Evidence mapPaperPMID 39801813Full record

ArticleJACC. Advances2025

Cardiovascular Risk Prediction Scores in Type 1 Diabetes: A Systematic Review and Meta-Analysis.

Sebhat Erqou, Ahmed Shahab, Fayez H Fayad, Mohammed Haji, Matthew F Yuyun, Jacob Joseph, Wen-Chih Wu, Amanda I Adler, Trevor J Orchard, Justin B Echouffo-Tcheugui

Abstract read
In one paragraph

Article in JACC. Advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Sebhat ErqouDepartment of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Ahmed ShahabDepartment of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Fayez H FayadDepartment of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Mohammed HajiDepartment of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Matthew F YuyunDepartment of Medicine, VA Boston Healthcare System, Boston, USA.
Jacob JosephDepartment of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Wen-Chih WuDepartment of Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Amanda I AdlerDiabetes Trials Unit, University of Oxford, Oxford, UK.
Trevor J OrchardDepartment of Epidemiology, University of Pittsburgh, School of Public Health, Pittsburgh, Pennsylvania, USA.
Justin B Echouffo-TcheuguiDivision of Diabetes, Department of Medicine, Endocrinology and Metabolism, Johns Hopkins University, Baltimore, Maryland, USA.

Funding

NHLBI NIH HHS K23 HL153774
6 · The paper itself

Abstract

Background: The extent of the performance and utility of scores for the risk of cardiovascular disease (CVD) in persons with type 1 diabetes (T1DM) largely remains unclear. Objective: The purpose of this study was to synthesize data on the performance of CVD risk scores in people living with T1DM. Methods: This study is a systematic review and meta-analysis. PubMed and EMBASE were searched through December 31, 2023. The included studies: 1) were retrospective, prospective, or cross-sectional in design; 2) included persons with T1DM; 3) assessed CVD outcomes; and 4) had data on at least on CVD risk score. Measures of calibration and discrimination qualitatively summarized. Measures of discrimination were combined using random-effects models stratified by type of risk model. Results: In a meta-analysis of observational studies of CVD risk scores in T1DM individuals, including 11 studies and 73,664 participants (mean age of 34 years, mainly White individuals and male [55%]), we evaluated 12 CVD risk prediction models (7 T1DM-specific, 1 type 2 diabetes-specific, and 4 general population models). Most risk scores had a moderate to excellent discrimination (C-statistic: 0.73-0.85) and predicted CVD risk well when compared to actual clinical events. CVD risk scores specifically developed in T1DM individuals exhibited a higher discriminative performance-pooled C-statistic of 0.81 vs 0.75 for risk scores developed in the general population or those with type 2 diabetes and also showed a better calibration. Conclusions: Among individuals with T1DM, CVD risk models had a moderate to excellent discrimination, with a better discrimination and accuracy for T1DM-specific scores.

Indexed as

cardiovascular riskepidemiologyrisk predictionrisk scorestype 1 diabetes

Identifiers

PMID39801813
PMCPMC11719351

What Socratic holds

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Registered trials

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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.