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 Original ResearchArtificial 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.BooksConceptsAdipose 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 knowledgeHealth
2024
A Narrative Review on Prolonged Neuropsychiatric Consequences of COVID-19: A Serious Concern
Theofilis P, Oikonomou E, Vasileiadou M, Tousoulis D. A Narrative Review on Prolonged Neuropsychiatric Consequences of COVID-19: A Serious Concern. Heart And Mind 2024, 8: 177-183. DOI: 10.4103/hm.hm-d-24-00019.Peer-Reviewed Original ResearchPostacute sequelae of COVID-19Cognitive behavioral therapyNeuropsychiatric consequences of COVID-19Neuropsychological supportBehavioral therapyAffect memoryCognitive dysfunctionCognitive rehabilitationPsychological evaluationNeuropsychiatric implicationsEmotional distressMultimodal assessmentSymptom chronicitySocial supportPersonalized interventionsCoping mechanismsWell-beingSupport networksConsequences of COVID-19SymptomsNew-onset symptomsTargeted interventionsQuality of lifeSequelae of COVID-19Narrative reviewEfficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
Holste G, Oikonomou E, Mortazavi B, Wang Z, Khera R. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning. Communications Medicine 2024, 4: 133. PMID: 38971887, PMCID: PMC11227494, DOI: 10.1038/s43856-024-00538-3.Peer-Reviewed Original ResearchSelf-supervised learningTransfer learningTraining dataEchocardiogram videosPortion of labelled dataStandard transfer learning approachContrastive self-supervised learningSelf-supervised learning approachLearning approachImage recognition tasksState-of-the-artContrastive learning approachFine-tuningTransfer learning approachMedical image diagnosisCardiac disease diagnosisContrastive learningVideo framesLabeled datasetLabeled dataExpert labelsClassification performanceMedical imagesRecognition taskVideoReal-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study
Oikonomou E, Aminorroaya A, Dhingra L, Partridge C, Velazquez E, Desai N, Krumholz H, Miller E, Khera R. Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study. European Heart Journal - Digital Health 2024, 5: 303-313. PMID: 38774380, PMCID: PMC11104476, DOI: 10.1093/ehjdh/ztae023.Peer-Reviewed Original ResearchRisk of acute myocardial infarctionAssociated with lower oddsHospital health systemCoronary artery diseaseCardiac testingRisk of adverse outcomesUK BiobankHealth systemProvider-drivenLower oddsAssociated with better outcomesAcute myocardial infarctionBlack raceStable chest painFemale sexReal world evaluationDiabetes historyMulticohort studyFunction testsSuspected coronary artery diseaseYounger ageRisk profileAdverse outcomesMultinational cohortPost hoc analysisTRENDS IN KNOWLEDGE OF RISK FACTOR TARGETS AMONG PATIENTS WITH DIABETES MELLITUS IN THE UNITED STATES: A NATIONALLY REPRESENTATIVE STUDY
Bansal B, Aminorroaya A, Dhingra L, Oikonomou E, Khera R. TRENDS IN KNOWLEDGE OF RISK FACTOR TARGETS AMONG PATIENTS WITH DIABETES MELLITUS IN THE UNITED STATES: A NATIONALLY REPRESENTATIVE STUDY. Journal Of The American College Of Cardiology 2024, 83: 1882. DOI: 10.1016/s0735-1097(24)03872-5.Peer-Reviewed Original ResearchHEART FAILURE RISK PREDICTION USING ARTIFICIAL INTELLIGENCE ON ECG PHOTOS IN LARGE CONTEMPORARY COHORT
Dhingra L, Sangha V, Aminorroaya A, Camargos A, Oikonomou E, Khera R. HEART FAILURE RISK PREDICTION USING ARTIFICIAL INTELLIGENCE ON ECG PHOTOS IN LARGE CONTEMPORARY COHORT. Journal Of The American College Of Cardiology 2024, 83: 277. DOI: 10.1016/s0735-1097(24)02267-8.Peer-Reviewed Original ResearchMULTINATIONAL REAL-WORLD EVALUATION OF A MACHINE LEARNING-DERIVED TOOL FOR ANATOMICAL VERSUS FUNCTIONAL TESTING IN SUSPECTED CORONARY ARTERY DISEASE
Oikonomou E, Aminorroaya A, Dhingra L, Khera R. MULTINATIONAL REAL-WORLD EVALUATION OF A MACHINE LEARNING-DERIVED TOOL FOR ANATOMICAL VERSUS FUNCTIONAL TESTING IN SUSPECTED CORONARY ARTERY DISEASE. Journal Of The American College Of Cardiology 2024, 83: 1357. DOI: 10.1016/s0735-1097(24)03347-3.Peer-Reviewed Original ResearchEVIDENCE FROM RANDOMIZED CONTROLLED TRIAL TO REAL-WORLD PATIENTS USING ELECTRONIC HEALTH RECORD-ADAPTED DIGITAL TWINS: A NOVEL APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE
Thangaraj P, Shankar S, Oikonomou E, Khera R. EVIDENCE FROM RANDOMIZED CONTROLLED TRIAL TO REAL-WORLD PATIENTS USING ELECTRONIC HEALTH RECORD-ADAPTED DIGITAL TWINS: A NOVEL APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE. Journal Of The American College Of Cardiology 2024, 83: 2340. DOI: 10.1016/s0735-1097(24)04330-4.Peer-Reviewed Original Research