Evidence map›Paper›PMID 42828276›Full record

ArticleAmerican journal of preventive cardiology2026

Evaluating algorithmic fairness in the performance of the AHA PREVENT equations versus pooled cohort equations among single-race and multiracial populations.

Adrian Matias Bacong, Xiaowei Yan, Qiwen Huang, Hannah Husby, Jiang Li, Pragati Kenkare, Fatima Rodriguez, Latha Palaniappan

Abstract read
In one paragraph

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. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

8 authors.

Adrian Matias BacongStanford University School of Medicine, Palo Alto, CA 94304, USA.
Xiaowei YanSutter Health Research Institute, Sutter Health, 2121 N. California Ave, Suite 310, Walnut Creek, CA 94596, USA.
Qiwen HuangSutter Health Research Institute, Sutter Health, 2121 N. California Ave, Suite 310, Walnut Creek, CA 94596, USA.
Hannah HusbySutter Health Research Institute, Sutter Health, 2121 N. California Ave, Suite 310, Walnut Creek, CA 94596, USA.
Jiang LiSutter Health Research Institute, Sutter Health, 2121 N. California Ave, Suite 310, Walnut Creek, CA 94596, USA.
Pragati KenkareSutter Health Research Institute, Sutter Health, 2121 N. California Ave, Suite 310, Walnut Creek, CA 94596, USA.
Fatima RodriguezStanford University School of Medicine, 453 Quarry Road, Rm 332b, Palo Alto, CA 94304, USA.
Latha PalaniappanStanford University School of Medicine, Palo Alto, CA 94304, USA.

Funding

Opportunistic Atherosclerotic Cardiovascular Disease Risk Estimation at Abdominal CTs with Robust and Unbiased Deep LearningR01HL167974 · NHLBI · STANFORD UNIVERSITY · PI Imon Banerjee, Akshay Chaudhari · 2023 to 2026
$2.4M
Adherence Determinants in the Health Electronic Record Evaluation of Statins (ADHERES)R01HL168188 · NHLBI · STANFORD UNIVERSITY · PI Fatima Rodriguez · 2024 to 2026
$2.1M
SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups)K01HL144607 · NHLBI · STANFORD UNIVERSITY · PI RODRIGUEZ, FATIMA · 2019 to 2023
$856k
META - Mentor, Educate, Train, Advocate: Patient Oriented Researchers in Cardiometabolic DiseaseK24HL150476 · NHLBI · STANFORD UNIVERSITY · PI Latha P Palaniappan · 2020 to 2026
$846k
NHLBI NIH HHS K01 HL144607NHLBI NIH HHS K24 HL150476NHLBI NIH HHS R01 HL167974NHLBI NIH HHS R01 HL168188
6 · The paper itself

Abstract

Background: The performance of AHA PREVENT equation among multiracial individuals has not been assessed. Objective: To assess model performance and fairness of the AHA PREVENT ASCVD (atherosclerotic cardiovascular disease) Equation versus Pooled Cohort Equations (PCE) among single-race and disaggregated multiracial individuals. Method: This is a retrospective cohort data analysis using electronic health record from a 2010-2023 primary care population in a large integrated healthcare system in California. Patient population was restricted to those who are eligible for both the AHA PREVENT base equation and PCE. The primary outcomes were incident ASCVD based on diagnosis codes. We compared measures of model performance as well as algorithmic fairness between PCE and AHA PREVENT, using Disparate Impact (DI), Average Odds Difference (AOD), and Equal Opportunity Difference (EOD). Results: A total of 298,276 patients aged 40-79 years were included, with an average follow-up time of 8 years (SD=3), of which 9% (N=26,761) self-identified as multiracial. In aggregate, 3.9% of multiracial patients developed ASCVD, ranging from 2.9% (Multiple Asian) to 5.4% (Multiracial American Indian/Alaska Native), compared to 4.4% of all single-race patients. Discriminatory performance of the AHA PREVENT ASCVD Equation was good for aggregated multiracial groups (AUC=0.747), ranging from 0.692 (Multiracial Black) to 0.793 (Multiracial Pacific Islander), and was comparable to single-race groups (AUC=0.753). Calibration was generally good among most multiracial groups. However, the AHA PREVENT ASCVD Equation was fairer in its predictive ability when compared to the PCE for all three fairness metrics. Conclusions and relevance: With good discriminatory ability and calibration for single race and multiracial groups, the AHA PREVENT ASCVD Equation provides both more accurate and fairer estimates of ASCVD risk compared to PCE.

Indexed as

AHA PREVENTASCVD preventionDisparityMulti-racePooled cohort equation

Identifiers

PMID42828276
PMCPMC13632069

What Socratic holds

Textmetadata
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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.