2024
Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing
Nargesi A, Adejumo P, Dhingra L, Rosand B, Hengartner A, Coppi A, Benigeri S, Sen S, Ahmad T, Nadkarni G, Lin Z, Ahmad F, Krumholz H, Khera R. Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing. JACC Heart Failure 2024 PMID: 39453355, DOI: 10.1016/j.jchf.2024.08.012.Peer-Reviewed Original ResearchReduced ejection fractionEjection fractionHeart failureLeft ventricular ejection fractionVentricular ejection fractionYale-New Haven HospitalIdentification of patientsCommunity hospitalIdentification of heart failureLanguage modelNorthwestern MedicineMeasure care qualityQuality of careNew Haven HospitalDeep learning-based natural language processingHFrEFGuideline-directed careDeep learning language modelsMIMIC-IIIDetect HFrEFNatural language processingReclassification improvementHospital dischargePatientsCare quality
2022
Institutional Variation in 30‐Day Complications Following Catheter Ablation of Atrial Fibrillation
Ngo L, Ali A, Ganesan A, Woodman R, Krumholz HM, Adams R, Ranasinghe I. Institutional Variation in 30‐Day Complications Following Catheter Ablation of Atrial Fibrillation. Journal Of The American Heart Association 2022, 11: e022009. PMID: 35156395, PMCID: PMC9245833, DOI: 10.1161/jaha.121.022009.Peer-Reviewed Original ResearchConceptsProcedure-related complicationsComplication rateAF ablationAtrial fibrillationCatheter ablationStroke/transient ischemic attackCare qualityTransient ischemic attackRisk of complicationsIschemic attackHospital stayCohort studyCommon complicationHospital dischargePericardial effusionCardiorespiratory failurePrimary outcomeProcedural characteristicsComplicationsPatientsHospitalStudy periodBackground ComplicationsPotential disparitiesFibrillation
2020
Frequency, trends and institutional variation in 30‐day all‐cause mortality and unplanned readmissions following hospitalisation for heart failure in Australia and New Zealand
Labrosciano C, Horton D, Air T, Tavella R, Beltrame JF, Zeitz CJ, Krumholz HM, Adams R, Scott IA, Gallagher M, Hossain S, Hariharaputhiran S, Ranasinghe I. Frequency, trends and institutional variation in 30‐day all‐cause mortality and unplanned readmissions following hospitalisation for heart failure in Australia and New Zealand. European Journal Of Heart Failure 2020, 23: 31-40. PMID: 33094886, DOI: 10.1002/ejhf.2030.Peer-Reviewed Original ResearchConceptsHF hospitalisationUnplanned readmissionReadmission ratesHeart failure hospitalisationUnplanned readmission rateMortality cohortReadmission cohortCause mortalityHeart failurePrimary outcomeHospitalisationReadmissionSeparate cohortMortality rateHospitalPatientsMortalityCare qualityPrivate hospitalsStudy periodCohortModest declineNational averageOutcomesDays
2019
Institutional Variation in Quality of Cardiovascular Implantable Electronic Device Implantation: A Cohort Study.
Ranasinghe I, Labrosciano C, Horton D, Ganesan A, Curtis JP, Krumholz HM, McGavigan A, Hossain S, Air T, Hariharaputhiran S. Institutional Variation in Quality of Cardiovascular Implantable Electronic Device Implantation: A Cohort Study. Annals Of Internal Medicine 2019, 171: 309-317. PMID: 31357210, DOI: 10.7326/m18-2810.Peer-Reviewed Original ResearchConceptsCardiovascular implantable electronic devicesComplication rateCohort studyCIED complicationsCardiovascular implantable electronic device (CIED) implantationMajor device-related complicationsDays of dischargeDevice-related complicationsProcedure-related complicationsImplantable electronic devicesPPM implantationMajor complicationsICD placementDevice implantationElective proceduresComplicationsHospitalCare qualityStudy periodPatientsAdministrative dataInstitutional variationNational averageImplantation
2012
Based On Key Measures, Care Quality For Medicare Enrollees At Safety-Net And Non-Safety-Net Hospitals Was Almost Equal
Ross JS, Bernheim SM, Lin Z, Drye EE, Chen J, Normand SL, Krumholz HM. Based On Key Measures, Care Quality For Medicare Enrollees At Safety-Net And Non-Safety-Net Hospitals Was Almost Equal. Health Affairs 2012, 31: 1739-1748. PMID: 22869652, PMCID: PMC3527010, DOI: 10.1377/hlthaff.2011.1028.Peer-Reviewed Original ResearchConceptsSafety-net hospitalNet hospitalReadmission ratesUrban hospitalHeart failure mortalityRisk-standardized ratesAcute myocardial infarctionIndicators of careService Medicare beneficiariesHeart failureClinical outcomesMyocardial infarctionWorse outcomesMedicare beneficiariesHospitalMedicare enrolleesHospital qualityCare qualityVulnerable populationsGreater financial strainOutcomesMortalityFinancial strainCareMore affluent populations
2011
Quality of Care in the US Territories
Nunez-Smith M, Bradley EH, Herrin J, Santana C, Curry LA, Normand SL, Krumholz HM. Quality of Care in the US Territories. JAMA Internal Medicine 2011, 171: 1528-1540. PMID: 21709184, PMCID: PMC3251926, DOI: 10.1001/archinternmed.2011.284.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionRisk-standardized readmission ratesRisk-standardized mortality ratesHeart failureMortality rateReadmission ratesProcess measuresHospital characteristicsHighest risk-standardized mortality ratesPrincipal discharge diagnosisQuality of careHealth care qualityDischarge diagnosisService patientsMyocardial infarctionTerritorial HospitalNonfederal hospitalsUS territoriesMedicare feePneumoniaHospitalCare qualityPatientsPerformance of hospitalsUS states
2008
Influence of Patients’ Socioeconomic Status on Clinical Management Decisions: A Qualitative Study
Bernheim SM, Ross JS, Krumholz HM, Bradley EH. Influence of Patients’ Socioeconomic Status on Clinical Management Decisions: A Qualitative Study. The Annals Of Family Medicine 2008, 6: 53-59. PMID: 18195315, PMCID: PMC2203396, DOI: 10.1370/afm.749.Peer-Reviewed Original ResearchConceptsClinical management decisionsPatients' socioeconomic statusClinical managementSocioeconomic statusPatient sPatient outcomesPrimary care physiciansStandard of careInfluence of patientLow socioeconomic statusVaried practice settingsHealth care qualitySES influencesCare physiciansHispanic ethnicityPhysician perspectivesPractice settingsCare qualityPatientsPhysiciansPatient interestMinority racial backgroundsInterview guideFinancial strainOutcomes