2020
Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment
Durant TJS, Jean RA, Huang C, Coppi A, Schulz WL, Geirsson A, Krumholz HM. Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment. JAMA Network Open 2020, 3: e2028361. PMID: 33284333, DOI: 10.1001/jamanetworkopen.2020.28361.Peer-Reviewed Original ResearchRates and Predictors of Patient Underreporting of Hospitalizations During Follow-Up After Acute Myocardial Infarction
Caraballo C, Khera R, Jones PG, Decker C, Schulz W, Spertus JA, Krumholz HM. Rates and Predictors of Patient Underreporting of Hospitalizations During Follow-Up After Acute Myocardial Infarction. Circulation Cardiovascular Quality And Outcomes 2020, 13: e006231. PMID: 32552061, PMCID: PMC9465954, DOI: 10.1161/circoutcomes.119.006231.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial infarctionHospitalization eventsMedical recordsLongitudinal multicenter cohort studyMulticenter cohort studyMedical record abstractionDifferent patient characteristicsHealth care eventsPatients' underreportingTRIUMPH registryAccuracy of reportingCohort studyPatient characteristicsRecord abstractionProspective studyClinical studiesClinical investigationHospitalizationPatientsCare eventsInfarctionEvent ratesParticipantsPredictors
2019
Tapping Into Underutilized Healthcare Data in Clinical Research
Mori M, Schulz WL, Geirsson A, Krumholz HM. Tapping Into Underutilized Healthcare Data in Clinical Research. Annals Of Surgery 2019, Publish Ahead of Print: &na;. PMID: 30998537, DOI: 10.1097/sla.0000000000003329.Peer-Reviewed Original Research
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