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 DatabaseAssociation between antecedent statin use and decreased mortality in hospitalized patients with COVID-19
Gupta A, Madhavan MV, Poterucha TJ, DeFilippis EM, Hennessey JA, Redfors B, Eckhardt C, Bikdeli B, Platt J, Nalbandian A, Elias P, Cummings MJ, Nouri SN, Lawlor M, Ranard LS, Li J, Boyle C, Givens R, Brodie D, Krumholz HM, Stone GW, Sethi SS, Burkhoff D, Uriel N, Schwartz A, Leon MB, Kirtane AJ, Wan EY, Parikh SA. Association between antecedent statin use and decreased mortality in hospitalized patients with COVID-19. Nature Communications 2021, 12: 1325. PMID: 33637713, PMCID: PMC7910606, DOI: 10.1038/s41467-021-21553-1.Peer-Reviewed Original ResearchConceptsAcute respiratory distress syndromeStatin usePrimary endpointCOVID-19Multivariable logistic regression modelStudy periodLower inpatient mortalityPropensity-matched cohortRespiratory distress syndromeCoronavirus disease 2019Electronic medical recordsLogistic regression modelsPropensity-score matchingHospital mortalityHyperinflammatory stateOutpatient medicationsClinical characteristicsInpatient mortalityStatin usersThrombotic complicationsDistress syndromeHospitalized patientsMyocardial injuryMedical recordsRetrospective analysis
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 Research
2019
Acute Kidney Injury Among Older Patients Undergoing Coronary Angiography for Acute Myocardial Infarction: The SILVER-AMI Study
Dodson JA, Hajduk A, Curtis J, Geda M, Krumholz HM, Song X, Tsang S, Blaum C, Miller P, Parikh CR, Chaudhry SI. Acute Kidney Injury Among Older Patients Undergoing Coronary Angiography for Acute Myocardial Infarction: The SILVER-AMI Study. The American Journal Of Medicine 2019, 132: e817-e826. PMID: 31170374, PMCID: PMC6891160, DOI: 10.1016/j.amjmed.2019.05.022.Peer-Reviewed Original ResearchMeSH KeywordsActivities of Daily LivingAcute Kidney InjuryAge FactorsAgedAged, 80 and overCohort StudiesCoronary AngiographyDatabases, FactualFemaleGeriatric AssessmentHospital MortalityHospitalizationHumansKaplan-Meier EstimateLogistic ModelsMaleMyocardial InfarctionOdds RatioPrognosisProportional Hazards ModelsRisk AssessmentSeverity of Illness IndexSurvival AnalysisConceptsAcute kidney injuryAcute myocardial infarctionAge-related conditionsKidney injuryBody mass indexCoronary angiographyRisk factorsMyocardial infarctionOlder patientsMean ageAcute kidney injury risk factorsAcute Myocardial Infarction StudyAcute myocardial infarction cohortOlder adultsGlobal Outcomes criteriaMyocardial Infarction StudyInjury risk factorsParticipants' mean ageSILVER-AMI StudyMultivariable adjustmentComorbid diseasesHeart failureIndependent predictorsGeriatric conditionsMass index
2018
Emergency Department Volume and Outcomes for Patients After Chest Pain Assessment
Ko DT, Dattani ND, Austin PC, Schull MJ, Ross JS, Wijeysundera HC, Tu JV, Eberg M, Koh M, Krumholz HM. Emergency Department Volume and Outcomes for Patients After Chest Pain Assessment. Circulation Cardiovascular Quality And Outcomes 2018, 11: e004683. PMID: 30354285, DOI: 10.1161/circoutcomes.118.004683.Peer-Reviewed Original ResearchConceptsAcute coronary syndromeChest painHigh-volume EDsCoronary syndromeED volumeVolume thresholdCardiac medication useChest pain assessmentLower adverse outcomesEmergency department visitsAdjusted odds ratioPopulation-based dataProcess of carePotential confounding variablesHigher ED volumesHierarchical logistic regression modelsLogistic regression modelsEmergency department volumeCause deathCardiac testingComposite outcomeDepartment visitsDiabetes mellitusMedication usePrimary outcome
2017
Risk Trajectories of Readmission and Death in the First Year after Hospitalization for Chronic Obstructive Pulmonary Disease
Lindenauer PK, Dharmarajan K, Qin L, Lin Z, Gershon AS, Krumholz HM. Risk Trajectories of Readmission and Death in the First Year after Hospitalization for Chronic Obstructive Pulmonary Disease. American Journal Of Respiratory And Critical Care Medicine 2017, 197: 1009-1017. PMID: 29206052, PMCID: PMC5909167, DOI: 10.1164/rccm.201709-1852oc.Peer-Reviewed Original ResearchConceptsChronic obstructive pulmonary diseaseSame respective groupsObstructive pulmonary diseaseVentilator supportPulmonary diseaseRisk of readmissionRisk of hospitalizationGeneral elderly populationGeneral Medicare populationRisk of deathDaily riskRespective groupsReadmission ratesHospital readmissionAbsolute riskMedicare populationReadmissionElderly populationMedicare beneficiariesHospitalizationClinical servicesFirst monthProlonged riskDeathLongitudinal outcomes
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 ResearchTrends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006–2013
Lipska KJ, Yao X, Herrin J, McCoy RG, Ross JS, Steinman MA, Inzucchi SE, Gill TM, Krumholz HM, Shah ND. Trends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006–2013. Diabetes Care 2016, 40: 468-475. PMID: 27659408, PMCID: PMC5360291, DOI: 10.2337/dc16-0985.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedBlood GlucoseComorbidityDiabetes Mellitus, Type 2Dipeptidyl-Peptidase IV InhibitorsDrug UtilizationFemaleGlycated HemoglobinHumansHypoglycemiaHypoglycemic AgentsInsulinLogistic ModelsMaleMetforminMiddle AgedRetrospective StudiesSulfonylurea CompoundsThiazolidinedionesYoung AdultConceptsGlycemic controlSevere hypoglycemiaOlder patientsDipeptidyl peptidase-4 inhibitorsGlucose-lowering drugsGlucose-lowering medicationsProportion of patientsOverall glycemic controlPeptidase-4 inhibitorsMedicare Advantage patientsSex-standardized ratesType 2 diabetesOverall rateClass of agentsMore comorbiditiesChronic comorbiditiesYounger patientsAdvantage patientsDrug utilizationClaims dataPatientsHypoglycemiaHemoglobin AT2DMComorbidities
2014
Hospital variation in risk-standardized hospital admission rates from US EDs among adults
Capp R, Ross JS, Fox JP, Wang Y, Desai MM, Venkatesh AK, Krumholz HM. Hospital variation in risk-standardized hospital admission rates from US EDs among adults. The American Journal Of Emergency Medicine 2014, 32: 837-843. PMID: 24881514, DOI: 10.1016/j.ajem.2014.03.033.Peer-Reviewed Original ResearchConceptsHospital admission ratesEmergency departmentAdmission ratesClinical characteristicsED visitsHospital factorsClinical factorsAdult ED visitsUS emergency departmentsHospital teaching statusCross-sectional analysisPatient characteristicsHospital admissionHospital variationPatientsTeaching statusHospitalED dataVisitsRepresentative sampleAdultsRural locationsAdmissionFactorsNational variations
2013
Quality collaboratives and campaigns to reduce readmissions: What strategies are hospitals using?
Bradley EH, Sipsma H, Curry L, Mehrotra D, Horwitz LI, Krumholz H. Quality collaboratives and campaigns to reduce readmissions: What strategies are hospitals using? Journal Of Hospital Medicine 2013, 8: 601-608. PMID: 24038927, PMCID: PMC4029612, DOI: 10.1002/jhm.2076.Peer-Reviewed Original ResearchMeSH KeywordsContinuity of Patient CareCooperative BehaviorCross-Sectional StudiesHealth Care SurveysHealth Plan ImplementationHumansInformation DisseminationInternetLogistic ModelsMedication ReconciliationMultivariate AnalysisPatient DischargePatient ReadmissionQuality Assurance, Health CareQuality Indicators, Health CareUnited StatesConceptsQuality collaborativesCardiac rehabilitation servicesMultivariable logistic regressionSkilled nursing facilitiesHospital readmissionMedication reconciliationTreating physicianPatient dischargePatient referralOutpatient physiciansMultivariable modelNursing facilitiesStandard frequency analysisHospitalRehabilitation servicesWeb-based surveyReadmissionLogistic regressionQuality InitiativeHospital strategiesPhysiciansCurrent useCollaborativesPatientsSTAARRegional 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 dataPatientsPneumoniaCardiologistsHospitalizationAdmissionQuintileLong-Term Outcomes in Elderly Survivors of In-Hospital Cardiac Arrest
Chan PS, Nallamothu BK, Krumholz HM, Spertus JA, Li Y, Hammill BG, Curtis LH. Long-Term Outcomes in Elderly Survivors of In-Hospital Cardiac Arrest. New England Journal Of Medicine 2013, 368: 1019-1026. PMID: 23484828, PMCID: PMC3652256, DOI: 10.1056/nejmoa1200657.Peer-Reviewed Original ResearchConceptsHospital cardiac arrestCardiac arrestNeurologic disabilityReadmission ratesElderly survivorsRisk-adjusted ratesNeurologic statusHeart failureIn-Hospital Cardiac ArrestInpatient cardiac arrestsSevere neurologic disabilityDemographic characteristicsLong-term outcomesLong-term survivalYears of ageRate of survivalHospital dischargeOlder patientsYounger patientsWhite patientsBlack patientsTerm outcomesNational registryMedicare filesPatientsIncome inequality and 30 day outcomes after acute myocardial infarction, heart failure, and pneumonia: retrospective cohort study
Lindenauer PK, Lagu T, Rothberg MB, Avrunin J, Pekow PS, Wang Y, Krumholz HM. Income inequality and 30 day outcomes after acute myocardial infarction, heart failure, and pneumonia: retrospective cohort study. The BMJ 2013, 346: f521. PMID: 23412830, PMCID: PMC3573180, DOI: 10.1136/bmj.f521.Peer-Reviewed Original ResearchConceptsIncome inequalityAcute myocardial infarctionUS statesRetrospective cohort studyDay of admissionHeart failureMyocardial infarctionCohort studyInequality levelsIndividual incomeGini coefficientUS acute care hospitalsHighest quarterDays of dischargeDays of hospitalizationRisk of readmissionRisk of deathPatient socioeconomic characteristicsRisk of mortalityAcute care hospitalsSocioeconomic characteristicsInequalityLogistic regression modelsCare hospitalReadmission analysis
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
2011
30-Day Readmission for Patients Undergoing Percutaneous Coronary Interventions in New York State
Hannan EL, Zhong Y, Krumholz H, Walford G, Holmes DR, Stamato NJ, Jacobs AK, Venditti FJ, Sharma S, King SB. 30-Day Readmission for Patients Undergoing Percutaneous Coronary Interventions in New York State. JACC Cardiovascular Interventions 2011, 4: 1335-1342. PMID: 22192374, DOI: 10.1016/j.jcin.2011.08.013.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionReadmission ratesUnique risk factorsRisk factorsCoronary interventionAdministrative databasesComplications of PCIPre-procedural risk factorsRepeat percutaneous coronary interventionChronic ischemic heart diseaseOverall readmission rateHigher readmission ratesIschemic heart diseaseLength of stayNew York State patientsRecognition of patientsDiagnostic risk factorsCost-effectiveness standpointPCI patientsPCI registryChest painHeart failureHospital readmissionHeart diseasePrincipal diagnosisNational Trends in Use of Computed Tomography in the Emergency Department
Kocher KE, Meurer WJ, Fazel R, Scott PA, Krumholz HM, Nallamothu BK. National Trends in Use of Computed Tomography in the Emergency Department. Annals Of Emergency Medicine 2011, 58: 452-462.e3. PMID: 21835499, DOI: 10.1016/j.annemergmed.2011.05.020.Peer-Reviewed Original ResearchConceptsEmergency departmentCT useComputed tomographyED visitsRisk ratioNational Hospital Ambulatory Medical Care SurveyAmbulatory Medical Care SurveyShortness of breathLikelihood of admissionSpecific emergency departmentLarge nationwide surveyAbdominal painChest painFlank painAcute illnessED encountersRetrospective studyCare SurveyCommon complaintPatient visitsMultivariable modelingCT scanMAIN OUTCOMEED servicesPainDo Imaging Studies Performed in Physician Offices Increase Downstream Utilization? An Empiric Analysis of Cardiac Stress Testing With Imaging
Chen J, Fazel R, Ross JS, McNamara RL, Einstein AJ, Al-Mallah M, Krumholz HM, Nallamothu BK. Do Imaging Studies Performed in Physician Offices Increase Downstream Utilization? An Empiric Analysis of Cardiac Stress Testing With Imaging. JACC Cardiovascular Imaging 2011, 4: 630-637. PMID: 21679898, PMCID: PMC3319749, DOI: 10.1016/j.jcmg.2011.04.003.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overCardiac CatheterizationChi-Square DistributionEchocardiography, StressEmpirical ResearchFemaleHealthcare DisparitiesHumansInsurance, HealthLogistic ModelsMaleMiddle AgedMyocardial Perfusion ImagingMyocardial RevascularizationOffice VisitsOutpatient Clinics, HospitalPractice Patterns, Physicians'Predictive Value of TestsResidence CharacteristicsTime FactorsUnited StatesYoung AdultConceptsMyocardial perfusion imagingSubsequent myocardial perfusion imagingProportion of patientsStress echocardiographyCardiac catheterizationHospital outpatient settingPhysician's officeOutpatient settingStress testingSubsequent cardiac testingCardiac stress testingDownstream resource utilizationHospital outpatient facilitiesCardiac testingPrivate health insuranceDownstream testingOffice imagingPerfusion imagingCatheterizationImaging studiesOutpatient imagingPatientsHealth insuranceLower ratesHigh rateAn 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
2010
Patterns of moderate and vigorous physical activity in obese and overweight compared with non‐overweight children
DORSEY KB, HERRIN J, KRUMHOLZ HM. Patterns of moderate and vigorous physical activity in obese and overweight compared with non‐overweight children. Pediatric Obesity 2010, 6: e547-e555. PMID: 20883127, PMCID: PMC3815589, DOI: 10.3109/17477166.2010.490586.Peer-Reviewed Original ResearchConceptsVigorous physical activityOW/OBNon-overweight childrenMVPA boutsPhysical activityGreater body mass index z-scoreVPA boutsOW/OB groupBody mass index z-scoreMean daily MVPANon-overweight groupLess physical activityIndex z-scoreMinutes of MVPANon-overweight peersObese childrenObese participantsOverweight childrenOB groupDaily MVPASustained MVPADistinct patternsOB participantsMVPAConsecutive boutsHospital 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