2021
Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models
Triche EW, Xin X, Stackland S, Purvis D, Harris A, Yu H, Grady JN, Li SX, Bernheim SM, Krumholz HM, Poyer J, Dorsey K. Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models. JAMA Network Open 2021, 4: e218512. PMID: 33978722, PMCID: PMC8116982, DOI: 10.1001/jamanetworkopen.2021.8512.Peer-Reviewed Original ResearchConceptsPOA indicatorRisk factorsOutcome measuresQuality outcome measuresRisk-adjustment modelsClaims dataAdmission indicatorsPatient risk factorsAcute myocardial infarctionPatient-level outcomesAdministrative claims dataQuality improvement studyClaims-based measuresComparative effectiveness studiesPatient claims dataInternational Statistical ClassificationMortality outcome measuresRelated Health ProblemsHospital quality measuresRisk model performanceHospital stayIndex admissionCare algorithmHeart failureMortality outcomes
2019
Comparative 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 rate
2017
Hospital-Readmission Risk — Isolating Hospital Effects from Patient Effects
Krumholz HM, Wang K, Lin Z, Dharmarajan K, Horwitz LI, Ross JS, Drye EE, Bernheim SM, Normand ST. Hospital-Readmission Risk — Isolating Hospital Effects from Patient Effects. New England Journal Of Medicine 2017, 377: 1055-1064. PMID: 28902587, PMCID: PMC5671772, DOI: 10.1056/nejmsa1702321.Peer-Reviewed Original ResearchConceptsRisk-standardized readmission ratesReadmission ratesObserved readmission ratesSimilar diagnosesHospital effectsDifferent hospitalsHospital readmission performanceRate of readmissionHospital readmission ratesLower readmission ratesStudy sampleYears of ageSignificant differencesMultiple admissionsReadmission outcomesOnly significant differencePatient effectsSame patientMedicare recipientsPatientsReadmission performanceRisk-standardized hospital readmission ratesHospitalHospital qualityQuartileReductions in Readmission Rates Are Associated With Modest Improvements in Patient-reported Health Gains Following Hip and Knee Replacement in England
Friebel R, Dharmarajan K, Krumholz HM, Steventon A. Reductions in Readmission Rates Are Associated With Modest Improvements in Patient-reported Health Gains Following Hip and Knee Replacement in England. Medical Care 2017, 55: 834-840. PMID: 28742545, PMCID: PMC5555974, DOI: 10.1097/mlr.0000000000000779.Peer-Reviewed Original ResearchConceptsRisk-adjusted readmission ratesReadmission ratesEQ-VASHealth gainsEQ-5DKnee replacementHip replacementOxford Hip ScoreOxford Knee ScorePatient-reported healthPatient-reported outcomesVisual analog scaleKnee replacement surgeryReadmission reduction initiativesHealth care systemAdditional health gainsHip scoreKnee scoreAnalog scalePresurgical assessmentReplacement surgeryPatients' senseHospital groupModest ImprovementPatient healthAssociation of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge
Dharmarajan K, Wang Y, Lin Z, Normand ST, Ross JS, Horwitz LI, Desai NR, Suter LG, Drye EE, Bernheim SM, Krumholz HM. Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge. JAMA 2017, 318: 270-278. PMID: 28719692, PMCID: PMC5817448, DOI: 10.1001/jama.2017.8444.Peer-Reviewed Original ResearchConceptsRisk-adjusted readmission ratesRisk-adjusted mortality ratesAcute myocardial infarctionHeart failureReadmission ratesMortality rateMyocardial infarctionMedicare feeService beneficiariesHospital readmission ratesMean hospitalHospital mortalityPostdischarge mortalityHospital dischargeHospital readmissionRetrospective studyAffordable Care ActReadmission reductionMAIN OUTCOMEPneumoniaHospitalSecondary analysisWeighted Pearson correlation coefficientMortalityCare Act
2016
Risk-standardized Acute Admission Rates Among Patients With Diabetes and Heart Failure as a Measure of Quality of Accountable Care Organizations
Spatz ES, Lipska KJ, Dai Y, Bao H, Lin Z, Parzynski CS, Altaf FK, Joyce EK, Montague JA, Ross JS, Bernheim SM, Krumholz HM, Drye EE. Risk-standardized Acute Admission Rates Among Patients With Diabetes and Heart Failure as a Measure of Quality of Accountable Care Organizations. Medical Care 2016, 54: 528-537. PMID: 26918404, PMCID: PMC5356461, DOI: 10.1097/mlr.0000000000000518.Peer-Reviewed Original ResearchConceptsHeart failure measuresAccountable care organizationsAcute admission ratesHeart failureAdmission ratesNational ratesUnplanned hospital admissionsHeart failure cohortRisk-adjustment variablesPopulation-based measuresCare organizationsOutcome measure developmentIntraclass correlation coefficientHospital admissionDiabetes measuresFailure cohortChronic conditionsMedicare feeDiabetesService beneficiariesPatientsMeet criteriaMeasures of qualitySocioeconomic statusPerformance categories
2015
Differences in Colonoscopy Quality Among Facilities: Development of a Post-Colonoscopy Risk-Standardized Rate of Unplanned Hospital Visits
Ranasinghe I, Parzynski CS, Searfoss R, Montague J, Lin Z, Allen J, Vender R, Bhat K, Ross JS, Bernheim S, Krumholz HM, Drye EE. Differences in Colonoscopy Quality Among Facilities: Development of a Post-Colonoscopy Risk-Standardized Rate of Unplanned Hospital Visits. Gastroenterology 2015, 150: 103-113. PMID: 26404952, DOI: 10.1053/j.gastro.2015.09.009.Peer-Reviewed Original ResearchConceptsUnplanned hospital visitsDay of colonoscopyHospital visitsOutpatient facilitiesColonoscopy qualityHealthcare costsRisk-standardized ratesHospital outpatient departmentsUtilization Project dataAmbulatory surgery centersLogistic regression modelsHierarchical logistic regressionQuality improvement effortsPrior arrhythmiaAbdominal painElectrolyte imbalanceOutpatient departmentSurgery centersCommon causeHospital careOutcome measuresPsychiatric disordersColonoscopyUtilization ProjectPatient choiceAn Administrative Claims Measure of Payments Made for Medicare Patients for a 30-Day Episode of Care for Acute Myocardial Infarction
Kim N, Bernheim SM, Ott LS, Han L, Spivack SB, Xu X, Volpe M, Liu A, Krumholz HM. An Administrative Claims Measure of Payments Made for Medicare Patients for a 30-Day Episode of Care for Acute Myocardial Infarction. Medical Care 2015, 53: 542-549. PMID: 25970575, DOI: 10.1097/mlr.0000000000000361.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial infarctionICD-9 codes 410Administrative Claims MeasurePatients 65 yearsDate of admissionClaims-based measuresIntraclass correlation coefficient scoreIntraclass correlation coefficientValue of careHigh-value careClinical variablesDischarge diagnosisMedicare patientsMedicare claimsClinical careAMI episodeAMI hospitalizationCare costsCode 410HospitalizationMedicaid ServicesHospitalClaims measuresCare
2014
Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission.
Horwitz LI, Partovian C, Lin Z, Grady JN, Herrin J, Conover M, Montague J, Dillaway C, Bartczak K, Suter LG, Ross JS, Bernheim SM, Krumholz HM, Drye EE. Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission. Annals Of Internal Medicine 2014, 161: s66-75. PMID: 25402406, PMCID: PMC4235629, DOI: 10.7326/m13-3000.Peer-Reviewed Original ResearchConceptsUnplanned readmissionReadmission measuresReadmission ratesReadmission riskMedicare feeHospital-wide readmission measureRisk-standardized readmission ratesPayer dataAdministrative Claims MeasureRisk-standardized ratesAverage-risk patientsUnplanned readmission rateDays of dischargeHospital risk-standardized readmission ratesAdult hospitalizationsComorbid conditionsPrincipal diagnosisClaims dataService claimsService beneficiariesReadmissionMeasure development studiesMedicaid ServicesRisk adjustmentHospitalRisk Adjustment of Ischemic Stroke Outcomes for Comparing Hospital Performance
Katzan IL, Spertus J, Bettger JP, Bravata DM, Reeves MJ, Smith EE, Bushnell C, Higashida RT, Hinchey JA, Holloway RG, Howard G, King RB, Krumholz HM, Lutz BJ, Yeh RW. Risk Adjustment of Ischemic Stroke Outcomes for Comparing Hospital Performance. Stroke 2014, 45: 918-944. PMID: 24457296, DOI: 10.1161/01.str.0000441948.35804.77.Peer-Reviewed Original ResearchMeSH KeywordsAmerican Heart AssociationBrain IschemiaHospitalsHumansModels, OrganizationalOutcome Assessment, Health CarePatient ReadmissionPredictive Value of TestsPrognosisQuality of Health CareRecovery of FunctionReproducibility of ResultsRisk AdjustmentSample SizeStrokeTreatment OutcomeUnited StatesConceptsIschemic stroke outcomeRisk-adjustment modelsStroke severityStroke outcomeStroke careOutcome measuresHospital levelRisk-adjusted outcome comparisonsRisk adjustmentHospital-level outcomesHospital performanceVascular risk factorsImportant prognostic factorIschemic stroke careIndividual patient levelStroke severity measuresRisk-adjusted modelsHospital-level performanceQuality of strokeComparison of qualityIschemic strokePrognostic factorsComorbid conditionsFunctional outcomeMajor disability
2013
Relationship Between Hospital Readmission and Mortality Rates for Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia
Krumholz HM, Lin Z, Keenan PS, Chen J, Ross JS, Drye EE, Bernheim SM, Wang Y, Bradley EH, Han LF, Normand SL. Relationship Between Hospital Readmission and Mortality Rates for Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia. JAMA 2013, 309: 587-593. PMID: 23403683, PMCID: PMC3621028, DOI: 10.1001/jama.2013.333.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesAcute myocardial infarctionRisk-standardized readmission ratesHospital risk-standardized mortality ratesHeart failureMyocardial infarctionHospital characteristicsMortality rateReadmission ratesProportion of hospitalsHospital readmissionMedicare feePneumoniaInfarctionService beneficiariesHospitalPatientsMedicaid ServicesHospital performanceSubgroupsFailureCauseReadmissionSignificant negative linear relationship
2012
Trends in Survival after In-Hospital Cardiac Arrest
Girotra S, Nallamothu BK, Spertus JA, Li Y, Krumholz HM, Chan PS. Trends in Survival after In-Hospital Cardiac Arrest. New England Journal Of Medicine 2012, 367: 1912-1920. PMID: 23150959, PMCID: PMC3517894, DOI: 10.1056/nejmoa1109148.Peer-Reviewed Original ResearchConceptsHospital cardiac arrestPulseless electrical activityCardiac arrestRisk-adjusted ratesNeurologic disabilityIn-Hospital Cardiac ArrestAcute resuscitation survivalGuidelines-Resuscitation registrySignificant neurologic disabilityQuality improvement registryPulseless ventricular tachycardiaElectrical activityInitial rhythmNeurologic outcomePostresuscitation careAcute resuscitationNeurologic functionSurvival improvementPostresuscitation survivalResuscitation careVentricular tachycardiaRhythm groupVentricular fibrillationMultivariable regressionSurvivalCorrelations among risk‐standardized mortality rates and among risk‐standardized readmission rates within hospitals
Horwitz LI, Wang Y, Desai MM, Curry LA, Bradley EH, Drye EE, Krumholz HM. Correlations among risk‐standardized mortality rates and among risk‐standardized readmission rates within hospitals. Journal Of Hospital Medicine 2012, 7: 690-696. PMID: 22865546, PMCID: PMC3535010, DOI: 10.1002/jhm.1965.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesAcute myocardial infarctionRisk-standardized readmission ratesReadmission ratesRisk-standardized mortalityHeart failureMortality rateReadmission measuresUS hospitalsMortality measuresCross-sectional studyMortality cohortReadmission cohortHospital outcomesSame hospitalMyocardial infarctionMedicare patientsMedicare feeService beneficiariesTeaching hospital membersHospitalSame quartileHospital membersPneumoniaCohort
2011
Patient-Centered Medicine
Krumholz HM. Patient-Centered Medicine. Circulation Cardiovascular Quality And Outcomes 2011, 4: 374-375. PMID: 21772000, DOI: 10.1161/circoutcomes.111.962217.Peer-Reviewed Original Research
2010
Hospital Volume and 30-Day Mortality for Three Common Medical Conditions
Ross JS, Normand SL, Wang Y, Ko DT, Chen J, Drye EE, Keenan PS, Lichtman JH, Bueno H, Schreiner GC, Krumholz HM. Hospital Volume and 30-Day Mortality for Three Common Medical Conditions. New England Journal Of Medicine 2010, 362: 1110-1118. PMID: 20335587, PMCID: PMC2880468, DOI: 10.1056/nejmsa0907130.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionHospital volumeHeart failureMyocardial infarctionVolume thresholdRisk factorsAnnual hospital volumeHigh-volume hospitalsPatient risk factorsOdds of deathCommon medical conditionsAcute care hospitalsMedicare administrative claimsHierarchical logistic regression modelsCross-sectional analysisLogistic regression modelsCare hospitalHospital characteristicsReduced oddsMedical conditionsAdministrative claimsInfarctionPatientsPneumoniaService beneficiaries
2007
Measuring Performance For Treating Heart Attacks And Heart Failure: The Case For Outcomes Measurement
Krumholz HM, Normand SL, Spertus JA, Shahian DM, Bradley EH. Measuring Performance For Treating Heart Attacks And Heart Failure: The Case For Outcomes Measurement. Health Affairs 2007, 26: 75-85. PMID: 17211016, DOI: 10.1377/hlthaff.26.1.75.Peer-Reviewed Original Research
2001
American College of Cardiology key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes
Cannon C, Battler A, Brindis R, Cox J, Ellis S, Every N, Flaherty J, Harrington R, Krumholz H, Simoons M, Van De Werf F, Weintraub W, Mitchell K, Morrisson S, Brindis R, Anderson H, Cannom D, Chitwood W, Cigarroa J, Collins-Nakai R, Ellis S, Gibbons R, Grover F, Heidenreich P, Khandheria B, Knoebel S, Krumholz H, Malenka D, Mark D, Mckay C, Passamani E, Radford M, Riner R, Schwartz J, Shaw R, Shemin R, Van Fossen D, Verrier E, Watkins M, Phoubandith D, Furnelli T. American College of Cardiology key data elements and definitions for measuring the clinical management and outcomes of patients with acute coronary syndromes. Journal Of The American College Of Cardiology 2001, 38: 2114-2130. PMID: 11738323, DOI: 10.1016/s0735-1097(01)01702-8.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 codes