Featured Publications
Individualising 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
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 StatementsConceptsArtificial 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 ResearchConceptsCanagliflozin 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
2019
Cardiac Computed Tomography
Oikonomou EK, West HW, Antoniades C. Cardiac Computed Tomography. Arteriosclerosis Thrombosis And Vascular Biology 2019, 39: 2207-2219. PMID: 31510795, DOI: 10.1161/atvbaha.119.312899.Peer-Reviewed Original ResearchConceptsCardiac computed tomographyComputed tomographyCoronary atherosclerosisCoronary plaque burdenAcute coronary syndromeCoronary artery diseaseFirst-line diagnostic testUnstable coronary plaquesMorphological plaque featuresCoronary syndromeInflammatory burdenArterial inflammationArtery diseaseCoronary lesionsPlaque burdenPlaque featuresPrognostic implicationsCoronary plaquesPerivascular fatHemodynamic significanceVulnerable plaquesDiagnostic testsMajor causeTomographyCurrent role
2018
Perivascular adipose tissue and coronary atherosclerosis
Mancio J, Oikonomou EK, Antoniades C. Perivascular adipose tissue and coronary atherosclerosis. Heart 2018, 104: 1654. PMID: 29853488, DOI: 10.1136/heartjnl-2017-312324.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsAdipose TissueAtherosclerosisBlood VesselsCoronary Artery DiseaseEnergy MetabolismHumansRisk FactorsConceptsCardiometabolic risk profilePerivascular ATPerivascular adipose tissueAdipose tissueCardiovascular diseaseVascular wallEpicardial adipose tissueClose anatomical proximityAnti-atherogenic rolePotential therapeutic targetNovel clinical diagnostic toolsPromising new modalityCoronary inflammationCoronary atherosclerosisInflammatory statusNon-invasive imagingCardiovascular healthBody of evidenceAnatomical proximityCurrent evidenceTherapeutic targetAdjacent vasculatureClinical diagnostic toolRisk profileTherapeutic opportunities