2025
Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning
Holste G, Oikonomou E, Tokodi M, Kovács A, Wang Z, Khera R. Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning. JAMA 2025, 334 PMID: 40549400, PMCID: PMC12186137, DOI: 10.1001/jama.2025.8731.Peer-Reviewed Original ResearchMultitask deep learningAI systemsDiagnostic classification tasksClassification taskDeep learningArtificial intelligenceArea under the receiver operating characteristic curveYale New Haven Health SystemTransthoracic echocardiography studyTransthoracic echocardiographyVentricular systolic dysfunctionParameter estimation taskSystolic dysfunctionDiagnosis tasksEchocardiographic videosRight ventricular systolic dysfunctionLeft ventricular ejection fractionAI predictionsEstimation taskVentricular ejection fractionSevere aortic stenosisManual reportingReceiver operating characteristic curveTaskClinical workflowSymptom Trajectories After COVID Hospitalization and Risk Factors for Symptom Burden in Older Persons: a Longitudinal Cohort Study
Pan Y, Cho G, Geda M, Gill T, Cohen A, Ferrante L, Hajduk A, Miner B. Symptom Trajectories After COVID Hospitalization and Risk Factors for Symptom Burden in Older Persons: a Longitudinal Cohort Study. The Journals Of Gerontology Series A 2025, glaf132. PMID: 40512177, DOI: 10.1093/gerona/glaf132.Peer-Reviewed Original ResearchHigh symptom burdenAssociated with high symptom burdenSymptom burdenGeriatric conditionsPsychosocial factorsOlder personsOlder adultsEdmonton Symptom Assessment SystemCommunity-dwelling older adultsSymptom trajectoriesModified Edmonton Symptom Assessment SystemYale New Haven Health SystemPost-COVID symptomsEvaluate symptom burdenImpairment of physical functionFemale sexMonths post-dischargeRisk factorsLongitudinal cohort studyMultinomial logistic regressionIdentified risk factorsPhysical functionHealth systemSocial supportPost-dischargeArtificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Dhingra L, Aminorroaya A, Pedroso A, Khunte A, Sangha V, McIntyre D, Chow C, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. JAMA Cardiology 2025, 10: 574-584. PMID: 40238120, PMCID: PMC12004248, DOI: 10.1001/jamacardio.2025.0492.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFNew-onset HFHarrell's C-statisticProspective population-based cohortUK Biobank (UKBBrazilian Longitudinal StudyELSA-Brasil participantsC-statisticPopulation-based cohortIntegrated discrimination improvementReclassification improvementRisk of deathUKB participantsHealth systemRetrospective cohort studyDiscrimination improvementMain OutcomesLeft ventricular systolic dysfunctionHF riskUKBCohort studySingle-lead ECGIndependent of ageCancer genetics evaluation among individuals at risk for Lynch syndrome across all qualifying indications.
Singh V, Chen G, Sena A, Rafter T, Xicola R, Sharbatji M, Liu J, Brown Q, Brierley K, Healy C, Hughes M, Kashyap N, Llor X. Cancer genetics evaluation among individuals at risk for Lynch syndrome across all qualifying indications. Journal Of Clinical Oncology 2025, 43: 10616-10616. DOI: 10.1200/jco.2025.43.16_suppl.10616.Peer-Reviewed Original ResearchLynch syndromeInherited cancer syndromeFamily historyPersonal historyCancer syndromesFamily history of colorectal cancerColorectal cancerPersonal history of cancerFamily history of cancerYale New Haven Health SystemLS-related cancersGenetic testingLS-associated cancersCancer genetic evaluationAt-riskIdentification of at-risk individualsAt-risk individualsPathogenic variantsLS cancersComparison of categorical variablesIndividuals at-riskEO-CRCPearson chi-squareHealth systemDescriptive statisticsComputational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference
Thangaraj P, Oikonomou E, Dhingra L, Aminorroaya A, Jayaram R, Suchard M, Khera R. Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference. Circulation Cardiovascular Quality And Outcomes 2025, 18: e011306. PMID: 40261065, PMCID: PMC12203226, DOI: 10.1161/circoutcomes.124.011306.Peer-Reviewed Original ResearchConceptsElectronic health record patientElectronic health recordsDistance metricRandomized clinical trialsElectronic health record dataMachine learning methodsYale New Haven Health SystemElectronic health record cohortRandomized clinical trial participantsLearning methodsHeart failureClinical trial participationTOPCAT participantsReal worldMultidimensional metricRCT participantsHealth recordsTreatment effectsHealth systemCharacteristics of patientsRandomized clinical trial cohortsTrial participantsMetricsUnited StatesNovel statisticEstimated Effectiveness of Nirsevimab Against Respiratory Syncytial Virus
Xu H, Aparicio C, Wats A, Araujo B, Pitzer V, Warren J, Shapiro E, Niccolai L, Weinberger D, Oliveira C. Estimated Effectiveness of Nirsevimab Against Respiratory Syncytial Virus. JAMA Network Open 2025, 8: e250380. PMID: 40063022, PMCID: PMC11894488, DOI: 10.1001/jamanetworkopen.2025.0380.Peer-Reviewed Original ResearchConceptsRSV-positive casesCase-control studyRSV infectionLRTI-associated hospitalizationsWeeks postimmunizationLong-acting monoclonal antibodiesTest-negative case-control studyClinical settingRespiratory syncytial virusMultivariate logistic regressionYale New Haven Health SystemRSV diseaseEmergency department dataState immunization registryRSV seasonSyncytial virusNirsevimabPolymerase chain reactionClinical trialsLRTIInfantsPotential confoundersMonoclonal antibodiesBroader outcomesDisease severityEffect of Sodium-Glucose Cotransporter-2 Inhibitors on the Progression of Aortic Stenosis
Shah T, Zhang Z, Shah H, Fanaroff A, Nathan A, Parise H, Lutz J, Sugeng L, Bellumkonda L, Redfors B, Omerovic E, Petrie M, Vora A, Fiorilli P, Kobayashi T, Ahmad Y, Forrest J, Giri J, Herrmann H, Lansky A. Effect of Sodium-Glucose Cotransporter-2 Inhibitors on the Progression of Aortic Stenosis. JACC Cardiovascular Interventions 2025, 18: 738-748. PMID: 39985508, DOI: 10.1016/j.jcin.2024.11.036.Peer-Reviewed Original ResearchSodium-glucose cotransporter-2 inhibitorsCotransporter-2 inhibitorsAortic stenosisEffects of sodium-glucose cotransporter-2 inhibitorsProgression of aortic stenosisBaseline AS severityAssociated with slower progressionEchocardiographic follow-upRetrospective electronic medical record dataAortic valve sclerosisDisease-related morbidityChronic kidney diseaseSGLT2i usageRates of diabetesSevere ASAS severityEjection fractionFollow-upPrimary outcomeGt;1 yearKidney diseaseMedical record dataYale New Haven Health SystemPatientsObservational studyArtificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
Oikonomou E, Vaid A, Holste G, Coppi A, McNamara R, Baloescu C, Krumholz H, Wang Z, Apakama D, Nadkarni G, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health 2025, 7: e113-e123. PMID: 39890242, PMCID: PMC12084816, DOI: 10.1016/s2589-7500(24)00249-8.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemPoint-of-care ultrasonographyMount Sinai Health SystemTransthyretin amyloid cardiomyopathyArtificial intelligenceHealth systemAmyloid cardiomyopathyHypertrophic cardiomyopathyRetrospective cohort of individualsCardiomyopathy casesTesting artificial intelligenceConvolutional neural networkSinai Health SystemCohort of individualsOpportunistic screeningHypertrophic cardiomyopathy casesMulti-labelPositive screenAI frameworkEmergency departmentMortality riskNeural networkLoss functionCardiac ultrasonographyAugmentation approachEvaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems
Aminorroaya A, Dhingra L, Oikonomou E, Khera R. Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems. Circulation Genomic And Precision Medicine 2025, 18: e004632. PMID: 39846171, PMCID: PMC11835527, DOI: 10.1161/circgen.124.004632.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemHealth systemVanderbilt University Medical CenterHealth system electronic health recordUniversity Medical CenterCoronary Artery Risk DevelopmentMulti-Ethnic Study of AtherosclerosisElectronic health recordsMedical CenterUS health systemHealth system patientsAssociated with significantly higher oddsMulti-Ethnic StudyUS-based cohortStudy of AtherosclerosisSignificantly higher oddsHealth recordsUK BiobankAtherosclerosis RiskRisk DevelopmentHigher oddsElevated Lp(aUniversal screeningSystem patientsStudy cohortA failure to launch: blood pressure control after stroke in a regional health system
Forman R, Xin X, Kim C, Kernan W, Sheth K, Krumholz H, de Havenon A, Spatz E, Lu Y. A failure to launch: blood pressure control after stroke in a regional health system. Journal Of Hypertension 2025, 43: 715-718. PMID: 39995224, PMCID: PMC12046530, DOI: 10.1097/hjh.0000000000003961.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemRegional health systemHealth systemSystolic blood pressureDiastolic blood pressureMonths post strokeAverage proportion of patientsBP controlPost strokeBlood pressureProfessional visitsPrimary outcomeBlood pressure controlProportion of patientsAverage proportionVisitsSBP valuesEpic systemPressure controlStrokePatientsGlobal budgetHeart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. European Heart Journal 2025, 46: 1044-1053. PMID: 39804243, PMCID: PMC12086686, DOI: 10.1093/eurheartj/ehae914.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFUK BiobankHF riskBrazilian Longitudinal Study of Adult HealthLongitudinal Study of Adult HealthBrazilian Longitudinal StudyRisk of new-onset HFPooled Cohort EquationsPrimary HF hospitalizationsHigher HF riskHarrell's C-statisticRisk of deathNew-onset HFCohort EquationsHealth systemComprehensive clinical evaluationAdult healthHeart failureIncident HFHF hospitalizationBaseline HFC-statisticPrevent HF
2024
Integrating the Airway Lead structure into a large healthcare system to appraise the landscape of airway management resources
Cormier N, Buckley R, Rosenblatt W. Integrating the Airway Lead structure into a large healthcare system to appraise the landscape of airway management resources. JCA Advances 2024, 1: 100075. DOI: 10.1016/j.jcadva.2024.100075.Peer-Reviewed Original ResearchYale New Haven Health SystemHealth systemHealthcare systemQuality improvement surveyAirway equipmentIdentified key stakeholdersAirway managementAirway management protocolMedical intensive care unitAirway management deviceHospital campusEmergency departmentBedside cliniciansKey stakeholdersSupraglottic airwayImprovement surveyIntensive care unitWaveform capnographyAids cliniciansPersonnel resourcesCare unitHealthcareSurgical facilitiesCliniciansManagement leadersDigital Elder Abuse Intervention for Early Detection of Abuse in Older Adults Living with Dementia
Abujarad F, Edwards C, Neugroschl J, Hwang U, Marottoli R. Digital Elder Abuse Intervention for Early Detection of Abuse in Older Adults Living with Dementia. Alzheimer's & Dementia 2024, 20: e092332. PMCID: PMC11713709, DOI: 10.1002/alz.092332.Peer-Reviewed Original ResearchElder abuseEmergency departmentSelf-reportYale New Haven Health SystemDigital health interventionsGeriatric memory clinicPrimary care settingEarly detection of abuseEvidence-informed interventionsCognitively intact participantsMontreal Cognitive AssessmentSevere cognitive impairmentCognitive abilities of participantsHigh-risk populationIncreased self-reportsPublic health problemCare settingsED settingPLWDHealth interventionsMemory clinicHealth systemOlder AdultsPost-survey questionsSelf-reported abuseImplementation of American Society of Hematology (ASH) Neuro-Related Guidelines at a Sickle Cell Center: How Are We Doing?
Afranie-Sakyi J, Karimi M, Bozzo J, Cole J, Van Doren L, Calhoun C. Implementation of American Society of Hematology (ASH) Neuro-Related Guidelines at a Sickle Cell Center: How Are We Doing? Blood 2024, 144: 5009-5009. DOI: 10.1182/blood-2024-201816.Peer-Reviewed Original ResearchYale New Haven Health SystemHistory of strokeCognitive screeningGuideline publicationHealth systemSilent cerebral infarctionCognitive disordersImplementation ratePredictors of screeningSickle cell diseaseRates of screeningMood disordersAmerican Society of HematologyPrevalence of cognitive disordersDetect silent cerebral infarctsDevelopment of interventionsCognitive impairmentManual chart reviewICD-10 diagnosisEmergency room visitsDiagnosis of sickle cell diseaseSickle Cell CenterAmerican Society of Hematology guidelinesOutpatient encountersMRI screeningScreening for Cognitive Impairment and Depression in Sickle Cell Disease: How Are We Doing?
Afranie-Sakyi J, Karimi M, Bozzo J, Cole J, Van Doren L, Calhoun C. Screening for Cognitive Impairment and Depression in Sickle Cell Disease: How Are We Doing? Blood 2024, 144: 2249-2249. DOI: 10.1182/blood-2024-211906.Peer-Reviewed Original ResearchPatient Health QuestionnaireCognitive screeningAmerican Society of HematologyPsychiatric disordersSickle cell diseaseMood screeningMood disordersCognitive disordersDisorder screeningYale New Haven Health SystemPredictors of screeningDisease-modifying therapiesICD-10 diagnosisManual chart reviewImplementation of recommendationsEmergency room visitsDiagnosis of sickle cell diseaseHistory of strokeCell diseaseScreening ratesAnxious symptomsHealthcare teamPromote screeningNeuropsychological testsCognitive domainsValidating International Classification of Diseases Code 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 10th Revision algorithms for accurate identification of pulmonary embolism. Journal Of Thrombosis And Haemostasis 2024, 23: 556-564. 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 algorithmEarly Warning Scores With and Without Artificial Intelligence
Edelson D, Churpek M, Carey K, Lin Z, Huang C, Siner J, Johnson J, Krumholz H, Rhodes D. Early Warning Scores With and Without Artificial Intelligence. JAMA Network Open 2024, 7: e2438986. PMID: 39405061, PMCID: PMC11544488, DOI: 10.1001/jamanetworkopen.2024.38986.Peer-Reviewed Original ResearchConceptsEarly Warning ScoreWarning ScoreCohort studyYale New Haven Health SystemClinical deterioration eventsHigh-risk thresholdHealth systemRetrospective cohort studyPatient encountersDeteriorating patientsOverall PPVMain OutcomesInpatient encountersEDI scoresHospital encountersDeterioration eventsClinical deteriorationIntensive care unitEarly warning toolCare unitDecision support toolArtificial intelligenceScoresReceiver operating characteristic curveNEWS2Use 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 measurementsBarriers to Optimal Clinician Guideline Adherence in Management of Markedly Elevated Blood Pressure
Lu Y, Arowojolu O, Qiu X, Liu Y, Curry L, Krumholz H. Barriers to Optimal Clinician Guideline Adherence in Management of Markedly Elevated Blood Pressure. JAMA Network Open 2024, 7: e2426135. PMID: 39106065, PMCID: PMC11304113, DOI: 10.1001/jamanetworkopen.2024.26135.Peer-Reviewed Original ResearchConceptsBarriers to guideline adherenceElectronic health recordsGuideline adherenceClinician adherenceEHR dataElevated blood pressureHypertension managementAnalysis of EHR dataYale New Haven Health SystemSevere hypertensionClinical practice guidelinesAdherence scenariosQualitative content analysisPublic health challengeThematic saturationHealth recordsHealth systemBlood pressureThematic analysisTargeted interventionsManagement of severe hypertensionQualitative studyHealth challengesPractice guidelinesPatient outcomesOpioid prescribing trends and pain scores among adult patients with cancer in a large health system.
Baum L, Soulos P, KC M, Jeffery M, Ruddy K, Lerro C, Lee H, Graham D, Liberatore M, Rivera D, Leapman M, Jairam V, Dinan M, Gross C, Park H. Opioid prescribing trends and pain scores among adult patients with cancer in a large health system. Journal Of Clinical Oncology 2024, 42: 11059-11059. DOI: 10.1200/jco.2024.42.16_suppl.11059.Peer-Reviewed Original ResearchDocumented painHealth systemOpioid prescribingOpioid prescriptionsOpioid prescribing trendsYale New Haven Health SystemPrescribing trendsMetastatic cancerDocumented pain scoresUS health systemOpioid usePain scoresAdult patientsCalculated predicted probabilitiesFlowsheet dataRetrospective cohort studyRelated harmSolid tumor malignanciesCohort studyCancer painPrescribingContext of cancer treatmentStudy criteriaPainSurgery cohort
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