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
2020
Geographical affiliation with top 10 NIH-funded academic medical centers and differences between mortality from cardiovascular disease and cancer
Angraal S, Caraballo C, Kahn P, Bhatnagar A, Singh B, Wilson FP, Fiuzat M, O'Connor CM, Allen LA, Desai NR, Mamtani R, Ahmad T. Geographical affiliation with top 10 NIH-funded academic medical centers and differences between mortality from cardiovascular disease and cancer. American Heart Journal 2020, 230: 54-58. PMID: 32950462, PMCID: PMC7734611, DOI: 10.1016/j.ahj.2020.08.014.Peer-Reviewed Original ResearchConceptsCardiovascular mortality ratesMortality rateCardiovascular mortalityCancer mortalityCardiovascular diseaseMedical CenterIndex groupAnnual cardiovascular mortality rateCardiovascular mortality trendsCancer mortality ratesAcademic medical centerBenefit of patientsMortality trendsSociodemographic characteristicsMortalityIncremental benefitComparison groupNIH fundingImplementation scienceNIHHigh rateDiseaseRapid translationAverage declineResearch priorities