2024
Illusory generalizability of clinical prediction models
Chekroud A, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, Kambeitz J, Corlett P, Koutsouleris N, Krumholz H, Krystal J, Paulus M. Illusory generalizability of clinical prediction models. Science 2024, 383: 164-167. PMID: 38207039, DOI: 10.1126/science.adg8538.Peer-Reviewed Original Research
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 ResearchSurgeons: Buyer beware—does “universal” risk prediction model apply to patients universally?
Mori M, Shahian DM, Huang C, Li SX, Normand ST, Geirsson A, Krumholz HM. Surgeons: Buyer beware—does “universal” risk prediction model apply to patients universally? Journal Of Thoracic And Cardiovascular Surgery 2020, 160: 176-179.e2. PMID: 32241616, DOI: 10.1016/j.jtcvs.2019.11.144.Peer-Reviewed Original Research
2019
Development and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention
Huang C, Li SX, Mahajan S, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Development and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention. JAMA Network Open 2019, 2: e1916021. PMID: 31755952, PMCID: PMC6902830, DOI: 10.1001/jamanetworkopen.2019.16021.Peer-Reviewed Original ResearchConceptsCreatinine level increaseAcute kidney injuryPercutaneous coronary interventionContrast volumeAKI riskKidney injuryCoronary interventionBaseline riskCardiology National Cardiovascular Data Registry's CathPCI RegistryNational Cardiovascular Data Registry CathPCI RegistryRisk of AKIAcute Kidney Injury AssociatedDifferent baseline risksPCI safetyCathPCI RegistryInjury AssociatedMean ageDerivation setPreprocedural riskMAIN OUTCOMEAmerican CollegePrognostic studiesUS hospitalsCalibration slopeValidation setPulmonary Embolism Hospitalization, Readmission, and Mortality Rates in US Older Adults, 1999-2015
Bikdeli B, Wang Y, Jimenez D, Parikh SA, Monreal M, Goldhaber SZ, Krumholz HM. Pulmonary Embolism Hospitalization, Readmission, and Mortality Rates in US Older Adults, 1999-2015. JAMA 2019, 322: 574-576. PMID: 31408124, PMCID: PMC6692667, DOI: 10.1001/jama.2019.8594.Peer-Reviewed Original ResearchMeSH KeywordsAgedFemaleHospitalizationHumansLength of StayMaleModels, StatisticalPatient ReadmissionPulmonary EmbolismUnited StatesComparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention
Mortazavi BJ, Bucholz EM, Desai NR, Huang C, Curtis JP, Masoudi FA, Shaw RE, Negahban SN, Krumholz HM. Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention. JAMA Network Open 2019, 2: e196835. PMID: 31290991, PMCID: PMC6624806, DOI: 10.1001/jamanetworkopen.2019.6835.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionMajor bleedingC-statisticCoronary interventionMAIN OUTCOMEIndex percutaneous coronary interventionSubsequent coronary artery bypassPercutaneous coronary intervention (PCI) proceduresHospital major bleedingMajor bleeding ratesNationwide clinical registryCoronary artery bypassCoronary intervention proceduresComparative effectiveness studiesRisk score modelComplexity of presentationMean c-statisticCoronary angiography dataRegistry modelNCDR modelsArtery bypassBleeding eventsPrediction of riskClinical variablesBleeding rate
2017
Discovery of temporal and disease association patterns in condition-specific hospital utilization rates
Haimovich JS, Venkatesh AK, Shojaee A, Coppi A, Warner F, Li SX, Krumholz HM. Discovery of temporal and disease association patterns in condition-specific hospital utilization rates. PLOS ONE 2017, 12: e0172049. PMID: 28355219, PMCID: PMC5371293, DOI: 10.1371/journal.pone.0172049.Peer-Reviewed Original ResearchPrediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures
Mortazavi B, Desai N, Zhang J, Coppi A, Warner F, Krumholz H, Negahban S. Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. IEEE Journal Of Biomedical And Health Informatics 2017, 21: 1719-1729. PMID: 28287993, DOI: 10.1109/jbhi.2017.2675340.Peer-Reviewed Original ResearchMeSH KeywordsCardiac Surgical ProceduresElectronic Health RecordsHumansMachine LearningModels, StatisticalPostoperative ComplicationsConceptsMajor cardiovascular proceduresElectronic health recordsRespiratory failureAdverse eventsCardiovascular proceduresYale-New Haven HospitalPostoperative respiratory failurePatient cohortHospital costsPatient outcomesSpecific patientPatientsHealth recordsCohort-specific modelsCharacteristic curveInfectionFailureHospitalCohortClinicians
2015
Development of a Hospital Outcome Measure Intended for Use With Electronic Health Records
McNamara RL, Wang Y, Partovian C, Montague J, Mody P, Eddy E, Krumholz HM, Bernheim SM. Development of a Hospital Outcome Measure Intended for Use With Electronic Health Records. Medical Care 2015, 53: 818-826. PMID: 26225445, DOI: 10.1097/mlr.0000000000000402.Peer-Reviewed Original ResearchConceptsElectronic health recordsOutcome measuresClinical dataMortality rateClinical practiceFuture quality improvement measuresRisk-standardized mortality ratesHospital risk-standardized mortality ratesLow-mortality hospitalsHealth recordsSystolic blood pressureOdds of mortalityClinical registry dataAcute myocardial infarctionHigh-mortality hospitalsHospital outcome measuresEHR dataFinal risk modelCurrent clinical practiceStandard clinical practiceFirst outcome measureNational Quality ForumCurrent electronic health recordsQuality improvement measuresChart abstraction
2011
An Administrative Claims Model for Profiling Hospital 30-Day Mortality Rates for Pneumonia Patients
Bratzler DW, Normand SL, Wang Y, O'Donnell WJ, Metersky M, Han LF, Rapp MT, Krumholz HM. An Administrative Claims Model for Profiling Hospital 30-Day Mortality Rates for Pneumonia Patients. PLOS ONE 2011, 6: e17401. PMID: 21532758, PMCID: PMC3075250, DOI: 10.1371/journal.pone.0017401.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesHospital MortalityHumansMedicareModels, StatisticalPneumoniaRetrospective StudiesUnited StatesConceptsMortality rateDerivation cohortValidation cohortModel derivation cohortAge 66 yearsPrincipal discharge diagnosisAdministrative diagnosis codesStandardized mortality rateRisk-adjustment variablesQuality of careState mortality ratesAdministrative Claims ModelClaims-based modelsIndex hospitalizationPatient demographicsDischarge diagnosisOutpatient encountersPneumonia mortalityPneumonia patientsRetrospective studyDiagnosis codesPneumonia casesMortality estimatesOutcome measuresProfiling HospitalsAn Administrative Claims Measure Suitable for Profiling Hospital Performance Based on 30-Day All-Cause Readmission Rates Among Patients With Acute Myocardial Infarction
Krumholz HM, Lin Z, Drye EE, Desai MM, Han LF, Rapp MT, Mattera JA, Normand SL. An Administrative Claims Measure Suitable for Profiling Hospital Performance Based on 30-Day All-Cause Readmission Rates Among Patients With Acute Myocardial Infarction. Circulation Cardiovascular Quality And Outcomes 2011, 4: 243-252. PMID: 21406673, PMCID: PMC3350811, DOI: 10.1161/circoutcomes.110.957498.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overCohort StudiesFemaleHumansInsurance Claim ReviewLogistic ModelsMaleMedicareModels, StatisticalMyocardial InfarctionOutcome and Process Assessment, Health CareOutcome Assessment, Health CarePatient ReadmissionQuality of Health CareReproducibility of ResultsRisk FactorsTime FactorsUnited States
2008
An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure
Keenan PS, Normand SL, Lin Z, Drye EE, Bhat KR, Ross JS, Schuur JD, Stauffer BD, Bernheim SM, Epstein AJ, Wang Y, Herrin J, Chen J, Federer JJ, Mattera JA, Wang Y, Krumholz HM. An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure. Circulation Cardiovascular Quality And Outcomes 2008, 1: 29-37. PMID: 20031785, DOI: 10.1161/circoutcomes.108.802686.Peer-Reviewed Original ResearchConceptsRisk-standardized readmission ratesCause readmission rateReadmission ratesHeart failureHospital-level readmission ratesAdjusted readmission ratesAdministrative Claims MeasureUnadjusted readmission ratesHeart failure patientsHospital risk-standardized readmission ratesMedical record dataProfiling Hospital PerformanceHierarchical logistic regression modelsUse of MedicareMedical record modelNational Quality ForumLogistic regression modelsCause readmissionClaims-based modelsHospital dischargeFailure patientsC-statisticPreventable eventsPatientsQuality ForumStatistical Models and Patient Predictors of Readmission for Heart Failure: A Systematic Review
Ross JS, Mulvey GK, Stauffer B, Patlolla V, Bernheim SM, Keenan PS, Krumholz HM. Statistical Models and Patient Predictors of Readmission for Heart Failure: A Systematic Review. JAMA Internal Medicine 2008, 168: 1371-1386. PMID: 18625917, DOI: 10.1001/archinte.168.13.1371.Peer-Reviewed Original ResearchMeSH KeywordsDisease ProgressionEvidence-Based MedicineFemaleHeart FailureHospitalizationHumansIncidenceMaleModels, StatisticalPatient ReadmissionPredictive Value of TestsProportional Hazards ModelsQuality-Adjusted Life YearsRisk FactorsSensitivity and SpecificitySeverity of Illness IndexSurvival AnalysisUnited StatesConceptsPatient characteristicsPatient readmission riskReadmission riskPatient riskSystematic reviewReadmission ratesHospital ratesOvid Evidence-Based Medicine ReviewsEligible English-language publicationsEvidence-Based Medicine ReviewsHeart failure hospitalizationPatient risk stratificationEnglish-language literatureEnglish-language publicationsFailure hospitalizationHF hospitalizationAdult patientsHeart failureHospital readmissionMedicine ReviewsRisk stratificationPatient predictorsInclusion criteriaReadmissionCombined outcome
2006
Prediction of medical morbidity and mortality after acute myocardial infarction in patients at increased psychosocial risk in the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) study
Jaffe AS, Krumholz HM, Catellier DJ, Freedland KE, Bittner V, Blumenthal JA, Calvin JE, Norman J, Sequeira R, O'Connor C, Rich MW, Sheps D, Wu C, Investigators F. Prediction of medical morbidity and mortality after acute myocardial infarction in patients at increased psychosocial risk in the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) study. American Heart Journal 2006, 152: 126-135. PMID: 16824842, DOI: 10.1016/j.ahj.2005.10.004.Peer-Reviewed Original ResearchConceptsPost-MI patientsAcute myocardial infarctionMyocardial infarctionLow social supportCardiovascular mortalityEnd pointNonfatal myocardial infarctionPrimary end pointSecondary end pointsLong-term mortalityPrior myocardial infarctionSignificant multivariable predictorsProportional hazards modelSocial supportKillip classCause mortalityElevated creatinineRecurrent infarctionAdverse eventsBaseline characteristicsBypass surgeryEjection fractionHeart failureMedical morbidityMedical predictorsAn Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With an Acute Myocardial Infarction
Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, Ingber MJ, Roman S, Normand SL. An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With an Acute Myocardial Infarction. Circulation 2006, 113: 1683-1692. PMID: 16549637, DOI: 10.1161/circulationaha.105.611186.Peer-Reviewed Original ResearchAn Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With Heart Failure
Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, Ingber MJ, Roman S, Normand SL. An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With Heart Failure. Circulation 2006, 113: 1693-1701. PMID: 16549636, DOI: 10.1161/circulationaha.105.611194.Peer-Reviewed Original Research
2005
Standards for Statistical Models Used for Public Reporting of Health Outcomes
Krumholz HM, Brindis RG, Brush JE, Cohen DJ, Epstein AJ, Furie K, Howard G, Peterson ED, Rathore SS, Smith SC, Spertus JA, Wang Y, Normand SL. Standards for Statistical Models Used for Public Reporting of Health Outcomes. Circulation 2005, 113: 456-462. PMID: 16365198, DOI: 10.1161/circulationaha.105.170769.Peer-Reviewed Original Research
1999
Comparing AMI Mortality Among Hospitals in Patients 65 Years of Age and Older
Krumholz H, Chen J, Wang Y, Radford M, Chen Y, Marciniak T. Comparing AMI Mortality Among Hospitals in Patients 65 Years of Age and Older. Circulation 1999, 99: 2986-2992. PMID: 10368115, DOI: 10.1161/01.cir.99.23.2986.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial infarctionWhite blood cell countPatients 65 yearsSystolic blood pressureCongestive heart failureMedical chart reviewReceiver-operating characteristic curveBlood cell countRisk-adjusted outcomesYears of ageAdministrative billing codesRisk-adjustment modelsHospital outcomesSerum creatinineChart reviewDerivation cohortHeart failurePatient characteristicsBlood pressureCardiac arrestValidation cohortCandidate predictor variablesAMI mortalityBilling codesMathematical Models and the Assessment of Performance in Cardiology
Krumholz H. Mathematical Models and the Assessment of Performance in Cardiology. Circulation 1999, 99: 2067-2069. PMID: 10217642, DOI: 10.1161/01.cir.99.16.2067.Peer-Reviewed Original Research