2023
Effect of the New Glomerular Filtration Rate Estimation Equation on Risk Predicting Models for Acute Kidney Injury After Percutaneous Coronary Intervention
Huang C, Murugiah K, Li X, Masoudi F, Messenger J, Williams K, Mortazavi B, Krumholz H. Effect of the New Glomerular Filtration Rate Estimation Equation on Risk Predicting Models for Acute Kidney Injury After Percutaneous Coronary Intervention. Circulation Cardiovascular Interventions 2023, 16: e012831. PMID: 37009734, PMCID: PMC10622038, DOI: 10.1161/circinterventions.122.012831.Peer-Reviewed Original ResearchConceptsAcute kidney injuryPercutaneous coronary interventionGlomerular filtration rate estimation equationsKidney injuryCoronary interventionInjury
2020
Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction in China
Song J, Murugiah K, Hu S, Gao Y, Li X, Krumholz HM, Zheng X, . Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction in China. Heart 2020, 107: 313-318. PMID: 32938773, PMCID: PMC7873426, DOI: 10.1136/heartjnl-2020-317165.Peer-Reviewed Original ResearchRecurrent acute myocardial infarctionAcute myocardial infarctionPrognostic impactMyocardial infarctionAMI eventsHospital percutaneous coronary interventionInitial acute myocardial infarctionTime-dependent Cox regressionGuideline-based medicationsKaplan-Meier methodologyPercutaneous coronary interventionLog-rank testRenal dysfunctionCardiac eventsCoronary interventionDischarge medicationsInitial admissionChina PatientCox regressionMean ageAge 75AMI ratesHeart rateMortality rateMultivariable modellingCharacteristics of cardiac catheterization laboratory directors at the 2017 U.S. News & World Report top 100 U.S. cardiovascular hospitals
Murugiah K, Annapureddy AR, Khera R, Lansky A, Curtis JP. Characteristics of cardiac catheterization laboratory directors at the 2017 U.S. News & World Report top 100 U.S. cardiovascular hospitals. Catheterization And Cardiovascular Interventions 2020, 97: e624-e626. PMID: 32833350, DOI: 10.1002/ccd.29217.Peer-Reviewed Original ResearchConceptsCardiac catheterization laboratoryCardiovascular HospitalFellowship trainingPercutaneous coronary interventionCardiac catheterization laboratory directorsStructural interventionsCoronary interventionMedian agePeripheral interventionsMedical school graduationCardiovascular programCatheterization laboratoryMedicare dataMedicare Provider UtilizationHospitalClinical focusMedian yearsProvider Utilization
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort