2022
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 women
2021
Patient Phenotypes and SGLT-2 Inhibition in Type 2 Diabetes Insights From the EMPA-REG OUTCOME Trial
Sharma A, Ofstad AP, Ahmad T, Zinman B, Zwiener I, Fitchett D, Wanner C, George JT, Hantel S, Desai N, Mentz RJ. Patient Phenotypes and SGLT-2 Inhibition in Type 2 Diabetes Insights From the EMPA-REG OUTCOME Trial. JACC Heart Failure 2021, 9: 568-577. PMID: 34325887, DOI: 10.1016/j.jchf.2021.03.003.Peer-Reviewed Original ResearchConceptsCV deathLatent class analysisTreatment effectsNon-coronary artery diseaseEMPA-REG OUTCOME trialAdvanced coronary diseaseEMPA-REG OUTCOMESGLT-2 inhibitionGlomerular filtration rateType 2 diabetesEmpagliflozin 25EMPA-REGCV diseaseCV riskLower eGFRT2D durationOlder patientsOutcome trialsYounger patientsArtery diseaseCoronary diseaseCox regressionFiltration rateCardiovascular diseaseGroup 2