Evidence map›Paper›PMID 41917126›Full record

ArticleScientific reports2026

Development and evaluation of cardiovascular disease risk prediction models for patients with type 2 diabetes.

Yang Yang, Tian Liu, Che-Yi Liao, Sun Ju Lee, Esmaeil Keyvanshokooh, Hui Shao, Mary Beth Weber, Francisco J Pasquel, Gian-Gabriel P Garcia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yang Yang *H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Tian Liu *Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
Che-Yi LiaoH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Sun Ju LeeFreeman College of Management, Bucknell University, Lewisburg, PA, USA.
Esmaeil KeyvanshokoohDepartment of Information and Operations Management, Mays Business School, Texas A&M University, College Station, TX, USA.
Hui ShaoHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Mary Beth WeberHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Francisco J PasquelDivision of Endocrinology, Metabolism, and Lipids, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
Gian-Gabriel P GarciaDepartment of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA. garciagg@uw.edu.

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
Translational Research Core - Engagement and Behavior ChangeP30DK111024 · NIDDK · EMORY UNIVERSITY · PI Mohammed Kumail Ali · 2016 to 2026
$13.4M
Georgia Center for Diabetes and Translation Research NIH/NIDDK P30DK111024NIDDK NIH HHS P30 DK111024NIH HHS 1OT2OD032581NIH HHS OT2 OD032581
6 · The paper itself

Abstract

To facilitate treatment decisions in people at risk of Cardiovascular Disease (CVD), several risk equations such as the Pooled Cohort Equations and Predicting Risk of Cardiovascular Disease Events (PREVENT) equations have been developed to estimate CVD risk for primary prevention patients. However, it is unclear whether these equations achieve high predictive accuracy and fairness in patients with type 2 diabetes (T2D), and whether a T2D-specific risk equation is needed. Accordingly, we developed a Weibull Accelerated Failure Time (AFT) survival model for predicting the 3-year CVD risk in 23,795 patients with T2D from the All of Us dataset, using sociodemographic information, physical measurements, medication, and CVD history. Among patients without CVD history, our Weibull AFT (vs. PREVENT) achieved a greater C-index (0.646 vs. 0.465), greater Concordance Fractions (0.610–0.674 vs. 0.541–0.600), and comparable Concordance Imparity (0.006 vs. 0.002) across sex and race/ethnicity (0.065 vs. 0.058) subgroups. Our findings highlight the need for a T2D-specific CVD risk equation and demonstrate the value of diverse datasets for developing fair and accurate predictive models.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2AgedFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedPrediction AlgorithmsRisk AssessmentRisk FactorsCardiovascular diseasesFairness evaluationRisk equation developmentSurvival modelingType 2 diabetes

Identifiers

PMID41917126
PMCPMC13187328

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.