Featured Publications
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTrainingDetection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha V, Nargesi A, Dhingra L, Khunte A, Mortazavi B, Ribeiro A, Banina E, Adeola O, Garg N, Brandt C, Miller E, Ribeiro A, Velazquez E, Giatti L, Barreto S, Foppa M, Yuan N, Ouyang D, Krumholz H, Khera R. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images. Circulation 2023, 148: 765-777. PMID: 37489538, PMCID: PMC10982757, DOI: 10.1161/circulationaha.122.062646.Peer-Reviewed Original ResearchConceptsLV systolic dysfunctionYale-New Haven HospitalVentricular systolic dysfunctionSystolic dysfunctionLV ejection fractionBrazilian Longitudinal StudyNew Haven HospitalEjection fractionCardiology clinicRegional hospitalLeft ventricular systolic dysfunctionCedars-Sinai Medical CenterAdult Health (ELSA-Brasil) cohortIndividualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials
Oikonomou EK, Spatz ES, Suchard MA, Khera R. Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials. The Lancet Digital Health 2022, 4: e796-e805. PMID: 36307193, PMCID: PMC9768739, DOI: 10.1016/s2589-7500(22)00170-4.Peer-Reviewed Original ResearchConceptsSystolic blood pressure controlBlood pressure controlIntensive systolic blood pressure controlType 2 diabetesPressure controlCardiovascular benefitsClinical trialsMajor adverse cardiovascular eventsFirst major adverse cardiovascular eventLarge randomised clinical trialsACCORD-BP trialAdverse cardiovascular eventsRandomised clinical trialsSystolic blood pressureCox regression analysisTreatment effectsHazard ratio estimatesACCORD-BPBP trialCardiovascular eventsBlood pressurePrimary outcomeStandard treatmentBaseline variablesIndex patients
2025
The emerging role of AI in transforming cardiovascular care
Croon P, Pedroso A, Khera R. The emerging role of AI in transforming cardiovascular care. Future Cardiology 2025, ahead-of-print: 1-4. PMID: 40248957, DOI: 10.1080/14796678.2025.2492973.Peer-Reviewed Original ResearchArtificial 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 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 ageDevelopment and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms
Aminorroaya A, Dhingra L, Pedroso A, Shankar S, Coppi A, Khunte A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms. European Heart Journal - Digital Health 2025, ztaf034. DOI: 10.1093/ehjdh/ztaf034.Peer-Reviewed Original ResearchDetectable structural heart diseaseStructural heart diseaseCommunity-based screeningLeft-sided valvular diseaseHeart diseaseELSA-BrasilYale-New Haven HospitalAI-ECG algorithmDeep learning algorithmsPopulation-based cohortSevere LVHEchocardiographic dataPredictive biomarkersHospital-based sitesNew Haven HospitalRisk stratificationValvular diseaseEnsemble deep learning algorithmUK BiobankCommunity hospitalLead I ECGAutomated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models
Vasisht Shankar S, Dhingra L, Aminorroaya A, Adejumo P, Nadkarni G, Xu H, Brandt C, Oikonomou E, Pedroso A, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. European Heart Journal - Digital Health 2025, ztaf030. DOI: 10.1093/ehjdh/ztaf030.Peer-Reviewed Original ResearchEnsemble 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, 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 cohortHarnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care
Aminorroaya A, Biswas D, Pedroso A, Khera R. Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care. Journal Of The Society For Cardiovascular Angiography & Interventions 2025, 4: 102562. PMID: 40230673, PMCID: PMC11993883, DOI: 10.1016/j.jscai.2025.102562.Peer-Reviewed Original ResearchClinical careCommunity-based screening programCare quality outcomesPatient outcomesPatient-focused careHarness artificial intelligenceArtificial intelligencePotential of AIImprove patient outcomesIndividualized clinical careTransform careTransform clinical practiceCardiovascular careScreening programHealth dataQuality outcomesCareClinical workflowClinical tasksAcute coronary syndromeClinical practiceHeart diseaseAI-driven technologiesInterventionAI-enabledAssessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study
Khera R, Sawano M, Warner F, Coppi A, Pedroso A, Spatz E, Yu H, Gottlieb M, Saydah S, Stephens K, Rising K, Elmore J, Hill M, Idris A, Montoy J, O’Laughlin K, Weinstein R, Venkatesh A, Weinstein R, Gottlieb M, Santangelo M, Koo K, Derden A, Gottlieb M, Gatling K, Ahmed Z, Gomez C, Guzman D, Hassaballa M, Jerger R, Kaadan A, Venkatesh A, Spatz E, Kinsman J, Malicki C, Lin Z, Li S, Yu H, Mannan I, Yang Z, Liu M, Venkatesh A, Spatz E, Ulrich A, Kinsman J, Malicki C, Dorney J, Pierce S, Puente X, Salah W, Nichol G, Stephens K, Anderson J, Schiffgens M, Morse D, Adams K, Stober T, Maat Z, O’Laughlin K, Gentile N, Geyer R, Willis M, Zhang Z, Chang G, Lyon V, Klabbers R, Ruiz L, Malone K, Park J, Rising K, Kean E, Chang A, Renzi N, Watts P, Kelly M, Schaeffer K, Grau D, Cheng D, Shutty C, Charlton A, Shughart L, Shughart H, Amadio G, Miao J, Hannikainen P, Elmore J, Wisk L, L’Hommedieu M, Chandler C, Eguchi M, Roldan K, Moreno R, Rodriguez R, Wang R, Montoy J, Kemball R, Chan V, Chavez C, Wong A, Arreguin M, Hill M, Site R, Kane A, Nikonowicz P, Sapp S, Idris A, McDonald S, Gallegos D, Martin K, Saydah S, Plumb I, Hall A, Briggs-Hagen M. Assessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study. Journal Of The American Medical Informatics Association 2025, 32: 784-794. PMID: 40036551, PMCID: PMC12012333, DOI: 10.1093/jamia/ocaf027.Peer-Reviewed Original ResearchElectronic health recordsSelf-report questionnairesSelf-reportHealth conditionsElectronic health record portalsElectronic health record platformsEHR elementsSelf-reported health conditionsElectronic health record dataSelf-reported conditionsAssessment of health conditionEvaluation of health conditionsPrevalence of conditionsPatient portalsTraditional self-reportPrevalence of comorbiditiesHealth recordsEHR dataEHR phenotypesDiagnosis codesHospitalization riskComputable phenotypeNationwide studyCohen's kappaPatient characteristicsSignificance of Coronary Artery Calcifications and Ischemic Electrocardiographic Changes Among Patients Undergoing Myocardial Perfusion Imaging
Kokkinidis D, Kyriakoulis I, Chui P, Agarwal R, Liu Y, Khera R, Sinusas A, Velazquez E, Miller E, Feher A. Significance of Coronary Artery Calcifications and Ischemic Electrocardiographic Changes Among Patients Undergoing Myocardial Perfusion Imaging. JACC Advances 2025, 4: 101618. PMID: 39983619, PMCID: PMC11891677, DOI: 10.1016/j.jacadv.2025.101618.Peer-Reviewed Original ResearchCoronary artery calcificationMyocardial perfusion imagingIschemic ECG changesLow event ratesECG changesNormal perfusionArtery calcificationPerfusion imagingSingle-photon emission computed tomography/computed tomographyStress ECGCAC evaluationPresence of coronary artery calcificationSuspected coronary artery diseaseMedian follow-upEvent ratesIschemic electrocardiographic changesAnalysis to patientsComposite endpoint rateCoronary artery diseaseTomography/computed tomographyPrognostic informationMACE rateFollow-upSubgroup analysisAdverse outcomesRisk of aortic aneurysm or dissection following use of fluoroquinolones: a retrospective multinational network cohort study
Janetzki J, Kim J, Minty E, Lee J, Morales D, Khera R, Kim C, Alshammari T, DuVall S, Matheny M, Falconer T, Kim S, Phan T, Nguyen P, Hsu M, Hsu J, Park R, Man K, Seager S, Van Zandt M, Gilbert J, Ryan P, Schuemie M, Suchard M, Hripcsak G, Pratt N, You S. Risk of aortic aneurysm or dissection following use of fluoroquinolones: a retrospective multinational network cohort study. EClinicalMedicine 2025, 81: 103096. PMID: 39975698, PMCID: PMC11836508, DOI: 10.1016/j.eclinm.2025.103096.Peer-Reviewed Original ResearchUrinary tract infectionRisk of aortic aneurysmTreat urinary tract infectionsOutpatient settingAortic aneurysmIndex dateCohort studyHazard ratioUrinary tract infection treatmentYonsei University College of MedicineRetrospective cohort studyPropensity scoreCox proportional hazards modelsPropensity-matched pairsRandom-effects meta-analysisProportional hazards modelNational Health and Medical Research Council (NHMRCUniversity College of MedicineSystemic fluoroquinolonesTract infectionsNo significant differenceBayesian random-effects meta-analysisFollow-upPrimary outcomeIncreased riskArtificial 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, 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 cohortHeart 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, 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
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers
Dhingra L, Sangha V, Aminorroaya A, Bryde R, Gaballa A, Ali A, Mehra N, Krumholz H, Sen S, Kramer C, Martinez M, Desai M, Oikonomou E, Khera R. A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers. The American Journal Of Cardiology 2024, 237: 35-40. PMID: 39581517, PMCID: PMC11761372, DOI: 10.1016/j.amjcard.2024.11.028.Peer-Reviewed Original ResearchCleveland Clinic FoundationHypertrophic cardiomyopathyMedian follow-up periodHypertrophic cardiomyopathy therapyMonitoring treatment responseFollow-up periodImpact of therapyAtlantic Health SystemLack of improvementOral alternativePost-SRTMedical therapyTreatment responseMulticenter evaluationInterventricular septumPercutaneous reductionMavacamtenTherapyPatientsClinic FoundationPoint-of-care monitoringECGECG imagesScoresHealth systemArtificial Intelligence Applications for Electrocardiography to Define New Digital Biomarkers of Cardiovascular Risk
Sangha V, Khera R. Artificial Intelligence Applications for Electrocardiography to Define New Digital Biomarkers of Cardiovascular Risk. Circulation Cardiovascular Quality And Outcomes 2024, 17: e011483. PMID: 39540286, DOI: 10.1161/circoutcomes.124.011483.Commentaries, Editorials and LettersExpanding artificial intelligence to understudied populations: congenital heart disease as the next frontier
Oikonomou E, Khera R. Expanding artificial intelligence to understudied populations: congenital heart disease as the next frontier. European Heart Journal 2024, 46: 869-871. PMID: 39523016, PMCID: PMC11879170, DOI: 10.1093/eurheartj/ehae737.Commentaries, Editorials and LettersImpact 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 dataValidating 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 algorithm
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