biostudies-literatureSegar MWNCATS NIH HHSNIA NIH HHSNHLBI NIH HHSNIMHD NIH HHSNCI NIH HHS2370-2383https://www.ebi.ac.uk/biostudies/studies/S-EPMC9976274biostudies-literatureUnknown143(24)<h4>Background</h4>Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races.<h4>Methods</h4>We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baselCirculationDevelopment and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.PMC9976274HHSN268201100011IR01 HL134320R03 AG067960UL1 TR001105HHSN261201800014IHHSN268201800014CN01HC95159HHSN261201800010IHHSN268201100009IHHSN268201800012CR01 HL117323HHSN268201100005CHHSN268201800012IHHSN268201100007CHHSN268201800014IHHSN268201100009CHHSN268201800010IHHSN268201100011CHHSN268201100005IHHSN268201100005GHHSN268201100007IN01HC95160N01HC95161N01HC95162N01HC95163N01HC95164N01HC95165N01HC95169HHSN268201800015IHHSN268201100006CHHSN268201800011CHHSN268201100008CHHSN268201800011IT32 HL125247HHSN268201800013IKL2 TR002490HHSN268201100010CHHSN268201100008IHHSN268201100012CSegar MWRao SLewis AARodriguez CJMentz RJde Lemos JAAyers CBallantyne CMRaffield LMCorrea AChandra APatel KVJaeger BCNambi VNdumele CEHall MEButler JMichos EDPandey AfalseDevelopment and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis.<h4>Background</h4>Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races.<h4>Methods</h4>We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at basel2021-01-01T00:00:00Z2021 Jun2025-04-04T00:35:47.66Z2025-04-04T00:35:47.66ZS-EPMC99762743384559310.1161/circulationaha.120.05313410.1161/CIRCULATIONAHA.120.053134