2022
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 failure
2021
Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes
Gevaert AB, Tibebu S, Mamas MA, Ravindra NG, Lee SF, Ahmad T, Ko DT, Januzzi JL, Van Spall HGC. Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes. ESC Heart Failure 2021, 8: 2741-2754. PMID: 33934542, PMCID: PMC8318507, DOI: 10.1002/ehf2.13344.Peer-Reviewed Original ResearchConceptsLeft ventricular ejection fractionChronic obstructive pulmonary diseaseHazard ratioComposite outcomePrimary outcomeAtrial fibrillationHeart diseaseComposite cardiovascular deathEjection fraction categoriesSecondary composite outcomeHeart failure outcomesPrimary composite outcomeObstructive pulmonary diseaseVentricular ejection fractionValvular heart diseaseCoronary heart diseaseGreater prognostic informationDifferent risk categoriesCause deathHF rehospitalizationPredominant comorbiditiesCardiovascular deathHF trialsBaseline characteristicsSecondary outcomes