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
2023
Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsElectronic Health RecordsHumansInternational Classification of DiseasesPredictive Value of TestsPulmonary EmbolismReproducibility of ResultsConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithm
2022
Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies
Khera R, Schuemie MJ, Lu Y, Ostropolets A, Chen R, Hripcsak G, Ryan PB, Krumholz HM, Suchard MA. Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies. BMJ Open 2022, 12: e057977. PMID: 35680274, PMCID: PMC9185490, DOI: 10.1136/bmjopen-2021-057977.Peer-Reviewed Original ResearchMeSH KeywordsAdultDiabetes Mellitus, Type 2Dipeptidyl-Peptidase IV InhibitorsHumansHypoglycemic AgentsReproducibility of ResultsSodium-Glucose Transporter 2 InhibitorsSulfonylurea CompoundsConceptsLarge-scale Evidence GenerationType 2 diabetes mellitusCardiovascular effectivenessDiabetes mellitusSafety outcomesGlucagon-like peptide-1 receptor agonistsSodium-glucose co-transporter-2 inhibitorsMajor adverse cardiovascular eventsNew-user cohort designPeptide-1 receptor agonistsDipeptidyl peptidase-4 inhibitorsSafety studiesAdverse cardiovascular eventsPrimary cardiovascular outcomePeptidase-4 inhibitorsAnti-hyperglycaemic agentsElectronic health record data sourcesEvidence generationCardiovascular eventsCardiovascular outcomesCardiovascular riskActive comparatorTherapeutic optionsReceptor agonistDrug comparisons
2021
Multicentre methodological study to create a publicly available score of hospital financial standing in the USA
Zinoviev R, Krumholz HM, Ciccarone R, Antle R, Forman HP. Multicentre methodological study to create a publicly available score of hospital financial standing in the USA. BMJ Open 2021, 11: e046500. PMID: 34301654, PMCID: PMC8311305, DOI: 10.1136/bmjopen-2020-046500.Peer-Reviewed Original ResearchConceptsCredit ratingsFinancial dataBond ratingsMoody’s credit ratingsHospital's financial standingHospital financial dataFinancial literatureFinancial stabilityFinancial indicatorsFinancial scoresFinancial trendsFinancial healthFinancial standingFinancial metricsGreater shareMedicare dischargesWeighted variablesShareHospital operationsUnique variablesSingle composite scoreVariablesMoodyHealth systemModel
2020
An instrument for assessing the quality of informed consent documents for elective procedures: development and testing
Spatz ES, Suter LG, George E, Perez M, Curry L, Desai V, Bao H, Geary LL, Herrin J, Lin Z, Bernheim SM, Krumholz HM. An instrument for assessing the quality of informed consent documents for elective procedures: development and testing. BMJ Open 2020, 10: e033297. PMID: 32434933, PMCID: PMC7247404, DOI: 10.1136/bmjopen-2019-033297.Peer-Reviewed Original ResearchMeSH KeywordsConsent FormsElective Surgical ProceduresHumansInformed ConsentReproducibility of ResultsResearch DesignSurveys and QuestionnairesQuality of informed consent documents among US. hospitals: a cross-sectional study
Spatz ES, Bao H, Herrin J, Desai V, Ramanan S, Lines L, Dendy R, Bernheim SM, Krumholz HM, Lin Z, Suter LG. Quality of informed consent documents among US. hospitals: a cross-sectional study. BMJ Open 2020, 10: e033299. PMID: 32434934, PMCID: PMC7247389, DOI: 10.1136/bmjopen-2019-033299.Peer-Reviewed Original ResearchMeSH KeywordsAgedConsent FormsCross-Sectional StudiesHospitalsHumansInformed ConsentMedicareReproducibility of ResultsUnited StatesConceptsInformed consent documentsHOSPITAL scoreUS hospitalsMean hospital scoresRetrospective observational studyConsent documentsCross-sectional studyEight-item instrumentService patientsElective proceduresProcedure typeObservational studySurgical proceduresMedicare feeHospitalHospital qualityMeasure scoresInformed consentMost hospitalsSpearman correlationScoresFace validityIndependent ratersOutcomesStakeholder feedback
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 ResearchMeSH KeywordsAcute Kidney InjuryAgedContrast MediaCreatinineFemaleHumansMaleModels, StatisticalPercutaneous Coronary InterventionReproducibility of ResultsRisk AssessmentRisk FactorsConceptsCreatinine 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 setValidation and Regulation of Clinical Artificial Intelligence
Schulz WL, Durant T, Krumholz HM. Validation and Regulation of Clinical Artificial Intelligence. Clinical Chemistry 2019, 65: 1336-1337. PMID: 32100825, DOI: 10.1373/clinchem.2019.308304.Commentaries, Editorials and LettersThirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction
Dodson JA, Hajduk AM, Murphy TE, Geda M, Krumholz HM, Tsang S, Nanna MG, Tinetti ME, Goldstein D, Forman DE, Alexander KP, Gill TM, Chaudhry SI. Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction. Circulation Cardiovascular Quality And Outcomes 2019, 12: e005320. PMID: 31010300, PMCID: PMC6481309, DOI: 10.1161/circoutcomes.118.005320.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAgedAged, 80 and overFemaleGeriatric AssessmentHealth Status IndicatorsHumansMaleMyocardial InfarctionPatient AdmissionPatient ReadmissionPredictive Value of TestsProspective StudiesReproducibility of ResultsRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUnited StatesConceptsAcute myocardial infarctionReadmission risk modelFinal risk modelFunctional mobilityFunctional impairmentMyocardial infarctionOlder adultsFirst diastolic blood pressureChronic obstructive pulmonary diseaseAge-related functional impairmentsP2Y12 inhibitor useAcute kidney injuryDaily living (ADL) disabilityPatient-level factorsProspective cohort studyDiastolic blood pressureObstructive pulmonary diseasePatients of ageGeneral health statusStrongest predictorRisk modelMore comorbiditiesCause readmissionKidney injuryCohort studyApplication of the VIRGO taxonomy to differentiate acute myocardial infarction in young women
Sciria CT, Dreyer RP, D'Onofrio G, Safdar B, Krumholz HM, Spatz ES. Application of the VIRGO taxonomy to differentiate acute myocardial infarction in young women. International Journal Of Cardiology 2019, 288: 5-11. PMID: 31031078, DOI: 10.1016/j.ijcard.2019.03.054.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseCardiac catheterizationYoung womenNon-obstructive coronary artery diseaseSingle-center retrospective chart reviewYoung AMI Patients (VIRGO) studyObstructive coronary artery diseaseRetrospective chart reviewAcute myocardial infarctionProportion of womenUniversal definitionIndex admissionChart reviewArtery diseaseConsecutive womenMyocardial infarctionTreatment strategiesCatheterizationMore young womenType 2Type 1Class IType 4BWomenPatient studies
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
2017
Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring
Steventon A, Chaudhry SI, Lin Z, Mattera JA, Krumholz HM. Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring. BMC Medical Informatics And Decision Making 2017, 17: 43. PMID: 28420352, PMCID: PMC5395848, DOI: 10.1186/s12911-017-0426-4.Peer-Reviewed Original ResearchMeSH KeywordsBody WeightDiagnostic ErrorsFemaleFraudHeart FailureHumansMaleMiddle AgedMonitoring, AmbulatoryReproducibility of ResultsSelf ReportTelemedicineConceptsEnd-digit preferenceHeart failureHeart Failure Outcomes trialEffective preventive careCharacteristics of patientsSelf-reported weightHealth care professionalsSix-month trial periodIntervention patientsMore medicationsAccuracy of reportingOutcome trialsTrial enrollmentPreventive careClinical managementUnnecessary treatmentDesign of initiativesCare professionalsPatientsRegistration numberAlert fatigueElectronic medical dataTrial periodTrialsNumber of days
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 ResearchTroponin Testing for Clinicians
Brush JE, Kaul S, Krumholz HM. Troponin Testing for Clinicians. Journal Of The American College Of Cardiology 2016, 68: 2365-2375. PMID: 27884254, DOI: 10.1016/j.jacc.2016.08.066.Peer-Reviewed Original ResearchMeSH KeywordsAcute Coronary SyndromeBiomarkersClinical Decision-MakingHumansMyocardial InfarctionReproducibility of ResultsTroponinRisk-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
The Variation in Recovery
Spatz ES, Curry LA, Masoudi FA, Zhou S, Strait KM, Gross CP, Curtis JP, Lansky AJ, Soares Barreto-Filho JA, Lampropulos JF, Bueno H, Chaudhry SI, D'Onofrio G, Safdar B, Dreyer RP, Murugiah K, Spertus JA, Krumholz HM. The Variation in Recovery. Circulation 2015, 132: 1710-1718. PMID: 26350057, PMCID: PMC4858327, DOI: 10.1161/circulationaha.115.016502.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAlgorithmsAortic DissectionClassificationCoronary DiseaseDiagnostic Techniques, CardiovascularFemaleHumansMaleMedical RecordsMiddle AgedMyocardial InfarctionMyocardiumOxygen ConsumptionPhenotypePlaque, AtheroscleroticProspective StudiesReproducibility of ResultsRisk FactorsSex FactorsTreatment OutcomeYoung AdultConceptsAcute myocardial infarctionCoronary artery diseaseArtery diseaseClinical phenotypeNonobstructive coronary artery diseaseYoung AMI Patients (VIRGO) studyObstructive coronary artery diseaseYoung womenType 2 acute myocardial infarctionBiological disease mechanismsSubset of patientsThird universal definitionUnique clinical phenotypeCulprit lesionClinical characteristicsMyocardial infarctionTherapeutic efficacyUniversal definitionStudy participantsPatientsSupply-demand mismatchYoung adultsDisease mechanismsPatient studiesCurrent classification schemes
2014
Reporting of Results in ClinicalTrials.gov and High-Impact Journals
Becker JE, Krumholz HM, Ben-Josef G, Ross JS. Reporting of Results in ClinicalTrials.gov and High-Impact Journals. JAMA 2014, 311: 1063-1065. PMID: 24618969, PMCID: PMC3979514, DOI: 10.1001/jama.2013.285634.Peer-Reviewed Original ResearchRisk 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
2012
Documenting the Methods History
Krumholz HM. Documenting the Methods History. Circulation Cardiovascular Quality And Outcomes 2012, 5: 418-419. PMID: 22811497, DOI: 10.1161/circoutcomes.112.967646.Peer-Reviewed Original Research
2011
An 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