2025
Artificial 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 ageArtificial 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 approachA 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
Validating 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 outcomes
2023
Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography
Oikonomou E, Holste G, Mcnamara R, Velazquez E, Nadkarni G, Ouyang D, Krumholz H, Wang Z, Khera R. Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography. European Heart Journal 2023, 44: ehad655.040. DOI: 10.1093/eurheartj/ehad655.040.Peer-Reviewed Original ResearchLeft ventricular ejection fractionSevere aortic stenosisAortic stenosisAS progressionAV VmaxTransthoracic echocardiographyYale New Haven Health SystemBaseline left ventricular ejection fractionAortic stenosis progressionModerate aortic stenosisRetrospective cohort studyVentricular ejection fractionTwo-dimensional echocardiographyMean rateModerate ASAS severityCohort studyEjection fractionPatient sexStenosis progressionTTE studiesEligible participantsSerial monitoringSpecialized centersTimely diagnosisDeveloping 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 ResearchConceptsElectronic 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 reportsAlgorithmQuantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study
Lu Y, Linderman G, Mahajan S, Liu Y, Huang C, Khera R, Mortazavi B, Spatz E, Krumholz H. Quantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study. Circulation Cardiovascular Quality And Outcomes 2023, 16: e009258. PMID: 36883456, DOI: 10.1161/circoutcomes.122.009258.Peer-Reviewed Original ResearchConceptsRetrospective cohort studyBlood pressure valuesPatient characteristicsReal-world settingCohort studyPatient subgroupsYale New Haven Health SystemMean body mass indexSystolic blood pressure valuesBlood pressure visitHistory of hypertensionCoronary artery diseaseManagement of patientsMultivariable linear regression modelsBlood pressure readingsBody mass indexPatient-level measuresBlood pressure variationAbsolute standardized differencesNon-Hispanic whitesAntihypertensive medicationsReal-world practiceVisit variabilityArtery diseaseRegression modelsDeveloping an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records
Lu Y, Du C, Khidir H, Caraballo C, Mahajan S, Spatz E, Curry L, Krumholz H. Developing an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records. Circulation Cardiovascular Quality And Outcomes 2023, 16: e009453. PMID: 36727515, DOI: 10.1161/circoutcomes.122.009453.Peer-Reviewed Original ResearchConceptsPersistent hypertensionElectronic health recordsBlood pressureHealth recordsPharmacologic agentsPrescribed treatmentYale New Haven Health SystemTreatment planAdditional pharmacologic agentsAntihypertensive treatment intensificationConsecutive outpatient visitsElevated blood pressurePersistence of hypertensionElectronic health record dataHealth record dataEligible patientsTreatment intensificationChart reviewHispanic patientsOutpatient visitsMean agePharmacological treatmentConventional content analysisHypertensionClinician notes
2022
A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations
Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. Npj Digital Medicine 2022, 5: 27. PMID: 35260762, PMCID: PMC8904579, DOI: 10.1038/s41746-022-00570-4.Peer-Reviewed Original ResearchCOVID-19 hospitalizationMayo ClinicDiagnosis codesCOVID-19 diagnosisPositive SARS-CoV-2 PCRYale New Haven Health SystemPositive SARS-CoV-2 testSARS-CoV-2 infectionSARS-CoV-2 PCRSARS-CoV-2 testCOVID-19Higher inhospital mortalitySARS-CoV2 infectionElectronic health record dataICD-10 diagnosisPositive laboratory testsHealth record dataInhospital mortalityAdditional patientsAntigen testSecondary diagnosisPrincipal diagnosisMulticenter evaluationPositive testComputable phenotype definitions
2020
Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure
Lu Y, Huang C, Mahajan S, Schulz WL, Nasir K, Spatz ES, Krumholz HM. Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure. Journal Of The American Heart Association 2020, 9: e015033. PMID: 32200730, PMCID: PMC7428633, DOI: 10.1161/jaha.119.015033.Peer-Reviewed Original ResearchConceptsDiastolic blood pressureSystolic blood pressureElevated blood pressureBlood pressureElectronic health recordsPopulation health surveillanceHealth recordsYale New Haven Health SystemHealth surveillanceHealth systemPatterns of patientsLarge health systemUsual careOutpatient encountersControl ratePatientsCare patternsPopulation healthMonthsHgSurveillancePrevalenceRecordsVisitsCare
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