Evidence map›Paper›PMID 41613350›Full record

ArticleAmerican journal of preventive cardiology2026

Cardiovascular risk stratification without recalibration: A comparative study of the PREVENT and WHO risk scores in a multiethnic Brazilian cohort.

Aline F Pedroso, Luisa C C Brant, Antonio L P Ribeiro, Sandhi M Barreto, Roberta C Figueiredo, Rohan Khera

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Article in American journal of preventive cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Aline F PedrosoSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Luisa C C BrantDepartment of Internal Medicine, Faculdade de Medicina, and Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Antonio L P RibeiroDepartment of Internal Medicine, Faculdade de Medicina, and Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Sandhi M BarretoUniversidade Federal de Minas Gerais/Medical School and Clinical Hospital, Belo Horizonte, MG, Brazil.
Roberta C FigueiredoUniversidade Federal de Sao Joao del-Rei, Divinopolis, MG, Brazil.
Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Models predicting ASCVD risk often overestimate risk in diverse populations from low- and middle-income countries (LMICs), limiting their clinical utility and efficiency of resource allocation. Methods: We evaluated the performance of the PREVENT score in a large, multiethnic, population-based cohort of adults without baseline ASCVD, followed prospectively for adjudicated cardiovascular events. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was evaluated by predicted-to-observed (P/O) risk ratios. We compared PREVENT with the 2019 WHO cardiovascular risk score, a model specifically recalibrated for use in LMICs, and assessed reclassification using the net reclassification index (NRI), scaling the 10-year risk to 5-year estimates using exponential survival transformation. Additionally, we examined how risk reclassification would affect recommendations for preventive therapy. Results: Among 11,077 participants (age 53.1 ± 8.1 years, 55.3% female), 157 ASCVD events occurred over five years. Discrimination was similar for PREVENT (AUC 0.76, 95 % CI: 0.72-0.80) and WHO (0.75, 95 % CI: 0.71-0.78). PREVENT had better calibration (P/O 1.21 [1.07-1.51] vs. 1.57 [1.31-2.46] for WHO) and improved risk classification (NRI 0.19). This improvement was more pronounced among women (NRI = 0.24) and Black or mixed-race individuals (NRI = 0.28). In adults aged 40-75 with ≥1 cardiovascular risk factor, the PREVENT model appropriately up-classified more individuals who had an event to the group for which there is a recommendation for preventive treatment. Conclusions: PREVENT demonstrated better alignment between predicted and observed ASCVD risk compared with the WHO score in a large, diverse LMIC cohort. Its higher out-of-the-box calibration may enable more accurate risk stratification and efficient resource allocation in LMICs.

Indexed as

ASCVD riskCalibrationLow- and middle-income countriesRecalibrationRisk prediction

Identifiers

PMID41613350
PMCPMC12849047

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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.