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 ResearchConceptsASCEND-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 womenUnsupervised Machine Learning To Define Acute Hfpef Phenotypes: Findings From Ascend-hf
Murray E, Greene S, Rao V, Sun J, Alhanti B, Blumer V, Butler J, Ahmad T, Hernandez A, Mentz R. Unsupervised Machine Learning To Define Acute Hfpef Phenotypes: Findings From Ascend-hf. Journal Of Cardiac Failure 2022, 28: s10-s11. DOI: 10.1016/j.cardfail.2022.03.029.Peer-Reviewed Original ResearchSystolic blood pressureHFpEF phenotypeGroup patientsBaseline characteristicsBlood pressureHeart rateHigher systolic blood pressureLower systolic blood pressureDistinct baseline characteristicsDistinct patient phenotypesClinical trial cohortNatriuretic peptide concentrationsASCEND-HF trialTime of admissionFuture clinical trialsDistinct clinical outcomesLower heart rateHigher heart rateDistinct phenotypesHigh rateMedical comorbiditiesTrial cohortClinical profileEjection fractionHeart failure