2024
Hospital COVID-19 Burden and Adverse Event Rates
Metersky M, Rodrick D, Ho S, Galusha D, Timashenka A, Grace E, Marshall D, Eckenrode S, Krumholz H. Hospital COVID-19 Burden and Adverse Event Rates. JAMA Network Open 2024, 7: e2442936. PMID: 39495512, DOI: 10.1001/jamanetworkopen.2024.42936.Peer-Reviewed Original ResearchConceptsCOVID-19 burdenHospital admissionPatient safetyRelative riskCohort studyStudy of hospital admissionsAcute care hospitalsRisk-adjustment variablesRisk-adjusted ratesMedicare hospital admissionsCOVID-19 pandemicStaffing shortagesHospital characteristicsMain OutcomesHospital resilienceSurge capacityMedicare patientsCare hospitalHighest burdenPrevent declinesPatient admissionsStudy sampleElixhauser comorbiditiesCOVID-19Low burdenValidating International Classification of Diseases Code (ICD) 10th Revision Algorithms for Accurate Identification of Pulmonary Embolism
Bikdeli B, Khairani C, Bejjani A, Lo Y, Mahajan S, Caraballo C, Jimenez J, Krishnathasan D, Zarghami M, Rashedi S, Jimenez D, Barco S, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Mojibian H, Aneja S, Khera R, Konstantinides S, Goldhaber S, Wang L, Zhou L, Monreal M, Piazza G, Krumholz H, Investigators P. Validating International Classification of Diseases Code (ICD) 10th Revision Algorithms for Accurate Identification of Pulmonary Embolism. Journal Of Thrombosis And Haemostasis 2024 PMID: 39505153, DOI: 10.1016/j.jtha.2024.10.013.Peer-Reviewed Original ResearchDischarge codesInternational ClassificationICD-10Yale New Haven Health SystemPositive predictive valueMass General Brigham hospitalsAccuracy of ICD-10ICD-10 codesPulmonary embolismHealth systemImage codingElectronic databasesF1 scorePre-specified protocolExcellent positive predictive valueIndependent physiciansHighest F1 scoreIdentification of pulmonary embolismAcute pulmonary embolismSecondary codePE codesScoresIdentified PERevised algorithmNatural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
Adejumo P, Thangaraj P, Dhingra L, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz H, Khera R. Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure. JAMA Network Open 2024, 7: e2443925. PMID: 39509128, PMCID: PMC11544492, DOI: 10.1001/jamanetworkopen.2024.43925.Peer-Reviewed Original ResearchConceptsFunctional status assessmentArea under the receiver operating characteristic curveClinical documentationElectronic health record dataHF symptomsOptimal care deliveryHealth record dataAssess functional statusStatus assessmentClinical trial participationProcessing of clinical documentsFunctional status groupCare deliveryOutpatient careMain OutcomesMedical notesTrial participantsNew York Heart AssociationFunctional statusQuality improvementRecord dataHeart failureClinical notesDiagnostic studiesStatus groupsArtificial Intelligence in Cardiovascular Clinical Trials
Cunningham J, Abraham W, Bhatt A, Dunn J, Felker G, Jain S, Lindsell C, Mace M, Martyn T, Shah R, Tison G, Fakhouri T, Psotka M, Krumholz H, Fiuzat M, O’Connor C, Solomon S, Collaboratory H. Artificial Intelligence in Cardiovascular Clinical Trials. Journal Of The American College Of Cardiology 2024, 84: 2051-2062. PMID: 39505413, DOI: 10.1016/j.jacc.2024.08.069.Peer-Reviewed Original ResearchConceptsArtificial intelligenceIntegrate AIPatient privacyClinical trialsRandomized clinical trialsClinical event outcomesCardiovascular clinical trialsIntelligenceInaccurate resultsRandomized trialsInterpreting imagesCardiovascular therapyMedical decision makingDecision makingGold standardValidity of trial resultsClinical trial operationsPrivacyEligibility for Anti-Obesity Medications Among Medicare Beneficiaries with Overweight or Obesity
Chetty A, Khunte M, Chen A, Jastreboff A, Krumholz H, Lu Y. Eligibility for Anti-Obesity Medications Among Medicare Beneficiaries with Overweight or Obesity. Journal Of General Internal Medicine 2024, 1-3. PMID: 39477867, DOI: 10.1007/s11606-024-09178-8.Peer-Reviewed Original ResearchRecommendations to promote equity, diversity and inclusion in decentralized clinical trials
Aiyegbusi O, Cruz Rivera S, Kamudoni P, Anderson N, Collis P, Denniston A, Harding R, Hughes S, Khunti K, Kotecha D, Krumholz H, Liu X, McMullan C, Molony-Oates B, Monteiro J, Myles P, Rantell K, Soltys K, Verdi R, Wilson R, Calvert M. Recommendations to promote equity, diversity and inclusion in decentralized clinical trials. Nature Medicine 2024, 30: 3075-3084. PMID: 39472759, DOI: 10.1038/s41591-024-03323-w.Peer-Reviewed Original ResearchClinical trial participationDecentralized clinical trialsTrial participantsBarriers to clinical trial participationPromote equityGeneralizability of trial resultsClinical trial teamsHealth inequalitiesUnderserved groupsBarriers individualsTrial teamClinical trialsParticipantsElectronic dataTrial resultsEquitable mannerTrialsRecommendationsHealthInclusionRemote researchAssociated with several challengesArtificial intelligence applied to electrocardiographic images for the risk stratification of cancer therapeutics-related cardiac dysfunction
Oikonomou E, Sangha V, Dhingra L, Aminorroaya A, Coppi A, Krumholz H, Baldassarre L, Khera R. Artificial intelligence applied to electrocardiographic images for the risk stratification of cancer therapeutics-related cardiac dysfunction. European Heart Journal 2024, 45: ehae666.3190. DOI: 10.1093/eurheartj/ehae666.3190.Peer-Reviewed Original ResearchCancer therapeutics-related cardiac dysfunctionImmune checkpoint inhibitorsGlobal longitudinal strainLeft ventricular systolic dysfunctionNon-Hodgkin's lymphomaCardiac dysfunctionAI-ECGNegative control analysesAssociated with higher incidenceVentricular systolic dysfunctionCohort of patientsRisk stratification strategiesCheckpoint inhibitorsTrastuzumab exposureSystolic dysfunctionRisk stratificationBreast cancerRisk biomarkersSecondary outcomesLongitudinal strainStratification strategiesTrastuzumabPatientsHigher incidenceAnthracyclinesArtificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography
Oikonomou E, Holste G, Coppi A, Mcnamara R, Nadkarni G, Krumholz H, Wang Z, Miller E, Khera R. Artificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography. European Heart Journal 2024, 45: ehae666.157. DOI: 10.1093/eurheartj/ehae666.157.Peer-Reviewed Original ResearchConvolutional neural networkMulti-labelState-of-the-art performanceState-of-the-artCustom loss functionDeep learning modelsAI frameworkNeural networkLoss functionAutomated metricsLearning modelsAugmentation approachVideoAcquisition qualityAdvanced protocolsPoint-of-care ultrasonographyImagesTransthoracic echocardiogramClassifierATTR-CMAlgorithmNetworkAI screeningAcquisitionPresence of severe ASCharacterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms
Oikonomou E, Sangha V, Coppi A, Krumholz H, Miller E, Khera R. Characterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms. European Heart Journal 2024, 45: ehae666.2089. DOI: 10.1093/eurheartj/ehae666.2089.Peer-Reviewed Original ResearchDiagnosis of ATTR-CMATTR-CMBone scintigraphy scansClinical diagnosisTransthyretin amyloid cardiomyopathyMonths of diagnosisSex-matched controlsElectrocardiographic (ECGIndolent courseCardiac amyloidosisScintigraphy scanAmyloid cardiomyopathyEchocardiographic studiesAI-ECGEchocardiogramEventual diagnosisDetect longitudinal changesConfirmatory testDiagnosisClinical diseasePercentage of individualsLongitudinal changesECGMedianMonthsArtificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Sangha V, Oikonomou E, Krumholz H, Miller E, Khera R. Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis. European Heart Journal 2024, 45: ehae666.3436. DOI: 10.1093/eurheartj/ehae666.3436.Peer-Reviewed Original ResearchATTR-CMBone scintigraphy scansTransthyretin amyloid cardiomyopathyPositive predictive valueAI-ECG algorithmCardiac amyloidosisScintigraphy scanAmyloid cardiomyopathyAI-ECGSex-matchedDevelopment cohortMyocardial remodelingUnder-diagnosedUnder-treatedMatched controlsPredictive valueUnder-recognizedTransthyretin stabilizersConvolutional neural networkPatientsECGArtificial intelligenceHospitalPrevalenceTransthyretinAutomated 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 qualityModernizing Medical Device Regulation: Challenges and Opportunities for the 510(k) Clearance Process.
Kadakia K, Rathi V, Dhruva S, Ross J, Krumholz H. Modernizing Medical Device Regulation: Challenges and Opportunities for the 510(k) Clearance Process. Annals Of Internal Medicine 2024 PMID: 39374526, DOI: 10.7326/annals-24-00728.Peer-Reviewed Original ResearchCost-effectiveness of iptacopan in paroxysmal nocturnal hemoglobinuria
Ito S, Chetlapalli K, Wang D, Potnis K, Richmond R, Krumholz H, Lee A, Cuker A, Goshua G. Cost-effectiveness of iptacopan in paroxysmal nocturnal hemoglobinuria. Blood 2024 PMID: 39374533, DOI: 10.1182/blood.2024025176.Peer-Reviewed Original ResearchStandard-of-careParoxysmal nocturnal hemoglobinuriaIncremental net monetary benefitNocturnal hemoglobinuriaComplement-mediated hemolytic anemiaTreating paroxysmal nocturnal hemoglobinuriaComplement C5 inhibitor eculizumabPhase 3 studyQuality-adjusted life expectancyRare blood disorderComprehensive cost-effectiveness analysisProbabilistic sensitivity analysesCost-saving thresholdsC5 inhibitor eculizumabNet monetary benefitPersistent anemiaIptacopanExtravascular hemolysisIntravenous infusionMonotherapyHemolytic anemiaAnemia resolutionC5 inhibitionFDA approvalPrimary outcomeRacial and Ethnic Disparities in Age-Specific All-Cause Mortality During the COVID-19 Pandemic
Faust J, Renton B, Bongiovanni T, Chen A, Sheares K, Du C, Essien U, Fuentes-Afflick E, Haywood T, Khera R, King T, Li S, Lin Z, Lu Y, Marshall A, Ndumele C, Opara I, Loarte-Rodriguez T, Sawano M, Taparra K, Taylor H, Watson K, Yancy C, Krumholz H. Racial and Ethnic Disparities in Age-Specific All-Cause Mortality During the COVID-19 Pandemic. JAMA Network Open 2024, 7: e2438918. PMID: 39392630, DOI: 10.1001/jamanetworkopen.2024.38918.Peer-Reviewed Original ResearchConceptsCOVID-19 public health emergencyNon-HispanicPublic health emergencyOther Pacific IslanderExcess mortalityAlaska NativesUS populationExcess deathsRates of excess mortalityCross-sectional study analyzed dataYears of potential lifeMortality relative riskNon-Hispanic whitesCross-sectional studyPacific IslandersStudy analyzed dataAll-cause mortalityEthnic groupsMortality disparitiesMortality ratioTotal populationDeath certificatesEthnic disparitiesMain OutcomesDecedent ageLong-term exposure to wildland fire smoke PM2.5 and mortality in the contiguous United States
Ma Y, Zang E, Liu Y, Wei J, Lu Y, Krumholz H, Bell M, Chen K. Long-term exposure to wildland fire smoke PM2.5 and mortality in the contiguous United States. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2403960121. PMID: 39316057, PMCID: PMC11459178, DOI: 10.1073/pnas.2403960121.Peer-Reviewed Original ResearchConceptsWildland firesContiguous United StatesNonaccidental mortalityExposure to ambient fine particlesSmoke PM<sWildland fire smokeMoving average concentrationsAmbient fine particlesLong-term exposureAverage concentrationSmoke PMHealth risksFine particlesFire smokeTemporal confoundingHealth effectsKidney disease mortalityChronic kidney disease mortalityPublic health actionFireMortality rateUnited StatesDisease mortalityHealth actionsMortality outcomesAdvancing patient-centric care: integrating patient reported outcomes for tolerability assessment in early phase clinical trials – insights from an expert virtual roundtable
Yap C, Aiyegbusi O, Alger E, Basch E, Bell J, Bhatnagar V, Cella D, Collis P, Dueck A, Gilbert A, Gnanasakthy A, Greystoke A, Hansen A, Kamudoni P, Kholmanskikh O, King-Kallimanis B, Krumholz H, Minchom A, O'Connor D, Petrie J, Piccinin C, Rantell K, Rauz S, Retzer A, Rizk S, Wagner L, Sasseville M, Seymour L, Weber H, Wilson R, Calvert M, Peipert J. Advancing patient-centric care: integrating patient reported outcomes for tolerability assessment in early phase clinical trials – insights from an expert virtual roundtable. EClinicalMedicine 2024, 76: 102838. PMID: 39386161, PMCID: PMC11462221, DOI: 10.1016/j.eclinm.2024.102838.Peer-Reviewed Original ResearchEarly phase trialsPatient-reported outcomesPhase trialsInvestigator-reported adverse eventsEarly phase clinical trialsTolerability assessmentsPhase clinical trialsIntegration of patient-reported outcomesAdverse eventsClinical trialsPatient-centred careDosing decisionsDiverse clinical areasTherapy risksLaboratory assessmentPatientsTrialsPatient advocatesClinical areasOutcomesSafety alertsPharmaceutical representativesVirtual roundtableRiskUse of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurementsClass I Recalls of Cardiovascular Devices Between 2013 and 2022 : A Cross-Sectional Analysis.
See C, Mooghali M, Dhruva S, Ross J, Krumholz H, Kadakia K. Class I Recalls of Cardiovascular Devices Between 2013 and 2022 : A Cross-Sectional Analysis. Annals Of Internal Medicine 2024 PMID: 39284187, DOI: 10.7326/annals-24-00724.Peer-Reviewed Original ResearchCross-sectional studyCross-sectional analysisAdverse health consequencesPatient safetyClinical testingClass IHealth consequencesClinical evidenceFDA summariesPostapproval studiesDecision summariesFood and Drug AdministrationU.S. Food and Drug AdministrationEnd-point selectionPremarket approvalMultiple class IClinical studiesPostmarketing surveillanceSummaryDrug AdministrationMedical device recall databaseRecallPatientsFDAPostmarketingCardiovascular Disease Risk Factor Control Following Release From Carceral Facilities: A Cross-Sectional Study.
Aminawung J, Puglisi L, Roy B, Horton N, Elumn J, Lin H, Bibbins-Domingo K, Krumholz H, Wang E. Cardiovascular Disease Risk Factor Control Following Release From Carceral Facilities: A Cross-Sectional Study. Journal Of The American Heart Association 2024, 13: ejaha2024035683t. PMID: 39248257, DOI: 10.1161/jaha.124.035683.Peer-Reviewed Original ResearchConceptsUncontrolled CVD risk factorsCardiovascular disease risk factor controlCVD risk factorsRisk factor controlFactor controlRisk factorsSocial determinant of cardiovascular healthCardiovascular diseaseProspective cohort study of individualsDeterminants of cardiovascular healthPublic health prevention effortsCardiovascular disease risk factorsCohort study of individualsHealth prevention effortsCross-sectional studyProspective cohort studyCarceral facilitiesCorrectional facilitiesSocial determinantsTailored interventionsTraditional risk factorsStudy of individualsAdversity scorePerceived stressCardiovascular healthCause-Specific Mortality Rates Among the US Black Population
Arun A, Caraballo C, Sawano M, Lu Y, Khera R, Yancy C, Krumholz H. Cause-Specific Mortality Rates Among the US Black Population. JAMA Network Open 2024, 7: e2436402. PMID: 39348122, PMCID: PMC11443349, DOI: 10.1001/jamanetworkopen.2024.36402.Peer-Reviewed Original Research