Evidence map›Paper›PMID 41603291›Full record

SynthesisDiabetic medicine : a journal of the British Diabetic Association2026

Prognostic models for all-cause and cardiovascular mortality in type 2 diabetes: Systematic review.

Monica Kundu, Mark P Funnell, Zahra Karimi, Setor K Kunutsor, Sara Naderpour, Arron Peace, Anneka Welford, Francesco Zaccardi, Kamlesh Khunti, Safoora Gharibzadeh and 1 more

Abstract readSystematic ReviewReview
In one paragraph

Synthesis in Diabetic medicine : a journal of the British Diabetic Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Prognostic models for all-cause and cardiovascular mortality in type 2 diabetes: Systematic review.Diabetic medicine : a journal of the British Diabetic Association · 2026
    Pooled it
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

11 authors.

Monica KunduDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.ORCID https://orcid.org/0000-0002-0087-925X
Mark P FunnellDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.
Zahra KarimiLeicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.
Setor K KunutsorLeicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.
Sara NaderpourLeicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.
Arron PeaceDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.
Anneka WelfordDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.
Francesco ZaccardiLeicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.
Kamlesh KhuntiLeicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.ORCID https://orcid.org/0000-0003-2343-7099
Safoora GharibzadehLeicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.
Ash RoutenDiabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThe elevated risk of all-cause and cardiovascular mortality in individuals with type 2 diabetes mellitus (T2DM) has led to growing efforts to develop prognostic models for early identification of high-risk individuals. This systematic review synthesised existing models to inform future model development, enhance predictive performance and guide targeted prevention strategies in diverse clinical and population health settings.

methodsWe systematically searched Ovid MEDLINE, Scopus and Web of Science for studies published between January 1, 2015, and June 11, 2024, reporting prognostic models developed and/or validated to predict all-cause or cardiovascular mortality in individuals T2DM. Data were extracted following the CHARMS checklist, and risk of bias assessed using the PROBAST tool.

resultsThe search yielded 18,126 records; 10,921 were screened after deduplication. Of 147 full texts assessed, 26 cohort studies met inclusion criteria, with sample sizes (median [IQR]: 20,554 [1931-59,180]). Models were developed in diverse regions, with the highest number from Taiwan (n = 5) and the USA (n = 5). Most studies focused on all-cause mortality (n = 26); eight addressed cardiovascular mortality. Prediction horizons varied from 1 to 15 years, with 5-year risk being the most common (n = 10). Discrimination had a median C-statistic of 0.77 (IQR: 0.72-0.81). Calibration was reported in 20 studies, though methods varied. Cox regression was the most common statistical method (n = 16).

conclusionsPrediction models for mortality in T2DM show considerable heterogeneity in methodology, performance and validation. Limited external validation and inconsistent calibration reporting highlight the need for robust, generalisable and transparently reported models to improve clinical risk stratification in diabetes care.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Cause of DeathHumansPrediction AlgorithmsPrognosisRisk AssessmentRisk Factorsmodel developmentmodel validationmortalityrisk prediction modelstype 2 diabetes

Identifiers

PMID41603291
PMCPMC12900499

What OpenQuestion holds

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LicenceCC BY-NC
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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 OpenQuestion graph.