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 ResearchConceptsSevere aortic stenosisDetection 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
Racial and Ethnic Disparities in Age-Specific All-Cause Mortality During the COVID-19 Pandemic
Faust J, Renton B, Bongiovanni T, Chen A, Sheares K, Du C, Essien U, Fuentes-Afflick E, Haywood T, Khera R, King T, Li S, Lin Z, Lu Y, Marshall A, Ndumele C, Opara I, Loarte-Rodriguez T, Sawano M, Taparra K, Taylor H, Watson K, Yancy C, Krumholz H. Racial and Ethnic Disparities in Age-Specific All-Cause Mortality During the COVID-19 Pandemic. JAMA Network Open 2024, 7: e2438918. PMID: 39392630, DOI: 10.1001/jamanetworkopen.2024.38918.Peer-Reviewed Original ResearchConceptsCOVID-19 public health emergencyNon-HispanicPublic health emergencyOther Pacific IslanderExcess mortalityAlaska NativesUS populationExcess deathsRates of excess mortalityCross-sectional study analyzed dataYears of potential lifeMortality relative riskNon-Hispanic whitesCross-sectional studyPacific IslandersStudy analyzed dataAll-cause mortalityEthnic groupsMortality disparitiesMortality ratioTotal populationDeath certificatesEthnic disparitiesMain OutcomesDecedent ageCause-Specific Mortality Rates Among the US Black Population
Arun A, Caraballo C, Sawano M, Lu Y, Khera R, Yancy C, Krumholz H. Cause-Specific Mortality Rates Among the US Black Population. JAMA Network Open 2024, 7: e2436402. PMID: 39348122, PMCID: PMC11443349, DOI: 10.1001/jamanetworkopen.2024.36402.Peer-Reviewed Original ResearchReviewer 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, PMCID: PMC11529699, 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 qualityEssayComparative 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 modelAI-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 ResearchTransforming 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 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 discriminationIntroducing the JAMA Summit
Bibbins-Domingo K, Angus D, Park H, Lewis R, Khera R, Zeis J, Flanagin A, Curfman G. Introducing the JAMA Summit. JAMA 2024, 331: 1451-1451. DOI: 10.1001/jama.2024.5570.Peer-Reviewed Original ResearchArtificial intelligence-enhanced exposomics: novel insights into cardiovascular health
Khera R. Artificial intelligence-enhanced exposomics: novel insights into cardiovascular health. European Heart Journal 2024, 45: 1550-1552. PMID: 38544282, DOI: 10.1093/eurheartj/ehae159.Peer-Reviewed Original ResearchConceptsCardiovascular health
2023
Quantitative Prediction of Right Ventricular Size and Function From the ECG
Duong S, Vaid A, My V, Butler L, Lampert J, Pass R, Charney A, Narula J, Khera R, Sakhuja A, Greenspan H, Gelb B, Do R, Nadkarni G. Quantitative Prediction of Right Ventricular Size and Function From the ECG. Journal Of The American Heart Association 2023, 13: e031671. PMID: 38156471, PMCID: PMC10863807, DOI: 10.1161/jaha.123.031671.Peer-Reviewed Original ResearchAccelerating chest pain evaluation with machine learning
Thangaraj P, Khera R. Accelerating chest pain evaluation with machine learning. European Heart Journal Acute Cardiovascular Care 2023, 12: 753-754. PMID: 37793075, PMCID: PMC11004857, DOI: 10.1093/ehjacc/zuad117.Peer-Reviewed Original ResearchMachine 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 frameworkHealthcareLifting the Veil on Advanced Heart Failure ∗
Sandhu A, Khera R. Lifting the Veil on Advanced Heart Failure ∗. JACC Heart Failure 2023, 11: 1607-1610. PMID: 37737760, PMCID: PMC11009373, DOI: 10.1016/j.jchf.2023.07.031.Peer-Reviewed Original ResearchHeart FailureHumansAI in Medicine—JAMA’s Focus on Clinical Outcomes, Patient-Centered Care, Quality, and Equity
Khera R, Butte A, Berkwits M, Hswen Y, Flanagin A, Park H, Curfman G, Bibbins-Domingo K. AI in Medicine—JAMA’s Focus on Clinical Outcomes, Patient-Centered Care, Quality, and Equity. JAMA 2023, 330: 818-820. PMID: 37566406, DOI: 10.1001/jama.2023.15481.Peer-Reviewed Original ResearchClinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity
Zhang L, Khera R, Mortazavi B. Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083199, PMCID: PMC11007255, DOI: 10.1109/embc40787.2023.10340765.Peer-Reviewed Original ResearchUse 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 ResearchConceptsHealth 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 dataSex Difference in Outcomes of Acute Myocardial Infarction in Young Patients
Sawano M, Lu Y, Caraballo C, Mahajan S, Dreyer R, Lichtman J, D'Onofrio G, Spatz E, Khera R, Onuma O, Murugiah K, Spertus J, Krumholz H. Sex Difference in Outcomes of Acute Myocardial Infarction in Young Patients. Journal Of The American College Of Cardiology 2023, 81: 1797-1806. PMID: 37137590, DOI: 10.1016/j.jacc.2023.03.383.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionNoncardiac hospitalizationsSubdistribution HRYounger patientsMyocardial infarctionSex differencesYoung womenCause-specific hospitalizationsCause of hospitalizationWorse health statusSignificant sex disparityNoncardiovascular hospitalizationsVIRGO StudyIndex episodeAdverse outcomesIncidence rateHospitalizationHigh riskSex disparitiesHealth statusPatientsU.S. hospitalsWomenInfarctionOutcomes