Featured Publications
Severe 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 ResearchMultinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM
Khera R, Dhingra L, Aminorroaya A, Li K, Zhou J, Arshad F, Blacketer C, Bowring M, Bu F, Cook M, Dorr D, Duarte-Salles T, DuVall S, Falconer T, French T, Hanchrow E, Horban S, Lau W, Li J, Liu Y, Lu Y, Man K, Matheny M, Mathioudakis N, McLemore M, Minty E, Morales D, Nagy P, Nishimura A, Ostropolets A, Pistillo A, Posada J, Pratt N, Reyes C, Ross J, Seager S, Shah N, Simon K, Wan E, Yang J, Yin C, You S, Schuemie M, Ryan P, Hripcsak G, Krumholz H, Suchard M. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM. BMJ Medicine 2023, 2: e000651. PMID: 37829182, PMCID: PMC10565313, DOI: 10.1136/bmjmed-2023-000651.Peer-Reviewed Original ResearchType 2 diabetes mellitusSecond-line treatmentCardiovascular risk groupsDiabetes mellitusCardiovascular diseaseAntihyperglycaemic drugsLine treatmentRisk groupsObservational Health Data SciencesGlucagon-like peptide-1 receptor agonistsElectronic health recordsSodium-glucose cotransporter 2 inhibitorsCalendar year trendsPeptide-1 receptor agonistsUS databaseOutcomes of patientsCotransporter 2 inhibitorsAdministrative claims databaseSecond-line drugsHealth recordsSodium-glucose cotransporter-2 inhibitorsMedication useMetformin monotherapyGuideline recommendationsOutcome measuresDetection 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
2024
Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest.
Silva G, Khera R, Schwamm L, Acampa M, Adelman E, Boltze J, Broderick J, Brodtmann A, Christensen H, Dalli L, Duncan K, Elgendy I, Ergul A, Goldstein L, Hinkle J, Johansen M, Jood K, Kasner S, Levine S, Li Z, Lip G, Marsh E, Muir K, Ospel J, Pera J, Quinn T, Räty S, Ranta A, Richards L, Romero J, Willey J, Hillis A, Veerbeek J. Reviewer Experience Detecting and Judging Human Versus Artificial Intelligence Content: The Stroke Journal Essay Contest. Stroke 2024, 55: 2573-2578. PMID: 39224979, DOI: 10.1161/strokeaha.124.045012.Peer-Reviewed Original ResearchConceptsArtificial intelligenceEditorial board membersAuthor typeTraditional peer reviewLanguage modelIntelligent contentAuthor attributionGeneral textAI expertiseHuman authorityImproved accuracyAuthor's identityAuthor's manuscriptScientific journalsEssay contestPeer reviewPerception of qualityAuthorshipNature of authorshipIntelligenceLLMScientific writingScientific essayEssay qualityEssayArtificial 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 screenComparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes A Multinational, Federated Analysis of LEGEND-T2DM
Khera R, Aminorroaya A, Dhingra L, Thangaraj P, Pedroso Camargos A, Bu F, Ding X, Nishimura A, Anand T, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr D, Duarte-Salles T, DuVall S, Falconer T, French T, Hanchrow E, Kaur G, Lau W, Li J, Li K, Liu Y, Lu Y, Man K, Matheny M, Mathioudakis N, McLeggon J, McLemore M, Minty E, Morales D, Nagy P, Ostropolets A, Pistillo A, Phan T, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager S, Simon K, Viernes B, Yang J, Yin C, You S, Zhou J, Ryan P, Schuemie M, Krumholz H, Hripcsak G, Suchard M. Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes A Multinational, Federated Analysis of LEGEND-T2DM. Journal Of The American College Of Cardiology 2024, 84: 904-917. PMID: 39197980, DOI: 10.1016/j.jacc.2024.05.069.Peer-Reviewed Original ResearchConceptsGLP-1 RAsSecond-line agentsGLP-1Antihyperglycemic agentsCardiovascular diseaseMACE riskGlucagon-like peptide-1 receptor agonistsSodium-glucose cotransporter 2 inhibitorsPeptide-1 receptor agonistsDipeptidyl peptidase-4 inhibitorsEffects of SGLT2isType 2 diabetes mellitusPeptidase-4 inhibitorsAdverse cardiovascular eventsCox proportional hazards modelsRandom-effects meta-analysisCardiovascular risk reductionTarget trial emulationProportional hazards modelMultimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population
Jiang J, Thi Vy H, Charney A, Kovatch P, Reddy V, Jayaraman P, Do R, Khera R, Chugh S, Bhatt D, Vaid A, Lampert J, Nadkarni G. Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population. Npj Digital Medicine 2024, 7: 226. PMID: 39181999, PMCID: PMC11344778, DOI: 10.1038/s41746-024-01218-1.Peer-Reviewed Original ResearchLong QT syndromeUnited Kingdom BiobankHigh-risk genotypesElectronic health record dataHealth record dataPathogenic variantsRacially/ethnically diverse cohortCongenital long QT syndromeLQTS-susceptibility genesRacially diverse populationMount Sinai BioMe BiobankPathogenic genetic mutationsQT corrected intervalArea under the receiver operating curveBioMe BiobankPatient prioritizationReceiver operating curveQT syndromeRecord dataDiverse cohortGenetic testingDiverse populationsPathogen genotypesGenetic mutationsPatientsInternal tremors and vibrations in long COVID: a cross-sectional study
Zhou T, Sawano M, Arun A, Caraballo C, Michelsen T, McAlpine L, Bhattacharjee B, Lu Y, Khera R, Huang C, Warner F, Herrin J, Iwasaki A, Krumholz H. Internal tremors and vibrations in long COVID: a cross-sectional study. The American Journal Of Medicine 2024 PMID: 39069199, DOI: 10.1016/j.amjmed.2024.07.008.Peer-Reviewed Original ResearchNew-onset conditionsInternal tremorLong COVID symptomsCOVID symptomsNon-Hispanic whitesCross-sectional studyQuality of lifeVisual analogue scaleWorse healthHealth statusStudy participantsDemographic characteristicsAnalogue scaleOutcome variablesNeurological conditionsLong COVIDMast cell disordersTreatment experienceHealthComorbiditiesSymptomsMedian agePeopleCell disordersAI-enabled diagnosis from an electrocardiogram image: the next frontier of innovation in a century-old technology
Khera R. AI-enabled diagnosis from an electrocardiogram image: the next frontier of innovation in a century-old technology. Heart 2024, 110: heartjnl-2024-324299. PMID: 39048290, PMCID: PMC11328242, DOI: 10.1136/heartjnl-2024-324299.Peer-Reviewed Original ResearchCorrelation between hospital rates of survival to discharge and long-term survival for in-hospital cardiac arrest: Insights from Get With The Guidelines®-Resuscitation registry
Khera R, Aminorroaya A, Kennedy K, Chan P, Investigators A, Grossestreuer A, Moskowitz A, Ornato J, Churpek M, Starks M, Girotra S, Perman S. Correlation between hospital rates of survival to discharge and long-term survival for in-hospital cardiac arrest: Insights from Get With The Guidelines®-Resuscitation registry. Resuscitation 2024, 202: 110322. PMID: 39029583, DOI: 10.1016/j.resuscitation.2024.110322.Peer-Reviewed Original ResearchRisk-standardized survival ratesIn-hospital cardiac arrestWeighted kappa coefficientResuscitation RegistryLong-term survivalSurvivors of in-hospital cardiac arrestHierarchical logistic regression modelsCardiac arrestIn-hospitalLogistic regression modelsLong-term outcomesSurvival dataKappa coefficientHospital performanceIn-hospital survivalMedicare filesMedicare beneficiariesYears of ageHospitalization ratesPost-discharge survivalHospital dischargeRate of survivalMedicareHospitalRegression modelsTransforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.
Shimbo D, Shah R, Abdalla M, Agarwal R, Ahmad F, Anaya G, Attia Z, Bull S, Chang A, Commodore-Mensah Y, Ferdinand K, Kawamoto K, Khera R, Leopold J, Luo J, Makhni S, Mortazavi B, Oh Y, Savage L, Spatz E, Stergiou G, Turakhia M, Whelton P, Yancy C, Iturriaga E. Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report. Hypertension 2024 PMID: 39011653, DOI: 10.1161/hypertensionaha.124.22095.Peer-Reviewed Original ResearchMachine learning toolsManagement of hypertensionNational HeartArtificial intelligenceBlood InstitutePredictive of incident hypertensionHealth care systemImplementation challengesDiverse group of stakeholdersAI toolsPopulation healthMeasurement of blood pressureCare systemHealth careIncident hypertensionHypertension riskEra of artificial intelligenceHypertension diagnosisLearning toolsManaging hypertensionHypertension-related complicationsAntihypertensive medicationsHealthPublic healthGroups of stakeholdersDesigning medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility
Oikonomou E, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic Journal Of Cardiology 2024 PMID: 39025234, DOI: 10.1016/j.hjc.2024.07.003.Peer-Reviewed Original ResearchArtificial intelligenceMedical artificial intelligence systemsDesigning AI systemsMachine learning systemsArtificial intelligence systemsBenefits of AIIntelligent systemsAI systemsLearning systemEnd-usersData typesAI developmentInteroperabilityTemporal settingAccessScalabilityTreatment of cardiovascular diseasesIntelligenceSystemMachineQuality assuranceInternational cohortCardiovascular diseaseObstaclesArtificial 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, ehae415. PMID: 38976371, DOI: 10.1093/eurheartj/ehae415.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsEfficient 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 taskVideoTransforming 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 ResearchExcess Cardiovascular Mortality Among Black Americans 2000-2022
Arun A, Sawano M, Lu Y, Warner F, Caraballo C, Khera R, Echols M, Yancy C, Krumholz H. Excess Cardiovascular Mortality Among Black Americans 2000-2022. Journal Of The American College Of Cardiology 2024, 84: 581-588. PMID: 38901531, DOI: 10.1016/j.jacc.2024.06.004.Peer-Reviewed Original ResearchPerformance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study
Camargos A, Barreto S, Brant L, Ribeiro A, Dhingra L, Aminorroaya A, Bittencourt M, Figueiredo R, Khera R. Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study. Open Heart 2024, 11: e002762. PMID: 38862252, PMCID: PMC11168182, DOI: 10.1136/openhrt-2024-002762.Peer-Reviewed Original ResearchConceptsPooled Cohort EquationsELSA-BrasilRisk scoreCardiovascular diseaseCVD eventsCommunity-based cohort studyArea under the receiver operating characteristic curveCVD risk scoreELSA-Brasil studyIncident CVD eventsMiddle-income countriesAdjudicated CVD eventsCardiovascular disease riskCVD scoreCohort EquationsNational guidelinesRisk stratification scoresWhite womenAge/sex groupsCohort studyProspective cohortLMICsSex/race groupsHigher incomeRisk discriminationA 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 structure