What is the clinical evidence from this study?

Study design: Cohort. Population: adult-onset diabetes (n=7081). Intervention: EHR-based classification algorithms vs. biomarker-defined diabetes subtypes. Primary outcome: positive predictive value (PPV), negative predictive value (NPV), and ROC AUC (PPV 23-79%, ROC AUC 0.65-0.78).

What does this research mean for the field?

Widely used EHR-based algorithms substantially misclassify adult-onset Type 1 Diabetes, demonstrating poor positive predictive value and modest discrimination when compared to biomarker-defined subtypes. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

To validate EHR-based diabetes classification algorithms against biomarker-defined adult-onset T1D.

June 7, 2026Diabetes

2214-P: Biomarker Validation Reveals Substantial Misclassification of Adult-Onset Type 1 Diabetes by EHR-Based Algorithms: Implications for Research Accuracy and Clinical Care

View Full Paper

Ask AI

Bookmark

Key result

EHR algorithms poorly identify biomarker-defined adult-onset T1D with a maximum PPV of ~79%.

PPV 23-79%, ROC AUC 0.65-0.78
n=7,081

Why the study?

Widely used EHR-based diabetes classification algorithms have not been robustly validated against gold-standard biomarker-defined diabetes subtypes for adult-onset T1D.

Do EHR-based classification algorithms accurately identify biomarker-defined adult-onset T1D in patients with adult-onset diabetes?

Population

7081 adult-onset diabetes cases from the MyCode cohort

Comparison

Eight EHR-based classification algorithms vs biomarker-defined diabetes subtypes

Design

Validation cohort study

Authors

JLJIANG LIDanville Community College RSRICHARD STAHLDanville Community College AGALICIA GOLDENDanville Community College

Discussion

Member takes

Implication

EHR algorithms risk misclassifying adult-onset T1D; leaves open biomarker validation before research or care use.

Key Points

To validate EHR-based diabetes classification algorithms against biomarker-defined adult-onset T1D.
Analyzed 7081 adult-onset diabetes cases from the MyCode cohort, including 3365 insulin-treated and 3152 non-insulin-treated individuals.
Measured random C-peptide and islet autoantibodies to define T1D and T2D.
Evaluated eight EHR-based algorithms for positive predictive value (PPV), negative predictive value (NPV), and ROC AUC.
Identified 3.1% adult-onset T1D and 71% T2D based on biomarkers.
Algorithms demonstrated poor identification of T1D, with PPV ranging from 23-79% and high NPV (96-97%).
The lipid model by Lynam et al. had the highest PPV at 79%, but ROC AUC was modest (0.65-0.78), especially lower for insulin-treated individuals.

Study Design

Type

Cohort (n=7,081)

Structured PICO

Do EHR-based classification algorithms accurately identify biomarker-defined adult-onset T1D in patients with adult-onset diabetes?

Population

7,081 adult-onset diabetes cases (3,365 insulin-treated and 3,152 non-insulin-treated) from the MyCode cohort with a mean age of 59.8 years.

Exposure

Eight widely used EHR-based classification algorithms for adult-onset T1D

Comparator

Biomarker-defined diabetes subtypes (C-peptide and islet autoantibodies)

Outcome

Positive predictive value (PPV), negative predictive value (NPV), and ROC AUC for identifying adult-onset T1D

Main Result

Effect estimate: PPV 23-79%, ROC AUC 0.65-0.78

EHR-based algorithms substantially misclassify adult-onset Type 1 Diabetes compared to biomarker definitions, highlighting the need for biomarker-informed classification in research and clinical care.

Cite This Study

LI et al. (2026) conducted a cohort in adult-onset diabetes (n=7,081). EHR-based classification algorithms vs. biomarker-defined diabetes subtypes was evaluated on positive predictive value (PPV), negative predictive value (NPV), and ROC AUC (PPV 23-79%, ROC AUC 0.65-0.78). EHR-based classification algorithms poorly identified biomarker-defined adult-onset T1D, with PPVs ranging from 23-79% and modest discrimination (ROC AUC 0.65-0.78).

synapsesocial.com/papers/6a250ae37def13d035e1afd2 https://doi.org/10.2337/db26-2214-p

View Full Paper

Ask AI

Bookmark

2214-P: Biomarker Validation Reveals Substantial Misclassification of Adult-Onset Type 1 Diabetes by EHR-Based Algorithms: Implications for Research Accuracy and Clinical Care

Discussion

Member takes

Implication

Key Points

Study Design

Structured PICO

Main Result

Cite This Study

2214-P: Biomarker Validation Reveals Substantial Misclassification of Adult-Onset Type 1 Diabetes by EHR-Based Algorithms: Implications for Research Accuracy and Clinical Care

Discussion

Member takes

Implication

Key Points

Study Design

Structured PICO

Main Result

Cite This Study

Also Consider

Also Consider