
reliable and qualified mHealth systems is evident. In a time
where human contact has been reduced, and health institu-
tions and teams are overwelmed with critical patients, the
benefits provided by digital platforms in patient care are
substantial. It is, therefore, urgent to move forward with
regulations and developments in this perspective.

Abbreviations

ACAAI: American College of Allergy, Asthma and Immunology; ACS: Average
combined score; adjRTSS [LOCF]: Adjusted Rhinoconjunctivitis Total
Symptom Score (method: last observation carried forward); adjRTSS
[WC]: Adjusted Rhinoconjunctivitis Total Symptom Score (method: worst
case); AdSS: Average adjusted symptom score; AIT: Allergen immunotherapy;
AR: Allergic rhinitis; CDSS: Clinical decision support system; EAACI: European
Academy of Allergy and Clinical Immunology; eDiary: Electronic diary;

eHealth: Electronic health; ICT: Information and communication technologies;
MASK: Mobile Airway Sentinel Network; mHealth: Mobile health;
PDA: Personal digital assistant; RC-ACS©: Rhino-conjunctivitis Allergy-control-
score; RMS: Rescue medication score; RTSS: Rhinoconjunctivitis total
symptom score; SAR: Seasonal allergic rhinitis; SCIT: Subcutaneous
immunotherapy; SLIT: Sublingual immunotherapy; SMS: Short message
service; SPT: Skin prick test; VAS: Visual analogue scale; WHO: World Health
Organization

Acknowledgments

Not applicable.

Authors’ contributions

ST and AG contributed equally in writing the manuscript. IS, SP, SD, AB, AP,
FJ, VV, EP were involved in data collection and analysis. PM and ST
conceived the studies. All authors edited, reviewed and approved the final
manuscript.

Funding

Not applicable.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are
not publicly available.

Fig. 9 Symptom monitoring of a pediatric patient with SAR during grass pollen SLIT. RTSS and pollen trajectories before starting SLIT (a) and
after 1 (b), 2 (c) and 3 years of SLIT (d)

Tripodi et al. Italian Journal of Pediatrics (2020) 46:105 Page 10 of 11

Ethics approval and consent to participate

This was a review article and not a study involving human subjects, so IRB
approval was not required.

Consent for publication

Not applicable.

Competing interests

Dr. Tripodi reports personal fees from TPS Production srl, during the conduct
of the study; In addition, Dr. Tripodi has a patent 102,017,000,106,570 issued
to TPS Production srl. Simone Pelosi reports personal fees from TPS
Production srl. P.M. Matricardi is funded by the Deutsche
Forschungsgemeinschaft (DFG; grant number MA 4740/2–1), is a consultant
for HYCOR Biomedical, Euroimmun, Thermo Fisher Scientific (TFS), has
received research funding from HYCOR Biomedical, Euroimmun, reagents for
research from Thermofisher; and speaker’s fees from Euroimmun, Thermo
Fisher Scientific, Stallergenes-Greer, HAL Allergy. All other authors declare
that they have no conflict of interest.

Author details

1Allergology Service Policlinico Casilino, Via Casilina, 1049, 00169 Rome, Italy.
2TPS software solutions, Rome, Italy. 3Pediatric Allergology Service Sandro
Pertini Hospital, Rome, Italy. 4Pediatric Pulmonology, Immunology and
Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany.
5Pediatric Department, San Camillo Hospital, Rome, Italy. 6Allergy Practice,
Berlin, Germany.

Received: 15 April 2020 Accepted: 16 July 2020

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Abstract
Background
Objective
Methods
Results
Conclusion
Introduction
Digital health
Digital health in Allergology
Apps for allergic rhinitis
AllergyMonitor
AllergyMonitor: targets, structure, functions
Clinical report
Scientific studies
Clinical routine and future perspectives
Conclusions and perspectives
Abbreviations
Acknowledgments
Authors’ contributions
Funding
Availability of data and materials
Ethics approval and consent to participate
Consent for publication
Competing interests
Author details
References
Publisher’s Note