Featured Publications
A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression
Oikonomou E, Holste G, Yuan N, Coppi A, McNamara R, Haynes N, Vora A, Velazquez E, Li F, Menon V, Kapadia S, Gill T, Nadkarni G, Krumholz H, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiology 2024, 9: 534-544. PMID: 38581644, PMCID: PMC10999005, DOI: 10.1001/jamacardio.2024.0595.Peer-Reviewed Original ResearchCardiac magnetic resonanceAortic valve replacementCardiac magnetic resonance imagingAV VmaxSevere ASAortic stenosisCohort studyPeak aortic valve velocityCohort study of patientsAortic valve velocityCohort of patientsTraditional cardiovascular risk factorsAssociated with faster progressionStudy of patientsCedars-Sinai Medical CenterAssociated with AS developmentCardiovascular risk factorsCardiovascular imaging modalitiesIndependent of ageModerate ASEjection fractionEchocardiographic studiesValve replacementRisk stratificationCardiac structureArtificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou E, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. European Heart Journal 2024, 45: 3204-3218. PMID: 38976371, PMCID: PMC11400875, DOI: 10.1093/eurheartj/ehae415.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsTransforming 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, DOI: 10.1016/j.jacc.2024.05.003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsArtificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study.
Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study. MedRxiv 2024 PMID: 38559021, DOI: 10.1101/2024.03.10.24304044.Peer-Reviewed Original ResearchAn explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials
Oikonomou E, Thangaraj P, Bhatt D, Ross J, Young L, Krumholz H, Suchard M, Khera R. An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials. Npj Digital Medicine 2023, 6: 217. PMID: 38001154, PMCID: PMC10673945, DOI: 10.1038/s41746-023-00963-z.Peer-Reviewed Original ResearchSevere aortic stenosis detection by deep learning applied to echocardiography
Holste G, Oikonomou E, Mortazavi B, Coppi A, Faridi K, Miller E, Forrest J, McNamara R, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz H, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. European Heart Journal 2023, 44: 4592-4604. PMID: 37611002, PMCID: PMC11004929, DOI: 10.1093/eurheartj/ehad456.Peer-Reviewed Original ResearchIndividualising 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 patientsA phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.Peer-Reviewed Original ResearchConceptsStable chest painChest painPrimary endpointMajor adverse cardiovascular eventsNon-fatal myocardial infarctionAdverse cardiovascular eventsStudy's primary endpointCoronary artery diseaseClinical trial populationsCox regression modelParticipant-level dataSCOT-HEARTCardiovascular eventsCause mortalityHazard ratioPatients 5Artery diseaseFunctional testingPROMISE trialTrial populationMyocardial infarctionLower incidenceStudy populationPainCollected variablesPerivascular Fat Attenuation Index Stratifies Cardiac Risk Associated With High-Risk Plaques in the CRISP-CT Study
Oikonomou EK, Desai MY, Marwan M, Kotanidis CP, Antonopoulos AS, Schottlander D, Channon KM, Neubauer S, Achenbach S, Antoniades C. Perivascular Fat Attenuation Index Stratifies Cardiac Risk Associated With High-Risk Plaques in the CRISP-CT Study. Journal Of The American College Of Cardiology 2020, 76: 755-757. PMID: 32762910, DOI: 10.1016/j.jacc.2020.05.078.Peer-Reviewed Original ResearchA novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography
Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 2019, 40: 3529-3543. PMID: 31504423, PMCID: PMC6855141, DOI: 10.1093/eurheartj/ehz592.Peer-Reviewed Original ResearchConceptsPerivascular adipose tissueFat attenuation indexCoronary CT angiographyCardiac risk predictionCoronary perivascular adipose tissueMajor adverse cardiac eventsCT angiographyRisk predictionHigh-risk plaque featuresPerivascular fat attenuation indexRadiomic featuresAdverse cardiac eventsConsecutive eligible participantsSCOT-HEART trialTraditional risk stratificationCoronary artery diseaseCoronary calcium scoreStandard coronary CT angiographyAcute myocardial infarctionCoronary inflammationCardiac eventsArtery diseaseCalcium scoreCardiac surgeryMACE predictionNon-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data
Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Centeno E, Thomas S, Herdman L, Kotanidis CP, Thomas KE, Griffin BP, Flamm SD, Antonopoulos AS, Shirodaria C, Sabharwal N, Deanfield J, Neubauer S, Hopewell JC, Channon KM, Achenbach S, Antoniades C. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. The Lancet 2018, 392: 929-939. PMID: 30170852, PMCID: PMC6137540, DOI: 10.1016/s0140-6736(18)31114-0.Peer-Reviewed Original ResearchMeSH KeywordsAdipocytesAdipose TissueAdolescentAdultAgedAged, 80 and overComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseCoronary VesselsFemaleFollow-Up StudiesHumansImaging, Three-DimensionalMaleMiddle AgedPlaque, AtheroscleroticPredictive Value of TestsProportional Hazards ModelsProspective StudiesRisk AssessmentSurvival AnalysisYoung AdultConceptsRight coronary arteryProximal right coronary arteryCardiac mortalityCoronary inflammationCoronary CTACoronary arteryDerivation cohortValidation cohortOutcome dataHealth Research Oxford Biomedical Research CentreCoronary artery disease indexHigh-risk plaque featuresPerivascular fat attenuation indexCoronary artery inflammationFat attenuation indexIntensive secondary preventionCardiovascular risk factorsResidual cardiovascular riskProspective outcome dataMajor coronary arteriesHounsfield unitsLeft circumflex arteryCox regression modelRisk predictionCardiac risk predictionAssessment of Prognostic Value of Left Ventricular Global Longitudinal Strain for Early Prediction of Chemotherapy-Induced Cardiotoxicity
Oikonomou EK, Kokkinidis DG, Kampaktsis PN, Amir EA, Marwick TH, Gupta D, Thavendiranathan P. Assessment of Prognostic Value of Left Ventricular Global Longitudinal Strain for Early Prediction of Chemotherapy-Induced Cardiotoxicity. JAMA Cardiology 2019, 4: 1007-1018. PMID: 31433450, PMCID: PMC6705141, DOI: 10.1001/jamacardio.2019.2952.Peer-Reviewed Original ResearchConceptsCancer therapy-related cardiac dysfunctionGlobal longitudinal strainLeft ventricular global longitudinal strainVentricular global longitudinal strainPrognostic valueCutoff valuePublication biasMeasurement of GLSNew-onset heart failure symptomsAbsolute global longitudinal strainWorse global longitudinal strainLarge prospective multicenter studyLeft ventricular ejection fractionDiscriminatory performanceLongitudinal strainChemotherapy-Induced CardiotoxicityGLS cutoff valueSubclinical ventricular dysfunctionHeart failure symptomsProspective multicenter studyVentricular ejection fractionCochrane Library databasesRisk of biasOptimal cutoff valueBetter prognostic performanceThe role of adipose tissue in cardiovascular health and disease
Oikonomou EK, Antoniades C. The role of adipose tissue in cardiovascular health and disease. Nature Reviews Cardiology 2018, 16: 83-99. PMID: 30287946, DOI: 10.1038/s41569-018-0097-6.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsAdipose tissueCardiovascular systemCardiovascular healthInsulin resistance leadPro-atherogenic profileCardiovascular risk stratificationAdipose tissue functionAdipose tissue biologyComplex homeostatic mechanismsRisk stratificationLocal inflammationCardiovascular diseasePossible clinical translationParacrine effectsTherapeutic targetingHomeostatic mechanismsDiseaseGaseous messengerTissueClinical translationTissue biologyTissue functionCrucial regulatorCurrent knowledgeHealthArtificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Oikonomou E, Sangha V, Dhingra L, Aminorroaya A, Coppi A, Krumholz H, Baldassarre L, Khera R. Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images. Circulation Cardiovascular Quality And Outcomes 2024 PMID: 39221857, DOI: 10.1161/circoutcomes.124.011504.Peer-Reviewed Original ResearchCancer therapeutics-related cardiac dysfunctionGlobal longitudinal strainLeft ventricular systolic dysfunctionCardiac dysfunctionBreast cancerNon-Hodgkin lymphoma therapyNon-Hodgkin's lymphomaVentricular systolic dysfunctionAssociated with worse global longitudinal strainRisk stratification strategiesHigh-risk groupMonths post-treatmentPost hoc analysisElectrocardiographic (ECGTrastuzumab exposureLymphoma therapySystolic dysfunctionAI-ECGBefore treatmentRisk biomarkersLongitudinal strainLow riskStratification strategiesHigher incidencePositive screen
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 PMID: 39581517, 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 systemExpanding 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, ehae737. PMID: 39523016, DOI: 10.1093/eurheartj/ehae737.Commentaries, Editorials and LettersAn Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.
Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Shankar SV, Coppi A, Foppa M, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD. MedRxiv 2024 PMID: 39417095, DOI: 10.1101/2024.10.06.24314939.Peer-Reviewed Original ResearchDevelopment and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms.
Aminorroaya A, Dhingra LS, Pedroso Camargos A, Vasisht Shankar S, Coppi A, Khunte A, Foppa M, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms. MedRxiv 2024 PMID: 39417103, DOI: 10.1101/2024.10.07.24314974.Peer-Reviewed Original Research In PressAutomated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models.
Shankar SV, Dhingra LS, Aminorroaya A, Adejumo P, Nadkarni GN, Xu H, Brandt C, Oikonomou EK, Pedroso AF, Khera R. Automated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models. MedRxiv 2024 PMID: 39417094, DOI: 10.1101/2024.10.08.24315035.Peer-Reviewed Original Research In PressCardiovascular care with digital twin technology in the era of generative artificial intelligence
Thangaraj P, Benson S, Oikonomou E, Asselbergs F, Khera R. Cardiovascular care with digital twin technology in the era of generative artificial intelligence. European Heart Journal 2024, ehae619. PMID: 39322420, DOI: 10.1093/eurheartj/ehae619.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsGenerative artificial intelligenceCardiovascular careCardiovascular medicinePersonalized cardiovascular careArtificial intelligenceSimulations of clinical scenariosData modalitiesPrediction of disease riskClinical decision-makingIn silico replicationPersonalized patient careDigital twinClinical scenariosDigital twin technologyMulti-modal dataProcedural planningDiagnostic workflowDisease phenotypePatient care