Unsupervised 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 failureHeart Failure Spending Function: An Investment Framework for Sequencing and Intensification of Guideline-Directed Medical Therapies
Allen LA, Teerlink JR, Gottlieb SS, Ahmad T, Lam CSP, Psotka MA. Heart Failure Spending Function: An Investment Framework for Sequencing and Intensification of Guideline-Directed Medical Therapies. Circulation Heart Failure 2022, 15: e008594. PMID: 35000432, DOI: 10.1161/circheartfailure.121.008594.Peer-Reviewed Original ResearchConceptsGuideline-directed medical therapyMedical therapyReduced ejection fractionHealth care delivery researchAdverse eventsCardiac benefitsSerum creatinineBlood pressureEjection fractionHeart failureRoutine carePatient burdenHeart rateNew therapiesPatient harmHigh dosesPatientsPsychosocial domainsPocket costsTherapeutic opportunitiesClinical useTherapyNew drugsPhysiological domainsSpending function