2024
Characteristics of High-Performing Hospitals in Cardiogenic Shock Following Acute Myocardial Infarction
Saha A, Li S, de Lemos J, Pandey A, Bhatt D, Fonarow G, Nallamothu B, Wang T, Navar A, Peterson E, Matsouaka R, Bavry A, Das S, Grodin J, Khera R, Drazner M, Kumbhani D, Registry N. Characteristics of High-Performing Hospitals in Cardiogenic Shock Following Acute Myocardial Infarction. The American Journal Of Cardiology 2024, 221: 19-28. PMID: 38583700, DOI: 10.1016/j.amjcard.2024.04.002.Peer-Reviewed Original ResearchAMI-CSNational Cardiovascular Data Registry Chest Pain-MI RegistryAcute myocardial infarctionChest Pain-MI RegistryRisk adjustmentRisk-adjusted multivariate logistic regressionHigh-performing hospitalsHospital-level characteristicsAssociated with lower in-hospital mortalityAssociated with decreased mortalityMultivariate logistic regressionFactors associated with decreased mortalityLower in-hospital mortalityMedian participant ageMyocardial infarctionIn-hospital mortalityCohort databaseParticipant ageOutcomes researchLogistic regressionCardiogenic shockLeft ventricular assist deviceCo-morbiditiesRetrospective cohort databaseTertile
2023
Patterns of Digoxin Prescribing for Medicare Beneficiaries in the United States 2013-2019
See C, Wheelock K, Caraballo C, Khera R, Annapureddy A, Mahajan S, Lu Y, Krumholz H, Murugiah K. Patterns of Digoxin Prescribing for Medicare Beneficiaries in the United States 2013-2019. American Journal Of Medicine Open 2023, 10: 100048. PMID: 38213879, PMCID: PMC10783702, DOI: 10.1016/j.ajmo.2023.100048.Peer-Reviewed Original ResearchDigoxin prescriptionDigoxin useNew heart failure therapiesGeneral medicine physiciansHeart failure therapyMedicare Part D dataPart D dataDigoxin prescribingFailure therapyPrescriber characteristicsMedicine physiciansMedicare beneficiariesPrescribersLikely maleLogistic regressionDigoxinNew prescribersPrescriptionRecent dataCardiology
2021
Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, Rumsfeld JS, Masoudi FA, Huang C, Normand SL, Mortazavi BJ, Krumholz HM. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiology 2021, 6: 633-641. PMID: 33688915, PMCID: PMC7948114, DOI: 10.1001/jamacardio.2021.0122.Peer-Reviewed Original ResearchConceptsMachine learning modelsMeta-classifier modelLearning modelNeural networkGradient descent boostingAcute myocardial infarctionContemporary machineGradient descentXGBoost modelXGBoostHospital mortalityCohort studyLogistic regressionMyocardial infarctionNetworkChest Pain-MI RegistryPrecise classificationIndependent validation dataInitial laboratory valuesNovel methodLarge national registryHigh-risk individualsData analysisValidation dataResolution of risk
2019
Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction
Angraal S, Mortazavi BJ, Gupta A, Khera R, Ahmad T, Desai NR, Jacoby DL, Masoudi FA, Spertus JA, Krumholz HM. Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction. JACC Heart Failure 2019, 8: 12-21. PMID: 31606361, DOI: 10.1016/j.jchf.2019.06.013.Peer-Reviewed Original ResearchConceptsHF hospitalizationRisk of mortalityEjection fractionBlood urea nitrogen levelsLogistic regressionPrevious HF hospitalizationHeart failure hospitalizationReduced ejection fractionReceiver-operating characteristic curveRisk of deathBody mass indexBlood urea nitrogenUrea nitrogen levelsHealth status dataMean c-statisticKCCQ scoresTOPCAT trialFailure hospitalizationHeart failureHemoglobin levelsMass indexC-statisticHospitalizationUrea nitrogenMortality