2025
A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.
Zhou X, Dhingra L, Aminorroaya A, Adejumo P, Khera R. A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model. AMIA Annual Symposium Proceedings 2025, 2024: 1332-1339. PMID: 40417570.Peer-Reviewed Original ResearchConceptsCommon Data ModelElectronic health recordsOMOP Common Data ModelSchema mappingsMapping electronic health recordData modelTransformer-based deep learning modelsNatural language processing approachEnd-to-endDeep learning modelsHealth recordsEnhance interoperabilityTransformation pipelineLearning modelsOMOPProcessing approachSchemaStandard conceptsDiverse healthcare systemsInteroperabilityLarge-scaleStandard mapDatasetSoftwareHealthcare systemComputational 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 statisticEnsemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Shankar S, Coppi A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD. Journal Of The American College Of Cardiology 2025, 85: 1302-1313. PMID: 40139886, PMCID: PMC12199746, DOI: 10.1016/j.jacc.2025.01.030.Peer-Reviewed Original ResearchConceptsStructural heart diseaseYale-New Haven HospitalTransthoracic echocardiogramRisk stratificationHeart failureLeft-sided valvular diseaseSevere left ventricular hypertrophyLeft ventricular ejection fractionReceiver-operating characteristic curveVentricular ejection fractionLeft ventricular hypertrophyHeart disease screeningELSA-BrasilEnsemble deep learning algorithmRisk of deathConvolutional neural network modelEjection fractionEnsemble deep learning approachVentricular hypertrophyDeep learning algorithmsNew Haven HospitalDeep learning approachValvular diseaseNeural network modelClinical cohortUso da Inteligência Artificial Aplicada ao Eletrocardiograma para Diagnóstico de Disfunção Sistólica Ventricular Esquerda
de Santana W, Pinto M, Barreto S, Foppa M, Giatti L, Khera R, Ribeiro A. Uso da Inteligência Artificial Aplicada ao Eletrocardiograma para Diagnóstico de Disfunção Sistólica Ventricular Esquerda. Arquivos Brasileiros De Cardiologia 2025, 122: e20240740. PMID: 40396866, PMCID: PMC12108124, DOI: 10.36660/abc.20240740.Peer-Reviewed Original ResearchConceptsLeft ventricular systolic dysfunctionLeft ventricular ejection fractionNegative predictive valueDiagnostic odds ratioPositive predictive valueHeart failureDetect left-ventricular systolic dysfunctionPredictive valueVentricular systolic dysfunctionVentricular ejection fractionNegative likelihood ratioPositive likelihood ratioDiagnostic accuracy cross-sectional studyLikelihood ratioCross-sectional studySystolic dysfunctionEjection fractionEvaluating HFAUC-ROCElectrocardiographic alterationsOdds ratioEchocardiogramROC curveScreening toolElectrocardiogramEffects of Tirzepatide in Type 2 Diabetes Individual Variation and Relationship to Cardiometabolic Outcomes
Aminorroaya A, Oikonomou E, Biswas D, Jastreboff A, Khera R. Effects of Tirzepatide in Type 2 Diabetes Individual Variation and Relationship to Cardiometabolic Outcomes. Journal Of The American College Of Cardiology 2025, 85: 1858-1872. PMID: 40368575, PMCID: PMC12186526, DOI: 10.1016/j.jacc.2025.03.516.Peer-Reviewed Original ResearchConceptsElevated body mass indexCardiometabolic abnormalitiesBody mass indexOdds of elevated body mass indexType 2 diabetesIndividual participant data meta-analysisMass indexParticipant data meta-analysisOdds of MetSCardiometabolic risk factorsComponents of metabolic syndromeData meta-analysisHigh-density lipoprotein cholesterolCardiometabolic healthStudy design differencesMixed-effects modelsBaseline usePhase 3 randomized clinical trialSodium-glucose cotransporter 2 inhibitorsOddsStudy outcomesEffects of tirzepatideMeta-analysisRisk factorsClinical subpopulationsControversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management
Armoundas A, Ahmad F, Attia Z, Doudesis D, Khera R, Kyriakoulis K, Stergiou G, Tang W. Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management. Hypertension 2025, 82: 929-944. PMID: 40091745, PMCID: PMC12094096, DOI: 10.1161/hypertensionaha.124.22349.Peer-Reviewed Original ResearchConceptsArtificial intelligenceHypertension diagnosisBlood pressure elevationRelationship to cardiovascular diseaseManagement of hypertensionLong-term managementArtificial intelligence-based solutionsPressure elevationPublic health challengeHypertensionArtificial intelligence scienceComplex pathogenesisClinical implementationCardiovascular diseaseState-of-artDiagnosisData-driven approachHypertension managementIntelligence scienceClinical adoptionArtificial 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 cohort
2024
Impact of the COVID-19 pandemic on hospital-based heart failure care in New South Wales, Australia: a linked data cohort study
McIntyre D, Quintans D, Kazi S, Min H, He W, Marschner S, Khera R, Nassar N, Chow C. Impact of the COVID-19 pandemic on hospital-based heart failure care in New South Wales, Australia: a linked data cohort study. BMC Health Services Research 2024, 24: 1364. PMID: 39516863, PMCID: PMC11545568, DOI: 10.1186/s12913-024-11840-0.Peer-Reviewed Original ResearchConceptsHeart failure careNew South WalesHospital admissionHealth service utilisationAdministrative health recordsPrimary diagnosis of heart failureData cohort studyRate of admissionPre-pandemicHealth of patientsSouth WalesCOVID-19 pandemicHospital utilisationService utilisationHealth recordsED presentationsMortality dataDiagnosis of heart failureCOVID-19 burdenEmergency departmentCohort studyPrimary diagnosisData collectionCareAustralian dataNatural 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 groupsRacial 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, PMCID: PMC11581672, 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 ageReviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest
Silva G, Khera R, Schwamm L, Acampa M, Adelman E, Boltze J, Broderick J, Brodtmann A, Christensen H, Dalli L, Duncan K, Elgendy I, Ergul A, Goldstein L, Hinkle J, Johansen M, Jood K, Kasner S, Levine S, Li Z, Lip G, Marsh E, Muir K, Ospel J, Pera J, Quinn T, Räty S, Ranta A, Richards L, Romero J, Willey J, Hillis A, Veerbeek J. Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest. Stroke 2024, 55: 2573-2578. PMID: 39224979, PMCID: PMC11529699, DOI: 10.1161/strokeaha.124.045012.Peer-Reviewed Original ResearchConceptsArtificial intelligenceEditorial board membersAuthor typeTraditional peer reviewLanguage modelIntelligent contentAuthor attributionGeneral textAI expertiseHuman authorityImproved accuracyAuthor's identityAuthor's manuscriptScientific journalsEssay contestPeer reviewPerception of qualityAuthorshipNature of authorshipIntelligenceLLMScientific writingScientific essayEssay qualityEssayCause-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.Commentaries, Editorials and LettersComparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes A Multinational, Federated Analysis of LEGEND-T2DM
Khera R, Aminorroaya A, Dhingra L, Thangaraj P, Pedroso Camargos A, Bu F, Ding X, Nishimura A, Anand T, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr D, Duarte-Salles T, DuVall S, Falconer T, French T, Hanchrow E, Kaur G, Lau W, Li J, Li K, Liu Y, Lu Y, Man K, Matheny M, Mathioudakis N, McLeggon J, McLemore M, Minty E, Morales D, Nagy P, Ostropolets A, Pistillo A, Phan T, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager S, Simon K, Viernes B, Yang J, Yin C, You S, Zhou J, Ryan P, Schuemie M, Krumholz H, Hripcsak G, Suchard M. Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes A Multinational, Federated Analysis of LEGEND-T2DM. Journal Of The American College Of Cardiology 2024, 84: 904-917. PMID: 39197980, PMCID: PMC12045554, DOI: 10.1016/j.jacc.2024.05.069.Peer-Reviewed Original ResearchConceptsGLP-1 RAsSecond-line agentsGLP-1Antihyperglycemic agentsCardiovascular diseaseMACE riskGlucagon-like peptide-1 receptor agonistsSodium-glucose cotransporter 2 inhibitorsPeptide-1 receptor agonistsDipeptidyl peptidase-4 inhibitorsEffects of SGLT2isType 2 diabetes mellitusPeptidase-4 inhibitorsAdverse cardiovascular eventsCox proportional hazards modelsRandom-effects meta-analysisCardiovascular risk reductionTarget trial emulationProportional hazards modelAI-enabled diagnosis from an electrocardiogram image: the next frontier of innovation in a century-old technology
Khera R. AI-enabled diagnosis from an electrocardiogram image: the next frontier of innovation in a century-old technology. Heart 2024, 110: heartjnl-2024-324299. PMID: 39048290, PMCID: PMC11328242, DOI: 10.1136/heartjnl-2024-324299.Commentaries, Editorials and LettersTransforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice JACC State-of-the-Art Review
Khera R, Oikonomou E, Nadkarni G, Morley J, Wiens J, Butte A, Topol E. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice JACC State-of-the-Art Review. Journal Of The American College Of Cardiology 2024, 84: 97-114. PMID: 38925729, PMCID: PMC12204085, DOI: 10.1016/j.jacc.2024.05.003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning
Nowroozilarki Z, Huang S, Khera R, Mortazavi B. ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039094, DOI: 10.1109/embc53108.2024.10782909.Peer-Reviewed Original ResearchConceptsContrastive learningPretraining frameworkEmbedding spaceComprehensive labeled datasetSupervised contrastive learningReal-time monitoring systemLatent representationLabeled dataECG sensorAbnormality detectionWearable devicesECG dataElectrocardiogram dataBalanced accuracyBiomedical waveformsData pointsMonitoring systemLearningFrameworkPerformance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study
Camargos A, Barreto S, Brant L, Ribeiro A, Dhingra L, Aminorroaya A, Bittencourt M, Figueiredo R, Khera R. Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study. Open Heart 2024, 11: e002762. PMID: 38862252, PMCID: PMC11168182, DOI: 10.1136/openhrt-2024-002762.Peer-Reviewed Original ResearchConceptsPooled Cohort EquationsELSA-BrasilRisk scoreCardiovascular diseaseCVD eventsCommunity-based cohort studyArea under the receiver operating characteristic curveCVD risk scoreELSA-Brasil studyIncident CVD eventsMiddle-income countriesAdjudicated CVD eventsCardiovascular disease riskCVD scoreCohort EquationsNational guidelinesRisk stratification scoresWhite womenAge/sex groupsCohort studyProspective cohortLMICsSex/race groupsHigher incomeRisk discriminationData Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association
Armoundas A, Ahmad F, Bennett D, Chung M, Davis L, Dunn J, Narayan S, Slotwiner D, Wiley K, Khera R, Care P. Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation Genomic And Precision Medicine 2024, 17: e000095. PMID: 38779844, PMCID: PMC11703599, DOI: 10.1161/hcg.0000000000000095.Peer-Reviewed Original ResearchConceptsData interoperabilityDeployment of platformsInteroperability frameworkSoftware applicationsData integrationWearable devicesData ecosystemInteroperabilityMonitoring of cardiovascular diseasesQuality of dataDiverse health systemsClinical workflowTransform health careDataScientific statementCardiovascular diseaseClinical contentAmerican Heart AssociationCaregivers' accessHealth systemHealth careIntroducing the JAMA Summit
Bibbins-Domingo K, Angus D, Park H, Lewis R, Khera R, Zeis J, Flanagin A, Curfman G. Introducing the JAMA Summit. JAMA 2024, 331: 1451-1451. DOI: 10.1001/jama.2024.5570.Commentaries, Editorials and Letters
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply