2024
Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure
Kyanko K, Sahay K, Wang Y, Li S, Schreiber M, Hager M, Myers R, Johnson W, Zhang J, Krumholz H, Suter L, Triche E. Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure. JAMA Network Open 2024, 7: e2414431. PMID: 38829614, PMCID: PMC11148674, DOI: 10.1001/jamanetworkopen.2024.14431.Peer-Reviewed Original ResearchConceptsCenters for Medicare & Medicaid ServicesSpecialty subgroupsPerformance quintileMedicare AdvantageReadmission ratesRisk-standardized readmission ratesHospital-wide readmission measureHospital outcome measuresTest-retest reliabilityRisk-adjustment variablesMeasurement reliabilityAdministrative claims dataReadmission measuresImprove measurement reliabilityIntegrated data repositoryMA beneficiariesQuintile rankingsMedicare beneficiariesMedicaid ServicesAll-causePublic reportingStudy assessed differencesClaims dataOutcome measuresMA cohort
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
2020
Attribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data
Dhruva SS, Parzynski CS, Gamble GM, Curtis JP, Desai NR, Yeh RW, Masoudi FA, Kuntz R, Shaw RE, Marinac‐Dabic D, Sedrakyan A, Normand S, Krumholz HM, Ross JS. Attribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data. Journal Of The American Heart Association 2020, 9: e013606. PMID: 32063087, PMCID: PMC7070203, DOI: 10.1161/jaha.119.013606.Peer-Reviewed Original ResearchMeSH KeywordsAdministrative Claims, HealthcareAgedAged, 80 and overCoronary RestenosisCoronary ThrombosisDatabases, FactualDrug-Eluting StentsFemaleHumansMaleMedicareMyocardial InfarctionPercutaneous Coronary InterventionProduct Surveillance, PostmarketingRegistriesRetreatmentRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUnited StatesConceptsIndex percutaneous coronary interventionPercutaneous coronary interventionSame coronary arteryDrug-eluting stentsNCDR CathPCI RegistrySubsequent percutaneous coronary interventionAcute myocardial infarctionCoronary arteryClaims dataCathPCI RegistryAdverse eventsIndex procedureMyocardial infarctionRepeat percutaneous coronary interventionReal-world registry dataTarget vessel revascularizationCoronary stent placementAdministrative claims dataLong-term safetyLongitudinal claims dataPotential safety eventsVessel revascularizationCoronary interventionDES placementStent thrombosis
2017
Identification of Emergency Department Visits in Medicare Administrative Claims: Approaches and Implications
Venkatesh AK, Mei H, Kocher KE, Granovsky M, Obermeyer Z, Spatz E, Rothenberg C, Krumholz H, Lin Z. Identification of Emergency Department Visits in Medicare Administrative Claims: Approaches and Implications. Academic Emergency Medicine 2017, 24: 422-431. PMID: 27864915, PMCID: PMC5905698, DOI: 10.1111/acem.13140.Peer-Reviewed Original ResearchConceptsED visitsEmergency department visitsClaims-based definitionED visitationAdministrative claimsDepartment visitsClaims dataAdministrative claims data setsHealthcare resource utilizationMore ED visitsAcute care practiceAdministrative claims dataQuality improvement interventionsEmergency care researchMedicare administrative claimsClaims data setsED useCritical careED servicesMedicare feeMedicare dataCare practicesService beneficiariesImprovement interventionsProvider definitions
2016
Association of Admission to Veterans Affairs Hospitals vs Non–Veterans Affairs Hospitals With Mortality and Readmission Rates Among Older Men Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia
Nuti SV, Qin L, Rumsfeld JS, Ross JS, Masoudi FA, Normand SL, Murugiah K, Bernheim SM, Suter LG, Krumholz HM. Association of Admission to Veterans Affairs Hospitals vs Non–Veterans Affairs Hospitals With Mortality and Readmission Rates Among Older Men Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia. JAMA 2016, 315: 582-592. PMID: 26864412, PMCID: PMC5459395, DOI: 10.1001/jama.2016.0278.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionNon-VA hospitalsReadmission ratesHeart failureVA hospitalsMortality rateVeterans AffairsMyocardial infarctionOlder menMedicare Standard Analytic FilesRisk-standardized mortality ratesCause readmission rateCause mortality ratesHigher readmission ratesStandard Analytic FilesVeterans Affairs hospitalRisk-standardized readmission ratesAdministrative claims dataAcute care hospitalsAssociation of admissionLittle contemporary informationLower mortality rateCross-sectional analysisAnalysis cohortCare hospital
2013
Regional Density of Cardiologists and Rates of Mortality for Acute Myocardial Infarction and Heart Failure
Kulkarni VT, Ross JS, Wang Y, Nallamothu BK, Spertus JA, Normand SL, Masoudi FA, Krumholz HM. Regional Density of Cardiologists and Rates of Mortality for Acute Myocardial Infarction and Heart Failure. Circulation Cardiovascular Quality And Outcomes 2013, 6: 352-359. PMID: 23680965, PMCID: PMC5323047, DOI: 10.1161/circoutcomes.113.000214.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overCardiologyCohort StudiesFemaleHealth Services AccessibilityHealth Services Needs and DemandHealthcare DisparitiesHeart FailureHospitalizationHumansLinear ModelsLogistic ModelsMaleMedicareMyocardial InfarctionOdds RatioPhysiciansPneumoniaPrognosisResidence CharacteristicsRisk AssessmentRisk FactorsTime FactorsUnited StatesWorkforceConceptsAcute myocardial infarctionHeart failureHospital referral regionsMortality riskLowest quintileMyocardial infarctionReferral regionsMedicare administrative claims dataCharacteristics of patientsRisk of deathAdministrative claims dataHierarchical logistic regression modelsLogistic regression modelsRate of mortalityRegional densityHighest quintileNumber of cardiologistsWorse outcomesClaims dataPatientsPneumoniaCardiologistsHospitalizationAdmissionQuintile
2012
Development of 2 Registry-Based Risk Models Suitable for Characterizing Hospital Performance on 30-Day All-Cause Mortality Rates Among Patients Undergoing Percutaneous Coronary Intervention
Curtis JP, Geary LL, Wang Y, Chen J, Drye EE, Grosso LM, Spertus JA, Rumsfeld JS, Weintraub WS, Masoudi FA, Brindis RG, Krumholz HM. Development of 2 Registry-Based Risk Models Suitable for Characterizing Hospital Performance on 30-Day All-Cause Mortality Rates Among Patients Undergoing Percutaneous Coronary Intervention. Circulation Cardiovascular Quality And Outcomes 2012, 5: 628-637. PMID: 22949491, DOI: 10.1161/circoutcomes.111.964569.Peer-Reviewed Original ResearchMeSH KeywordsAcute Coronary SyndromeAgedAged, 80 and overAngina PectorisChi-Square DistributionComorbidityFemaleHeart DiseasesHospital MortalityHospitalsHumansLogistic ModelsMaleMyocardial InfarctionOdds RatioOutcome and Process Assessment, Health CarePercutaneous Coronary InterventionQuality Indicators, Health CareRegistriesRisk AssessmentRisk FactorsShock, CardiogenicTime FactorsTreatment OutcomeUnited StatesConceptsST-segment elevation myocardial infarctionPercutaneous coronary interventionRisk-standardized mortality ratesElevation myocardial infarctionPatient mortality ratesMyocardial infarctionMortality rateCardiogenic shockCoronary interventionDerivation cohortHospital risk-standardized mortality ratesCause mortality ratesAdministrative claims dataQuality of careHierarchical logistic regression modelsNational Quality ForumLogistic regression modelsObserved mortality rateCathPCI RegistryNational HospitalClaims dataInfarctionPatientsQuality ForumFinal model
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
2007
Assessing surrogacy of data sources for institutional comparisons
Normand S, Wang Y, Krumholz H. Assessing surrogacy of data sources for institutional comparisons. Health Services And Outcomes Research Methodology 2007, 7: 79-96. DOI: 10.1007/s10742-006-0018-8.Peer-Reviewed Original ResearchThe Impact of Venous Thromboembolism on Risk of Death or Hemorrhage in Older Cancer Patients
Gross CP, Galusha DH, Krumholz HM. The Impact of Venous Thromboembolism on Risk of Death or Hemorrhage in Older Cancer Patients. Journal Of General Internal Medicine 2007, 22: 321-326. PMID: 17356962, PMCID: PMC1824718, DOI: 10.1007/s11606-006-0019-x.Peer-Reviewed Original ResearchConceptsRisk of deathOlder cancer patientsConcomitant venous thromboembolismVenous thromboembolismMajor hemorrhageCancer patientsCancer typesCancer diagnosisMedicare administrative claims dataPrevalence of VTEEnd Results cancer registryRetrospective cohort studyAdministrative claims dataCohort studyCancer RegistryInvasive cancerExcess riskMost cancer typesCancer stageClaims dataHemorrhagePatientsSociodemographic factorsPotential mediatorsDeath
2006
The effect of age and chronic illness on life expectancy after a diagnosis of colorectal cancer: implications for screening.
Gross CP, McAvay GJ, Krumholz HM, Paltiel AD, Bhasin D, Tinetti ME. The effect of age and chronic illness on life expectancy after a diagnosis of colorectal cancer: implications for screening. Annals Of Internal Medicine 2006, 145: 646-53. PMID: 17088577, DOI: 10.7326/0003-4819-145-9-200611070-00006.Peer-Reviewed Original ResearchConceptsChronic illnessColorectal cancerChronic conditionsLife expectancyCancer stageEarly-stage colorectal cancerPopulation-based cancer registriesPatients 67 yearsRetrospective cohort studyStage I cancerAdministrative claims dataChronic condition groupsFinal study sampleYears of ageShort life expectancyCohort studyEffect of agePatient ageI cancerCancer RegistryCancer variesHealthy patientsIndividual patientsMedicare claimsAdministrative claims