2025
Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Dhingra L, Aminorroaya A, Pedroso A, Khunte A, Sangha V, McIntyre D, Chow C, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. JAMA Cardiology 2025, 10: 574-584. PMID: 40238120, PMCID: PMC12004248, DOI: 10.1001/jamacardio.2025.0492.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFNew-onset HFHarrell's C-statisticProspective population-based cohortUK Biobank (UKBBrazilian Longitudinal StudyELSA-Brasil participantsC-statisticPopulation-based cohortIntegrated discrimination improvementReclassification improvementRisk of deathUKB participantsHealth systemRetrospective cohort studyDiscrimination improvementMain OutcomesLeft ventricular systolic dysfunctionHF riskUKBCohort studySingle-lead ECGIndependent of ageThe emerging role of AI in transforming cardiovascular care
Croon P, Pedroso A, Khera R. The emerging role of AI in transforming cardiovascular care. Future Cardiology 2025, 21: 547-550. PMID: 40248957, PMCID: PMC12150600, DOI: 10.1080/14796678.2025.2492973.Peer-Reviewed Original ResearchDevelopment and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms
Aminorroaya A, Dhingra L, Pedroso A, Shankar S, Coppi A, Khunte A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Development and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms. European Heart Journal - Digital Health 2025, ztaf034. DOI: 10.1093/ehjdh/ztaf034.Peer-Reviewed Original ResearchDetectable structural heart diseaseStructural heart diseaseCommunity-based screeningLeft-sided valvular diseaseHeart diseaseELSA-BrasilYale-New Haven HospitalAI-ECG algorithmDeep learning algorithmsPopulation-based cohortSevere LVHEchocardiographic dataPredictive biomarkersHospital-based sitesNew Haven HospitalRisk stratificationValvular diseaseEnsemble deep learning algorithmUK BiobankCommunity hospitalLead I ECGAutomated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Model
Vasisht Shankar S, Dhingra LS, Aminorroaya A, Adejumo P, Nadkarni GN, Xu H, Brandt C, Oikonomou EK, Pedroso AF, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. Eur Heart J Digit Health. 2025;ztaf030.Peer-Reviewed Original ResearchAutomated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models
Vasisht Shankar S, Dhingra L, Aminorroaya A, Adejumo P, Nadkarni G, Xu H, Brandt C, Oikonomou E, Pedroso A, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. European Heart Journal - Digital Health 2025, ztaf030. DOI: 10.1093/ehjdh/ztaf030.Peer-Reviewed Original ResearchEnsemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Shankar S, Coppi A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD. Journal Of The American College Of Cardiology 2025, 85: 1302-1313. PMID: 40139886, PMCID: PMC12199746, DOI: 10.1016/j.jacc.2025.01.030.Peer-Reviewed Original ResearchConceptsStructural heart diseaseYale-New Haven HospitalTransthoracic echocardiogramRisk stratificationHeart failureLeft-sided valvular diseaseSevere left ventricular hypertrophyLeft ventricular ejection fractionReceiver-operating characteristic curveVentricular ejection fractionLeft ventricular hypertrophyHeart disease screeningELSA-BrasilEnsemble deep learning algorithmRisk of deathConvolutional neural network modelEjection fractionEnsemble deep learning approachVentricular hypertrophyDeep learning algorithmsNew Haven HospitalDeep learning approachValvular diseaseNeural network modelClinical cohortHarnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care
Aminorroaya A, Biswas D, Pedroso A, Khera R. Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care. Journal Of The Society For Cardiovascular Angiography & Interventions 2025, 4: 102562. PMID: 40230673, PMCID: PMC11993883, DOI: 10.1016/j.jscai.2025.102562.Peer-Reviewed Original ResearchClinical careCommunity-based screening programCare quality outcomesPatient outcomesPatient-focused careHarness artificial intelligenceArtificial intelligencePotential of AIImprove patient outcomesIndividualized clinical careTransform careTransform clinical practiceCardiovascular careScreening programHealth dataQuality outcomesCareClinical workflowClinical tasksAcute coronary syndromeClinical practiceHeart diseaseAI-driven technologiesInterventionAI-enabledHarnessing artificial intelligence for innovation in interventional cardiovascular care
Aminorroaya A, Biswas D, Pedroso AF, Khera R. Harnessing artificial intelligence for innovation in interventional cardiovascular care. J Soc Cardiovasc Angiogr Interv. 2025;4:102562.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsAssessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study
Khera R, Sawano M, Warner F, Coppi A, Pedroso A, Spatz E, Yu H, Gottlieb M, Saydah S, Stephens K, Rising K, Elmore J, Hill M, Idris A, Montoy J, O’Laughlin K, Weinstein R, Venkatesh A, Weinstein R, Gottlieb M, Santangelo M, Koo K, Derden A, Gottlieb M, Gatling K, Ahmed Z, Gomez C, Guzman D, Hassaballa M, Jerger R, Kaadan A, Venkatesh A, Spatz E, Kinsman J, Malicki C, Lin Z, Li S, Yu H, Mannan I, Yang Z, Liu M, Venkatesh A, Spatz E, Ulrich A, Kinsman J, Malicki C, Dorney J, Pierce S, Puente X, Salah W, Nichol G, Stephens K, Anderson J, Schiffgens M, Morse D, Adams K, Stober T, Maat Z, O’Laughlin K, Gentile N, Geyer R, Willis M, Zhang Z, Chang G, Lyon V, Klabbers R, Ruiz L, Malone K, Park J, Rising K, Kean E, Chang A, Renzi N, Watts P, Kelly M, Schaeffer K, Grau D, Cheng D, Shutty C, Charlton A, Shughart L, Shughart H, Amadio G, Miao J, Hannikainen P, Elmore J, Wisk L, L’Hommedieu M, Chandler C, Eguchi M, Roldan K, Moreno R, Rodriguez R, Wang R, Montoy J, Kemball R, Chan V, Chavez C, Wong A, Arreguin M, Hill M, Site R, Kane A, Nikonowicz P, Sapp S, Idris A, McDonald S, Gallegos D, Martin K, Saydah S, Plumb I, Hall A, Briggs-Hagen M. Assessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study. Journal Of The American Medical Informatics Association 2025, 32: 784-794. PMID: 40036551, PMCID: PMC12012333, DOI: 10.1093/jamia/ocaf027.Peer-Reviewed Original ResearchElectronic health recordsSelf-report questionnairesSelf-reportHealth conditionsElectronic health record portalsElectronic health record platformsEHR elementsSelf-reported health conditionsElectronic health record dataSelf-reported conditionsAssessment of health conditionEvaluation of health conditionsPrevalence of conditionsPatient portalsTraditional self-reportPrevalence of comorbiditiesHealth recordsEHR dataEHR phenotypesDiagnosis codesHospitalization riskComputable phenotypeNationwide studyCohen's kappaPatient characteristicsHeart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. European Heart Journal 2025, 46: 1044-1053. PMID: 39804243, PMCID: PMC12086686, DOI: 10.1093/eurheartj/ehae914.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFUK BiobankHF riskBrazilian Longitudinal Study of Adult HealthLongitudinal Study of Adult HealthBrazilian Longitudinal StudyRisk of new-onset HFPooled Cohort EquationsPrimary HF hospitalizationsHigher HF riskHarrell's C-statisticRisk of deathNew-onset HFCohort EquationsHealth systemComprehensive clinical evaluationAdult healthHeart failureIncident HFHF hospitalizationBaseline HFC-statisticPrevent HF
2024
Automated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models.
Shankar SV, Dhingra LS, Aminorroaya A, Adejumo P, Nadkarni GN, Xu H, Brandt C, Oikonomou EK, Pedroso AF, Khera R. Automated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Models. MedRxiv 2024 PMID: 39417094, DOI: 10.1101/2024.10.08.24315035.Peer-Reviewed Original Research In PressDevelopment and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms.
Aminorroaya A, Dhingra LS, Pedroso Camargos A, Vasisht Shankar S, Coppi A, Khunte A, Foppa M, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms. MedRxiv 2024 PMID: 39417103, DOI: 10.1101/2024.10.07.24314974.Peer-Reviewed Original Research In PressAn Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.
Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Shankar SV, Coppi A, Foppa M, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD. MedRxiv 2024 PMID: 39417095, DOI: 10.1101/2024.10.06.24314939.Peer-Reviewed Original Research In PressComparative 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, PMCID: PMC12045554, 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 modelPerformance 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 discriminationStudy Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD).
Aminorroaya A, Dhingra LS, Camargos AP, Shankar SV, Khunte A, Sangha V, Sen S, McNamara RL, Haynes N, Oikonomou EK, Khera R. Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD). MedRxiv 2024 PMID: 38562867, DOI: 10.1101/2024.03.18.24304477.Peer-Reviewed Original Research In PressUsing Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.
Dhingra LS, Aminorroaya A, Camargos AP, Khunte A, Sangha V, McIntyre D, Chow CK, Asselbergs FW, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment. MedRxiv 2024 PMID: 38854022, DOI: 10.1101/2024.05.27.24307952.Peer-Reviewed Original Research In PressScalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study.
Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Asselbergs FW, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study. MedRxiv 2024 PMID: 38633808, DOI: 10.1101/2024.04.02.24305232.Peer-Reviewed Original Research In Press
2023
Uncovering the Relationship Between Statins and Muscle Problems in the ELSA-Brasil MSK Cohort
Pedroso A, Barreto S, Telles R, Machado L, Haueisen Sander Diniz M, Duncan B, Figueiredo R. Uncovering the Relationship Between Statins and Muscle Problems in the ELSA-Brasil MSK Cohort. Cardiovascular Drugs And Therapy 2023, 38: 1409-1414. PMID: 37261675, DOI: 10.1007/s10557-023-07476-7.Peer-Reviewed Original ResearchConceptsFive-times-sit-to-standStatin useMSK cohortMuscle problemsELSA-BrasilMuscle painHandgrip testFive-times-sit-to-stand testDuration of statin treatmentSecondary analysisMultivariate logistic regression analysisBrazilian civil servantsLogistic regression analysisNo significant associationSelf-reported symptomsCross-sectional data analysisStatin treatmentEfficacy-effectiveness gapMuscle strengthHandgrip strengthHighest quintileMuscle groupsMuscle weaknessStatinsPain
2019
STATIN USE AND MUSCULAR PROBLEMS: A CROSS-SECTIONAL ANALYSIS OF THE ELSA-BRASIL MUSCULOSKELETAL COHORT
PEDROSO A, TELLES R, MACHADO L, BARRETO S, FIGUEIREDO R. STATIN USE AND MUSCULAR PROBLEMS: A CROSS-SECTIONAL ANALYSIS OF THE ELSA-BRASIL MUSCULOSKELETAL COHORT. 2019, 587-587. DOI: 10.5151/sbr2019-587.Peer-Reviewed Original Research
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