JMIR MEDICAL INFORMATICS

Morris et al

Original Paper

An Iterative Process for Identifying Pediatric Patients With Type
1 Diabetes: Retrospective Observational Study
Heather Lynne Morris1, BA, MS, PhD; William Troy Donahoo2, MD; Brittany Bruggeman2, MD; Chelsea Zimmerman2,
MD; Paul Hiers2, MD; Victor W Zhong3, PhD; Desmond Schatz4, MD
1

Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States

2

Department of Medicine, University of Florida, Gainesville, FL, United States

3

Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States

4

Department of Pediatrics, University of Florida, Gainesville, FL, United States

Corresponding Author:
Heather Lynne Morris, BA, MS, PhD
Department of Health Outcomes and Biomedical Informatics
University of Florida
P.O. Box 100177
Gainesville, FL,
United States
Phone: 1 3526279074
Email: hlmorris27@ufl.edu

Abstract
Background: The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing.
However, the current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial
manual efforts.
Objective: The purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes
and separating T1DM from T2DM in pediatric patients.
Methods: This retrospective study utilized a data set from the University of Florida Health Integrated Data Repository to identify
300 patients aged 18 or younger with T1DM, T2DM, or that were healthy based on a developed computable phenotype. Three
endocrinology residents/fellows manually reviewed medical records of all probable cases to validate diabetes status and type.
This refined computable phenotype was then used to identify all cases of T1DM and T2DM in the OneFlorida Clinical ResearchConsortium.
Results: A total of 295 electronic health records were manually reviewed; of these, 128 cases were found to have T1DM, 35
T2DM, and 132 no diagnosis. The positive predictive value was 94.7%, the sensitivity was 96.9%, specificity was 95.8%, andthe negative predictive value was 97.6%. Overall, the computable phenotype was found to be an accurate and sensitive method
to pinpoint pediatric patients with T1DM.
Conclusions: We developed a computable phenotype for identifying T1DM correctly and efficiently. The computable phenotype
that was developed will enable researchers to identify a population accurately and cost-effectively. As such, this will vastly
improve the ease of identifying patients for future intervention studies.
(JMIR Med Inform 2020;8(9):e18874) doi: 10.2196/18874
KEYWORDS
computable phenotype; type 1 diabetes; electronic health record; pediatricIntroduction
Diabetes is one of the most common chronic diseases seen
during childhood and adolescence. The incidence and prevalenceof diabetes mellitus has continued to increase worldwide for
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both type 1 diabetes (T1DM) and type 2 diabetes (T2DM), withthe rise in T2DM due in large part to the obesity epidemic [1,2].
Uncontrolled T1DM leads to short- and long-term complications
and early mortality [3-6].

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The vast majority of the population data about the incidence,prevalence, and effects of diabetes in youth in the United States
come from select sites, such as the SEARCH for Diabetes inYouth Study [7] and the T1D Exchange [8]. In the past, outsideof highly manicured registries, the thorough and accurate
identification of pediatric patients with T1DM versus T2DM
could only be accomplished by manual clinical record review,which was both costly and time-consuming, requiring manual
medical record reviews. Currently, through the use of algorithms
derived from electronic health record data, accurate
identification of patients with T1DM versus T2DM may bepossible. One such algorithm using a subset of SEARCH cohort
revealed a 89% positive predictive value (PPV) and a 97%
negative predictive value using only ICD-10-CM codes [9].
However, this study was conducted within a self-contained data
set overseen by Kaiser Permanente. As such, this does not givea comprehensive insight into patients seen at a variety of healthsettings using different electronic record systems. There is thus
a need for timely real-world population-level monitoring of theincidence, prevalence, and disease course of diabetes in youththat includes the ability to separate T1DM from T2DM.
The overall purpose of this project was to develop and validate
an algorithm to identify pediatric patients with T1DM in an
efficient and accurate manner that would be valid in a real-world
database outside of a closed medical system such as Kaiser
Permanente.

Methods
Population
University of Florida HealthPatients eligible for inclusion in this study were aged 0-18 andseen at University of Florida Health (UF Health). The UF Health
System is a medical network associated with the University of
Florida with the only comprehensive pediatric facility in North
Central Florida. The Integrated Data Repository (IDR) is alarge-scale database that collects and organizes information
across UF Health’s clinical and research enterprises. The IDR
is a secure, clinical data warehouse that aggregates data fromthe university’s clinical and administrative information systems,
including the electronic health record system. As of 2018, the
IDR housed more than 1 billion observational facts across morethan 1 million patients. For query 1, the IDR was utilized to
identify 300 patients for the development of the computablephenotype. Similar to other studies, 100 individuals per cohort
were selected (T1DM, T2DM, no diagnosis) with the nodiagnosis classification being used as the reference group.

OneFlorida
The OneFlorida Clinical Research Consortium contains over
12 million unique patient records from as early as of 2012,
including Medicaid claims records. This database is maintained
and updated on a quarterly basis with information from partners
across the state of Florida. The OneFlorida Data Trust’s
repository of statewide health care data is regularly updatedwith the inclusion of new partners and data refreshes fromexisting partners. All data are cleaned, transformed, curated,and contained in a centralized data warehouse, allowing
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Morris et al
streamlined inquiries and uniform results based on high-quality
data. At present, data on 15 million patients across 22 hospitals
are included within the data set going back to 2012, of whichapproximately 4.3 million are pediatric patients aged 18 oryounger across thousands of providers, clinics, practices, andmultiple hospital systems throughout the state of Florida. A
SAS code that was developed from the algorithm was used to
identify eligible members. Previous work has demonstrated that
the OneFlorida Data Trust demographics are similar to estimatesreported by the US Census Bureau [10,11]. Five OneFloridasites that did not have prescribing data were excluded. For
queries 2 and 3, we limited our results to patients aged 0-18
seen within the OneFlorida Data Trust in the year 2018.

Study Overview
In query 1, the initial algorithm for differentiating T1DM and
T2DM was developed and validated with chart reviews using
data from the UF Health system. Subsequently, this algorithm
was utilized in the OneFlorida database (query 2).

QueriesQuery 1: Computable Phenotype Algorithm Development
Using UF Health IDR
For the development of the algorithm, we identified individuals
in the UF Health System that would meet the criteria of having
T1DM or T2DM, and a cohort with no diagnosis of either forcomparison. A total of 300 random records were requested fromthe IDR with 100 of each of the following: T1DM, T2DM, andno diagnosis of either. The criteria for diagnosis of T1DM useddiagnosis codes, medication dispensing, and laboratory results.
Patients met the T1DM algorithm criteria if they were less thanor equal to 18 years of age as of December 31, 2016, andfulfilled the following criteria: (1) inpatient/outpatient with
ICD-9/10 for T1DM and insulin medication within 90 days or(2) inpatient/outpatient with ICD-9/10 for T1DM and glucose>200 mg/dL or (3) inpatient/outpatient with ICD-9/10 for T1DM
and hemoglobin A1c > 6.5%.
The type 2 criteria differed slightly in that it involved ICD-9/10
for patients with T2DM under the age of 18. For each identified
member within the 300 total records, we obtained data on age,sex, race, ethnicity, height, weight, BMI, diagnoses, locationof services, and the admit date. In order to account for a numberof conflicting diagnoses for individual patients, a diagnosis ratio
was used to make a final diagnosis categorization (T1DM vs
T2DM). Conflicting diagnosis codes occurred when patientswere seen by multiple providers, or different settings, andreceived both a T1DM and T2DM in the electronic healthrecord. In order to receive a designation of T1DM or T2DM,
they had to have greater occurrences of one diagnosis. Diagnosis
ratio designations were applied prior to the medical record
review to allow for further investigation.
The data management for query 1 was managed in a REDCap
database [12]. A data abstraction form was developed for use
by the medical record reviewers to manually abstract data related
to a diabetes mellitus diagnosis and treatment from the medical
records. This form was utilized to collect demographic data anddiabetes-related clinical information including the most recent

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record of height, weight, hemoglobin A1c, and if islet
autoantibodies were present (and type).

Query 3: Computable Phenotype Algorithm Using
OneFlorida Revised

Medical Record Review
A total of 3 pediatric endocrinology fellows (BB, CZ, and PH)
evaluated the medical records to determine the true diagnosis.
A total of 295 cases, with an overlap of 41 cases to assessinterrater reliability, were reviewed. For quality assurance, 14%(40/295) of all records were manually abstracted by multiple
reviewers (BB, CZ, or PH). Any discrepancies were adjudicatedby a senior reviewer (WD). All reviewers were blinded to thediagnosis category patients were assigned to. Each reviewer
accessed the patient electronic health records to evaluate themedical record thoroughly to make a final diagnosis. Patientswere given a designation of T1DM if they fell into the range ofclinical criteria including diagnosis at a younger age, a historyof diabetic ketoacidosis, positive antibody status, lower insulin
requirements, and lower BMI. Additional data were abstracted
so the most up-to-date information for laboratory values wasrecorded. Reviewers entered all information into a REDCap
database. Following the review, data were exported into SPSS
and reviewed for interrater reliability. A total of 5 cases were
evaluated in greater depth due to missingness, terminology, anda differing diagnosis. The sensitivity, PPV, negative predictive
value, and specificity were calculated using the numerators anddenominators from the medical record review.

In the initial run of the computable phenotype in the OneFlorida
Clinical Research Consortium, there was an inconsistency inthe number of cases of patients with T1DM and T2DM. Morespecifically, there were more cases of patients with T2DM thanon average. We revised the algorithm to include additional
pharmacy data to identify patients who met the algorithm criteria
where patients with a diagnosis code of T2DM were also
required to have a prescription of metformin.

Query 2: Computable Phenotype Algorithm using
OneFlorida
Abstraction conducted in query 1 highlighted a number offalse-positive diagnoses. In order to correctly categorize patientswith other forms of diabetes (eg, cystic fibrosis–related diabetes,
maturity-onset diabetes of youth, neonatal hyperglycemia), we
separated patients with these diagnostic codes into a third cohort
identified as other diabetes. We revised the algorithm to include
patients with ICD-10 of Neonatal Diabetes Mellitus P70.2
instead of P61.0 for the Other DM categories. This resulted ina reduction of 5397 patients across all years (originally 9727),
and 685 patients in the year 2018 alone (previously 1316).

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Results
Computable Phenotype Algorithm Development Using
UF Health IDR
In our first query of 300 medical records drawn from the UF
Health IDR, 5 cases had no discerning diagnosis (conflicting
diagnosis of T1DM and T2DM) based on the diagnosis ratio,
and therefore, these were excluded from the study. A total of
295 records were reviewed. Table 1 shows the demographicsof these patients.
After applying a diagnosis ratio between hospital encounters,
there were a total of 131 patients with T1DM, 64 with T2DM,
and 100 with no diagnosis of either. Of the 131 patients
identified using the computable phenotype algorithm, abstractors
confirmed a diagnosis of T1DM for 125 patients (true positive;
Table 2), which yielded a PPV of 96.8% (Table 2). Upon
validation with the medical record review, it was confirmedthat 7 patients were incorrectly identified (false positive; Table2) by the algorithm. These patients instead were found to have
either no diagnosis (n=5) or T2DM (n=2). The final computablephenotype algorithm was determined to have a sensitivity of
95.3%. The T2DM algorithm had a lower PPV than T1DM
(51.6%) but had a high sensitivity (94.3%) and specificity(97.5%).

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Table 1. UF demographics.
Demographic

Overall (N=295)

No diagnosis (N=132)

T1DMa (N=128)

T2DMb (N=35)

Age, mean (SD)10.7 (5.44)

7.8 (5.56)

12.3 (4.07)

15.4 (2.87)

Male, n (%)

134 (45.4)

63 (47.7)

60 (46.9)

11 (31.4)

Female, n (%)

161 (54.6)

69 (52.3)

68 (53.1)

24 (68.6)

Caucasian, n (%)

179 (60.7)

79 (59.8)

87 (68.0)

13 (37.1)

African American, n(%)

62 (21.0)

33 (25.0)

10 (7.8)

19 (54.3)

Hispanic, n (%)

31 (10.5)

11 (8.3)

19 (14.8)

1 (2.9)Asian, n (%)

2 (0.7)2 (1.5)

0 (0)

0 (0)

Multiple races, n (%)

15 (5.1)

4 (3.0)9 (7.0)2 (5.7)

Missing, n (%)

6 (2.0)3 (2.3)3 (2.3)0 (0)UFc Health, n (%)

231 (78.3)

81 (61.4)

117 (91.4)

33 (94.3)

Autoantibodies presence, n(%)

67 (22.7)

2 (1.5)65 (50.8)0 (0)38 (12.9)

13 (9.8)

23 (18.0)

2 (5.7)

Glucose level, mean (SD);range

153.86 (95.45); 7-555

89.43 (30.33); 7-284

207.59 (98.57); 58-555

161.11 (96.83); 64-432

Hemoglobin A1c, mean(SD); range

8.62 (2.31); 4.8-14.00

5.48 (0.58); 4.8-7.5

9.27 (1.80); 5.6-14

7.92 (2.90); 4.9-14.00

GenderRace

Treatment facility

Ethnicity
Hispanic, n (%)

a

T1DM: type 1 diabetes mellitus.

b

T2DM: type 2 diabetes mellitus.

c

UF: University of Florida.

Table 2. Results from query 1.
Query 1T1DMa case identified via CPb
algorithmaTotal confirmed, nSensitivity, %Specificity, %Positive predictive value, %Negative predictive value, %

131

124

96.9

95.8

94.7

97.6

64

33

94.3

88.1

51.6

99.1

c

T2DMd case identified via CP
algorithm

Total reviewed, ne

T1DM: type 1 diabetes mellitus.

b

CP: computable phenotype.

c

T1DM algorithm: sensitivity=124/124+4; specificity=160/160+7; PPV=124/124+7; NPV=160/160+4.

d

T2DM: type 2 diabetes mellitus.

e

T2DM algorithm: sensitivity=33/33+2; specificity=229/229+31; PPV=33/33+31; NPV=229/229+2.

Computable Phenotype Algorithm Performance in
OneFlorida
In the second query, the performance of the algorithm was tested
in the OneFlorida Data Trust. Although the validity of using
only ICD codes for the determination of diabetes type in youth
has been demonstrated in the large integrated health system ofKaiser Permanente Southern California [9], and while our
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algorithm was based largely on ICD codes and did very well in
the UF Health IDR, when this was run in the OneFlorida Data
Trust, there were issues with appropriate categorization as
described in the “Methods” section. As these numbers were notconsistent with what we know about the epidemiology andbiology of T1DM versus T2DM in youth [13], we undertook arevision of the algorithm.

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The revised algorithm included additional pharmacy data toidentify patients who met the algorithm criteria. In the revision,
patients with a diagnosis code of T2DM were also required tohave a prescription of metformin. The results from the finalalgorithm are presented in Table 3. The majority of patients
identified by the algorithm had a diagnosis of T1DM (n=4246)
followed by other DM (n=660) and T2DM (n=550). Patientswith T1DM had an even distribution of male and female, werepredominantly White (2153/4246, 50.71%), between 11 and 15
years of age (1789/4246, 42.13%), and on insulin (3907/4246,
92.02%). Patients identified as having T2DM were more likely
to be female (342/550, 62.1%), other race (190/550, 34.5%),
Black (241/550, 43.8%), and between 16 and 18 years of age(300/550, 54.5%). Because of the already high sensitivity andspecificity of the less robust initial algorithm for T1DM, we did
not do additional chart reviews for the revised algorithm.

Table 3. Results of final algorithm in OneFlorida.
T1DMa (N=4246)

T2DMb (N=550)

Other DM (N=660)

Female, n (%)

2120 (49.93)

342 (62.18)

326 (49.39)

Male, n (%)

2126 (50.07)

208 (37.82)

334 (50.61)

White, n (%)

2153 (50.71)

117 (21.27)

195 (29.55)

Black, n (%)

709 (16.70)

241 (43.82)

234 (35.45)

Asian, n (%)

23 (0.54)

N/Ac

N/Ac

Other/unknown, n (%)

1361 (32.05)

190 (34.55)

229 (34.70)

0-5 years, n (%)

253 (5.96)

N/Ac

512 (77.58)

6-10 years, n (%)

895 (21.08)

N/Ac

31 (4.70)

11-15 years, n (%)

1789 (42.13)

240 (43.64)

66 (10.00)

16-18 years, n (%)

1309 (30.83)

300 (54.55)

51 (7.73)

3907 (92.02)

284 (51.64)

63 (9.55)

Demographic
Sex

Race

Age

Insulin, n (%)
a

T1DM: type 1 diabetes mellitus.

b

T2DM: type 2 diabetes mellitus.

c

N/A: no available data (ie, no patients identified).

Discussion
Principal Findings
Overall, the computable phenotype we developed to identify
pediatric patients with T1DM was effective using data within
the electronic health record. The identification of patients withdiabetes can be complex and conflicting diagnosis codes makeit even more difficult to disentangle an accurate classification.
As such, the use of additional clinical parameters to narrow thefocus to a specific population refines the specificity of thealgorithm. For T1DM, this includes laboratory values (A1c ≥6.5, glucose ≥ 200 m/g).
For the purposes of this study, we drew upon the parameters
already defined by the SEARCH study which allows researchers
to identify adults with T1DM. Referencing this study, we maderefinements to account for variations among pediatric patients.
The utility of this computable phenotype is that it enables us to
identify patients with an accuracy of 97%. Identification ofpatients solely based on the data found within the electronic
health record can be complex, thus accounting for our need ofnumerous queries. The idiosyncrasies of diagnosis codes andlimited recordings of HbA1c for patients added complexities
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to the methods of identification. In our experience, diagnosis
codes for patients often had contradictions. For example, apatient seen multiple times in the measurement year in various
settings may have conflicting diagnosis (ie, T1DM and T2DM).
To overcome this problem, we applied a diagnosis ratio toinclude the most prevalent diagnosis. This is an importantconsideration for other individuals utilizing electronic healthrecords for identification. The identification of pediatric patientssolely based on the ICD-9 or ICD-10 code only allows us tolook at patients on the surface level rather than as a whole.
The findings from this study were instrumental in developing
a computable phenotype to identify pediatric patients with
T1DM. Through this process, a number of limitations were ofnote that should be considered. First, the utilization of theelectronic health record presented a few obstacles that were notoriginally foreseen, particularly the conflicting diagnoses ofpatients. Inaccuracies and data entry error are plausible withinlarge data sets and need to be accounted for. Being aware of thepossibility of inaccurate diagnoses increases the importance of
not relying solely on ICD-9 and ICD-10 diagnoses for
identifying patients. Similarly, this impacted our proposed
methodology of 100 individuals for each of the 3 cohorts (ie,T1DM, T2DM, and no diagnosis). These differences were
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accounted for in our calculations of predictive value, sensitivity,and specificity, but still need to be noted as a potential limiting
factor. Another limitation of this paper is that the medical record
review was limited to 1 health care system. While we were able
to identify all pediatric patients within the OneFlorida ClinicalResearch Consortium with T1DM, we were unable to accessindividualized records within each of the contributing data
centers and thus unable to conduct medical record reviews at
each site. Additionally, as 5 OneFlorida sites did not haveprescribing data, this limits our available data, andgeneralizability, from the entire state of Florida.

Morris et al

Conclusions
In summary, the computable phenotype that we developed to
identify pediatric patients with T1DM is both accurate(PPV=96.8%) and sensitive (95.3%). This computablephenotype will enable future researchers to not only identify apopulation of interest accurately, but also cost-effectively. As
such, this will allow for more precise implementation ofinterventions to help improve both clinical and psychosocial
care, and ultimately improve outcomes important to patients.

Acknowledgments
Research reported in this publication was supported in part by the OneFlorida Clinical Data Network, funded by the Patient-Centered
Outcomes Research Institute (No. CDRN-1501-26692), in part by the OneFlorida Cancer Control Alliance, funded by the Florida
Department of Health’s James and Esther King Biomedical Research Program (No. 4KB16), and in part by the University of
Florida Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing
Translational Sciences under award number UL1TR001427. The content is solely the responsibility of the authors and does notnecessarily represent the official views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors
or Methodology, the OneFlorida Clinical Research Consortium, the University of Florida’s Clinical and Translational Science
Institute, the Florida Department of Health, or the National Institutes of Health.

Conflicts of Interest
None declared.

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Abbreviations
IDR: Integrated Data Repository
PPV: positive predictive value
T1DM: type 1 diabetes mellitus
T2DM: type 2 diabetes mellitus
UF Health: University of Florida Health system

Edited by M Focsa; submitted 25.03.20; peer-reviewed by S Oser, R Bush, C Wu; comments to author 05.05.20; revised version
received 30.06.20; accepted 16.07.20; published 04.09.20
Please cite as:
Morris HL, Donahoo WT, Bruggeman B, Zimmerman C, Hiers P, Zhong VW, Schatz D
An Iterative Process for Identifying Pediatric Patients With Type 1 Diabetes: Retrospective Observational StudyJMIR Med Inform 2020;8(9):e18874
URL: http://medinform.jmir.org/2020/9/e18874/
doi: 10.2196/18874
PMID: 32886067©Heather Lynne Morris, William Troy Donahoo, Brittany Bruggeman, Chelsea Zimmerman, Paul Hiers, Victor W Zhong,Desmond Schatz. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 04.09.2020. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR
Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on
http://medinform.jmir.org/, as well as this copyright and license information must be included.

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