Evidence map›Paper›PMID 39344840›Full record

ArticleDiabetes, obesity & metabolism2025

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.

Piaopiao Li, Eliot Spector, Khalid Alkhuzam, Rahul Patel, William T Donahoo, Sarah Bost, Tianchen Lyu, Yonghui Wu, William Hogan, Mattia Prosperi and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Piaopiao LiDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.ORCID 0000-0003-0941-5563
Eliot SpectorDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Khalid AlkhuzamDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Rahul PatelDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
William T DonahooDivision of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida, Gainesville, Florida, USA.
Sarah BostDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.ORCID 0000-0001-8553-9539
Tianchen LyuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Yonghui WuDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
William HoganDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Mattia ProsperiDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Brian E DixonDepartment of Epidemiology, Indiana University (IU) Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA.
Dana DabeleaLifecourse Epidemiology of Adiposity and Diabetes Centre, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Levon H UtidjianDivision of General Paediatrics and Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Tessa L CrumeDepartment of Epidemiology, LEAD Centre, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Lorna ThorpeDepartment of Population Health, NYU Langone Health, New York, New York, USA.
Angela D LieseDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
Desmond A SchatzDepartment of Paediatrics, University of Florida College of Medicine, Gainesville, Florida, USA.
Mark A AtkinsonDiabetes Institute, University of Florida, Gainesville, Florida, USA.
Michael J HallerDepartment of Paediatrics, University of Florida College of Medicine, Gainesville, Florida, USA.
Elizabeth A ShenkmanDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Hui ShaoDepartment of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.ORCID 0000-0002-4088-546X

Funding

Translational Research Core - Engagement and Behavior ChangeP30DK111024 · NIDDK · EMORY UNIVERSITY · PI Mohammed Kumail Ali · 2016 to 2026
$13.4M
Using Real-world Data to Assess the Burden of Diabetes in Children and Adolescents in FloridaU18DP006512 · DP · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2024
$1.4M
CDC HHSNCCDPHP CDC HHS U18 DP006512NIDDK NIH HHS P30 DK111024NIDDK NIH HHS U18DP006512
6 · The paper itself

Abstract

aimTo develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System. MATERIALS AND

methodsThe CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type.

resultsPresumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from Step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D.

conclusionWe developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.

Indexed as

AlgorithmsDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Electronic Health RecordsAdolescentBlood GlucoseChildChild, PreschoolFemaleGlycated HemoglobinHumansInfantMalePhenotypeBlood GlucoseGlycated Hemoglobindatabase researchreal‐world evidencetype 1 diabetestype 2 diabetes

Identifiers

PMID39344840
PMCPMC11620941

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.