Featured Publications
Transforming 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 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 patientsPerivascular 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 ResearchMeSH KeywordsAdipose TissueAdultAgedCardiovascular DiseasesCoronary VesselsFemaleHumansMaleMiddle AgedPlaque, AtheroscleroticRisk AssessmentTomography, X-Ray ComputedThe 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
2023
Machine learning in precision diabetes care and cardiovascular risk prediction
Oikonomou E, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovascular Diabetology 2023, 22: 259. PMID: 37749579, PMCID: PMC10521578, DOI: 10.1186/s12933-023-01985-3.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsArtificial IntelligenceCardiovascular DiseasesDiabetes MellitusHeart Disease Risk FactorsHumansMachine LearningRisk FactorsConceptsArtificial intelligence solutionsArtificial intelligence productsData-driven methodIntelligence solutionsArtificial intelligenceMachine learningPersonalized solutionsIntelligence productsBias mitigationMachineKey issuesPredictive modelSuch modelsSuccessful applicationRisk predictionParadigm shiftIntelligenceKey propertiesApplicationsLearningPersonalized careFrameworkSolutionCurrent regulatory frameworkHealthcareUse of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020
Dhingra L, Aminorroaya A, Oikonomou E, Nargesi A, Wilson F, Krumholz H, Khera R. Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020. JAMA Network Open 2023, 6: e2316634. PMID: 37285157, PMCID: PMC10248745, DOI: 10.1001/jamanetworkopen.2023.16634.Peer-Reviewed Original ResearchMeSH KeywordsAdultCardiovascular DiseasesCross-Sectional StudiesFemaleHumansHypertensionMaleMiddle AgedObesityRisk FactorsConceptsHealth Information National Trends SurveyUS adultsExacerbate disparitiesWearable device usersCardiovascular diseaseCardiovascular healthPopulation-based cross-sectional studySelf-reported cardiovascular diseaseCardiovascular disease risk factorsNational Trends SurveyOverall US adult populationCardiovascular risk factor profileSelf-reported accessAssociated with lower useUse of wearable devicesImprove cardiovascular healthLower household incomeLower educational attainmentUS adult populationRisk factor profileNationally representative sampleCross-sectional studyProportion of adultsTrends SurveyWearable device data
2022
Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes.
Oikonomou EK, Suchard MA, McGuire DK, Khera R. Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes. Diabetes Care 2022, 45: 965-974. PMID: 35120199, PMCID: PMC9016734, DOI: 10.2337/dc21-1765.Peer-Reviewed Original ResearchMeSH KeywordsCanagliflozinCardiovascular DiseasesDiabetes Mellitus, Type 2FemaleHeart Disease Risk FactorsHumansMaleRisk FactorsConceptsCanagliflozin Cardiovascular Assessment StudyMajor adverse cardiovascular eventsType 2 diabetesHazard ratioSodium-glucose cotransporter 2 inhibitorsCardiovascular disease benefitAdverse cardiovascular eventsCotransporter 2 inhibitorsEffects of canagliflozinCanagliflozin dosesCanagliflozin's effectsCardiovascular eventsCardiovascular riskPatients 5Cardioprotective effectsSGLT2 inhibitorsDisease benefitBaseline variablesOriginal trialCanagliflozinType 2DiabetesPatientsRisk estimatesEffect estimates
2020
Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease
Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovascular Research 2020, 116: 2040-2054. PMID: 32090243, PMCID: PMC7585409, DOI: 10.1093/cvr/cvaa021.Peer-Reviewed Original ResearchConceptsCardiac computed tomographyNon-invasive imagingCardiovascular imagingCardiovascular diseaseNon-invasive cardiovascular imagingCardiac CTCardiovascular risk stratificationFirst-line optionImportant clinical implicationsRisk stratificationUnstable patientsClinical careComputed tomographyCurrent evidenceClinical implicationsCardiac imagingRadiomics approachPatientsRadiomics methodTissue biologyCardiovascular diagnosticsDiseaseFuture studiesImagingPhenotyping
2017
Predictive value of telomere length on outcome following acute myocardial infarction: evidence for contrasting effects of vascular vs. blood oxidative stress
Margaritis M, Sanna F, Lazaros G, Akoumianakis I, Patel S, Antonopoulos AS, Duke C, Herdman L, Psarros C, Oikonomou EK, Shirodaria C, Petrou M, Sayeed R, Krasopoulos G, Lee R, Tousoulis D, Channon KM, Antoniades C. Predictive value of telomere length on outcome following acute myocardial infarction: evidence for contrasting effects of vascular vs. blood oxidative stress. European Heart Journal 2017, 38: 3094-3104. PMID: 28444175, PMCID: PMC5837455, DOI: 10.1093/eurheartj/ehx177.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsAcute myocardial infarctionVascular smooth muscle cellsVascular oxidative stressSaphenous veinMyocardial infarctionOxidative stressPredictive valueRecent acute myocardial infarctionCoronary artery bypassBlood mononuclear cellsClinical predictive valueBlood oxidative stressSmooth muscle cellsFunctional genetic polymorphismsEffects of vascularTissue-specific biomarkersArtery bypassProspective cohortConsecutive patientsClinical outcomesVascular factorsPost-AMIMononuclear cellsArtery segments