Evidence map›Paper›PMID 42270100›Full record

ArticleBMJ open2026

Prospective validation of an AI algorithm to identify adult-onset type 1 diabetes misclassification: protocol for a non-interventional multicentre study.

Irene Brusini, Suyin Lee, Alvi Lai, Amanda Sees, Matthew Hackenberg, Greg Liptak, Ajay D Rao, Catherine Anastasopoulou, Nadejda Leavitt

Abstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Irene BrusiniApplied AI Science, IQVIA, London, UK irene.brusini@iqvia.com.ORCID http://orcid.org/0000-0003-3479-4243
Suyin LeeApplied AI Science, IQVIA, Wayne, Pennsylvania, USA.
Alvi LaiApplied AI Science, IQVIA, London, UK.
Amanda SeesApplied AI Science, IQVIA, Wayne, Pennsylvania, USA.
Matthew HackenbergApplied AI Science, IQVIA, Wayne, Pennsylvania, USA.
Greg LiptakHealthShare Exchange (HSX), Philadelphia, Pennsylvania, USA.
Ajay D RaoSection of Endocrinology, Diabetes and Metabolism, Temple University, Philadelphia, Pennsylvania, USA.
Catherine AnastasopoulouEndocrinology Department, Jefferson Einstein Medical Center, Philadelphia, Pennsylvania, USA.
Nadejda LeavittApplied AI Science, IQVIA, Wayne, Pennsylvania, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAdult-onset type 1 diabetes (T1D) is often misclassified as type 2 diabetes (T2D), resulting in delayed treatment, missed opportunities for referrals to specialists and increased risk of complications including diabetic ketoacidosis. An electronic medical record (EMR)-based algorithm-originally trained on a large national EMR dataset to identify likely misclassified adult-onset T1D cases-was tested and retrained on a health information exchange (HIE) dataset from HealthShare Exchange (HSX). Promising results were achieved on historical data, particularly when using the retrained algorithm. However, its prospective validation is essential to more reliably assess its clinical utility and real-world precision in flagging high-risk patients for clinician review. METHODS AND ANALYSIS: This is a prospective, multicentre, non-interventional cohort study in two HSX-member healthcare organisations (HCOs) in southeastern Pennsylvania. At the onset of the study, all adult T2D patients are scored by the algorithm analysing HIE data on relevant predictors found in the 24-month lookback period. Patients meeting a prespecified score threshold estimated in retrospective testing to yield 10% recall will be presented to designated endocrinology or primary care providers for structured chart review, attribution confirmation and guideline-concordant follow-up (including autoantibody testing where appropriate). The primary endpoint is positive predictive value for confirmed adult-onset T1D among flagged patients. Secondary endpoints characterise operational cascade metrics (attribution, provider recommendation, test ordering/results and diagnosis updates) along with 95% CIs. Exploratory endpoints will assess provider adoption, interpretability and workflow integration via structured provider interviews. ETHICS AND DISSEMINATION: This study was reviewed and approved by Advarra Institutional Review Board (protocol Pro00075945). The Institutional Review Board waived patient informed consent and granted a full waiver of HIPAA authorisation for patient records, while providers were required to provide written informed consent. HSX data were accessed and shared under its member-defined use cases. Findings will be disseminated via peer-reviewed publications and conference presentations. Reporting will follow Strengthening the Reporting of Observational Studies in Epidemiology guidance for cohort studies.

Indexed as

AlgorithmsDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2AdultElectronic Health RecordsHumansMulticenter Studies as TopicObservational Studies as TopicProspective StudiesResearch DesignValidation Studies as TopicClinical Decision-MakingDiabetes & endocrinologyHealth informaticsMachine LearningProspective studies

Identifiers

PMID42270100
PMCPMC13264913

What OpenQuestion holds

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LicenceCC BY-NC
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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.