Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF
Murray EM, Greene SJ, Rao VN, Sun JL, Alhanti BA, Blumer V, Butler J, Ahmad T, Mentz RJ. Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF. American Heart Journal 2022, 254: 112-121. PMID: 36007566, DOI: 10.1016/j.ahj.2022.08.009.Peer-Reviewed Original ResearchMeSH KeywordsAtrial FibrillationClinical Trials as TopicFemaleHeart FailureHumansMachine LearningMaleObesityPrognosisStroke VolumeConceptsASCEND-HF trialAtrial fibrillationBlood pressureEjection fractionHeart failureLatent class analysisOutcomes of patientsLong-term outcomesYoung menFour-hour urine outputDistinct phenotypesAcute HFRenal impairmentClinical profileUrine outputASCEND-HFClinical benefitHeterogenous diseaseClinical dataOlder womenHFpEFPatientsOlder individualsCluster 3Asian womenA Clinical Framework for Evaluating Machine Learning Studies ∗
Ghazi L, Ahmad T, Wilson FP. A Clinical Framework for Evaluating Machine Learning Studies ∗. JACC Heart Failure 2022, 10: 648-650. PMID: 35963817, DOI: 10.1016/j.jchf.2022.07.002.Commentaries, Editorials and Letters