2021
Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft
Mori M, Durant TJS, Huang C, Mortazavi BJ, Coppi A, Jean RA, Geirsson A, Schulz WL, Krumholz HM. Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft. Circulation Cardiovascular Quality And Outcomes 2021, 14: e007363. PMID: 34078100, PMCID: PMC8635167, DOI: 10.1161/circoutcomes.120.007363.Peer-Reviewed Original ResearchConceptsCoronary artery bypass graftArtery bypass graftIntraoperative variablesBypass graftLogistic regression modelsOperative mortalityC-statisticCoronary artery bypass graft casesThoracic Surgeons Adult Cardiac Surgery DatabaseAdult Cardiac Surgery DatabaseMean patient ageGood c-statisticCardiac Surgery DatabaseBrier scoreRisk restratificationDynamic risk predictionIntraoperative deathsPostoperative complicationsPostoperative eventsAdverse eventsPatient agePreoperative variablesRegression modelsGraft casesSurgery DatabaseAdministrative Claims Measure for Profiling Hospital Performance Based on 90-Day All-Cause Mortality Following Coronary Artery Bypass Graft Surgery
Mori M, Nasir K, Bao H, Jimenez A, Legore SS, Wang Y, Grady J, Lama SD, Brandi N, Lin Z, Kurlansky P, Geirsson A, Bernheim SM, Krumholz HM, Suter LG. Administrative Claims Measure for Profiling Hospital Performance Based on 90-Day All-Cause Mortality Following Coronary Artery Bypass Graft Surgery. Circulation Cardiovascular Quality And Outcomes 2021, 14: e006644. PMID: 33535776, DOI: 10.1161/circoutcomes.120.006644.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesCoronary artery bypass graft surgeryArtery bypass graft surgeryBypass graft surgeryMortality rateGraft surgeryC-statisticMedicaid ServicesAdministrative Claims MeasureCause mortality ratesMortality measuresUnadjusted mortality ratesProfiling Hospital PerformanceHierarchical logistic regression modelsAlternate payment modelsHospital performanceLogistic regression modelsCABG recoveryPayment modelsCABG surgeryCause mortalityCABG proceduresDays postsurgeryHospital levelSurgery
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 nitrogenMortalityP415330-Day readmission after hospitalization for heart failure in china
Li J, Bai X, Zhang L, Masoudi F, Spertus J, Krumholz H. P415330-Day readmission after hospitalization for heart failure in china. European Heart Journal 2019, 40: ehz745.0725. DOI: 10.1093/eurheartj/ehz745.0725.Peer-Reviewed Original ResearchDays of dischargeHeart failurePatient characteristicsMedian odds ratioOdds ratioChronic obstructive pulmonary diseaseIndex hospital stayObstructive pulmonary diseaseWeeks of dischargeValvular heart diseaseTransitions of careHF hospitalizationRandom hospitalsCause readmissionEligible patientsHospital stayHospitalization stayReadmission diagnosesReadmission ratesConsecutive patientsHospital readmissionMedian agePulmonary diseasePatient factorsC-statisticComparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data
Krumholz HM, Coppi AC, Warner F, Triche EW, Li SX, Mahajan S, Li Y, Bernheim SM, Grady J, Dorsey K, Lin Z, Normand ST. Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data. JAMA Network Open 2019, 2: e197314. PMID: 31314120, PMCID: PMC6647547, DOI: 10.1001/jamanetworkopen.2019.7314.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionICD-9-CM codesMortality risk modelHeart failureHospital admissionC-statisticMAIN OUTCOMEMortality rateRisk-standardized mortality ratesHospital risk-standardized mortality ratesIndex admission diagnosisPatients 65 yearsDays of hospitalizationComparative effectiveness studiesClaims-based dataHospital-level performance measuresMedicare claims dataPatient-level modelsCMS modelRisk-adjustment modelsRisk modelHospital performance measuresAdmission diagnosisNinth RevisionMyocardial infarctionComparison 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 rateSubmissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation
Jackevicius CA, An J, Ko DT, Ross JS, Angraal S, Wallach JD, Koh M, Song J, Krumholz HM. Submissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation. BMJ Open 2019, 9: e025936. PMID: 30904868, PMCID: PMC6475140, DOI: 10.1136/bmjopen-2018-025936.Peer-Reviewed Original ResearchConceptsRisk prediction toolsCross-sectional evaluationClinical risk predictionClinical performanceCardiovascular disease historyClinical risk scoreHigh-risk patientsLow-risk patientsClinical prediction toolRisk predictionEfficacy outcomesC-statisticDisease historyInclusion criteriaIndependent reviewersRisk scoreExternal validationPatientsPrediction toolsEfficacyOutcomesSame outcome
2018
Racial Disparities in Patient Characteristics and Survival After Acute Myocardial Infarction
Graham GN, Jones PG, Chan PS, Arnold SV, Krumholz HM, Spertus JA. Racial Disparities in Patient Characteristics and Survival After Acute Myocardial Infarction. JAMA Network Open 2018, 1: e184240. PMID: 30646346, PMCID: PMC6324589, DOI: 10.1001/jamanetworkopen.2018.4240.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionWhite patientsBlack patientsMortality rate differencesMortality ratePatient characteristicsMyocardial infarctionPropensity scoreAcute Myocardial Infarction Patients' Health Status (TRIUMPH) registrySelf-identified black patientsObserved survival differencesNational Death IndexTime of admissionLower propensity scoreProspective registryClinical characteristicsCohort studyDeath IndexHighest quintileBlack raceC-statisticSurvival differencesWorse outcomesMAIN OUTCOMEPatientsRisk Factors Associated With Major Cardiovascular Events 1 Year After Acute Myocardial Infarction
Wang Y, Li J, Zheng X, Jiang Z, Hu S, Wadhera RK, Bai X, Lu J, Wang Q, Li Y, Wu C, Xing C, Normand SL, Krumholz HM, Jiang L. Risk Factors Associated With Major Cardiovascular Events 1 Year After Acute Myocardial Infarction. JAMA Network Open 2018, 1: e181079-e181079. PMID: 30646102, PMCID: PMC6324290, DOI: 10.1001/jamanetworkopen.2018.1079.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMajor cardiovascular eventsCardiovascular eventsRisk factorsC-statisticMyocardial infarctionAggressive risk factor reductionOne-year event ratesSubsequent major cardiovascular eventsRecurrent acute myocardial infarctionIndex AMI hospitalizationRisk factor reductionHigh-risk patientsProspective cohort studyCoronary heart diseaseLow-risk groupAcute care hospitalsCohort studyCommon comorbiditiesHeart failureMean ageRisk modelHeart diseaseMAIN OUTCOMEHigh risk
2016
Analysis of Machine Learning Techniques for Heart Failure Readmissions
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circulation Cardiovascular Quality And Outcomes 2016, 9: 629-640. PMID: 28263938, PMCID: PMC5459389, DOI: 10.1161/circoutcomes.116.003039.Peer-Reviewed Original Research
2015
Do Non-Clinical Factors Improve Prediction of Readmission Risk? Results From the Tele-HF Study
Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J. Do Non-Clinical Factors Improve Prediction of Readmission Risk? Results From the Tele-HF Study. JACC Heart Failure 2015, 4: 12-20. PMID: 26656140, PMCID: PMC5459404, DOI: 10.1016/j.jchf.2015.07.017.Peer-Reviewed Original ResearchConceptsReadmission ratesPatient-reported informationHeart failureHealth statusReadmission riskC-statisticRisk scorePsychosocial variablesMedical record abstractionWeeks of dischargeReadmission risk modelNon-clinical factorsCandidate risk factorsReadmission risk predictionRecord abstractionClinical variablesPatient interviewsMedical recordsRisk factorsPatientsPsychosocial informationPsychosocial characteristicsTelephone interviewsRisk predictionScores
2010
Use of Administrative Claims Models to Assess 30-Day Mortality Among Veterans Health Administration Hospitals
Ross JS, Maynard C, Krumholz HM, Sun H, Rumsfeld JS, Normand SL, Wang Y, Fihn SD. Use of Administrative Claims Models to Assess 30-Day Mortality Among Veterans Health Administration Hospitals. Medical Care 2010, 48: 652-658. PMID: 20548253, PMCID: PMC3020977, DOI: 10.1097/mlr.0b013e3181dbe35d.Peer-Reviewed Original ResearchConceptsStatistical modelAcute myocardial infarctionVeterans Health Administration hospitalsVHA hospitalsHeart failurePneumonia hospitalizationsC-statisticNon-federal hospitalsMedian numberModest heterogeneityAdministration HospitalAdministrative claims dataService Medicare beneficiariesYears of age
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 Forum
2005
Monitoring clinical changes in patients with heart failure: A comparison of methods
Spertus J, Peterson E, Conard MW, Heidenreich PA, Krumholz HM, Jones P, McCullough PA, Pina I, Tooley J, Weintraub WS, Rumsfeld JS, Consortium F. Monitoring clinical changes in patients with heart failure: A comparison of methods. American Heart Journal 2005, 150: 707-715. PMID: 16209970, DOI: 10.1016/j.ahj.2004.12.010.Peer-Reviewed Original ResearchConceptsClinical changesHeart failureWalk testNew York Heart AssociationHeart failure measuresHealth status instrumentsHighest c-statisticClinical deteriorationWalk distanceClinical statusPatient weightC-statisticHeart AssociationExercise testKCCQIndividual patientsNYHAPatientsDisease statusFunctional classClinical medicineLarge improvementStatusDeteriorationOutpatients