2025
Access to Electrophysiologic Care for Medicare Beneficiaries Across the United States: Travel Distance and Time to Nearest Clinician, 2013-2020
Khaloo P, Wheelock K, Hanna J, Kapadia S, Pedroso A, Nabi W, Aminorroaya A, Freeman J, Khera R. Access to Electrophysiologic Care for Medicare Beneficiaries Across the United States: Travel Distance and Time to Nearest Clinician, 2013-2020. Heart Rhythm 2025 PMID: 40935055, DOI: 10.1016/j.hrthm.2025.09.013.Peer-Reviewed Original ResearchElectrophysiological careMedicare beneficiariesZip codesPercentage of Hispanic residentsSocio-economically disadvantaged groupsResidents of rural areasAnnual income <Multivariate logistic regression modelHigh school educationLogistic regression modelsUnited StatesUS zip codesSociodemographic factorsCardiovascular careOlder adultsGeographic disparitiesHealthcare ResearchPractitioner dataHispanic residentsLong travel timesMedicare providersPacemaker implantationMedicare PhysicianAF ablationUS countiesTransforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges
Biswas D, Aminorroaya A, Croon P, Batinica B, Pedroso A, Khera R. Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges. Current Atherosclerosis Reports 2025, 27: 86. PMID: 40888973, DOI: 10.1007/s11883-025-01337-4.Peer-Reviewed Original ResearchConceptsAtherosclerotic cardiovascular diseasePopulation health screeningPopulation-level screeningCardiovascular diseaseLow riskHealth screeningStandard risk factorsHospital-basedCardiovascular healthSubclinical coronary artery diseaseWorkflow integrationSingle-lead ECGPersonalized interventionsPatient outcomesDiverse populationsTraditional risk modelsECG interpretationRisk factorsAscertainment biasImplementation challengesAdverse cardiovascular eventsProspective studyLogistical challengesRe-classifying patientsCoronary artery diseasePhenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.
Croon P, Dhingra L, Biswas D, Oikonomou E, Khera R. Phenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction. Circulation 2025 PMID: 40888124, DOI: 10.1161/circulationaha.125.076279.Peer-Reviewed Original ResearchElectronic health recordsNon-cardiovascular conditionsPhenome-wide association studyCross-sectional phenotypingNew-onset cardiovascular diseaseCardiovascular diseaseProspective cohort studyPhenotypic associationsHealth recordsLeft ventricular hypertrophyStructural heart diseaseAI-ECGAssociated with cardiovascular phenotypesPearson correlation coefficientDiagnosis codesCohort studyCardiovascular risk markersLogistic regressionAssociation studiesCardiovascular diagnosisMitral regurgitationAortic stenosisCardiovascular conditionsStudy populationDetection of LVSDInternational Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm
Duffy G, Oikonomou E, Easton N, Usuku H, Patel J, Katsumata Y, Yamasawa D, Stern L, Goto S, Tsujita K, Cheng P, Khera R, Ahmad F, Ouyang D. International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm. JACC Advances 2025, 102067. PMID: 40965401, DOI: 10.1016/j.jacadv.2025.102067.Peer-Reviewed Original ResearchPositive predictive valueCardiac amyloidosisReceiver operating characteristic curveArea under the receiver operating characteristic curveOperating characteristics curvePredictive valueDiagnosis of cardiac amyloidosisAccurate diagnosisRetrospective case-control studyGlobal longitudinal strainLeft ventricular wall thicknessNegative predictive valueDiagnosis of CACharacteristic curveVentricular wall thicknessBody mass indexParasternal long axisTransthoracic echocardiographic measurementsCase-control studyCA patientsVentricular hypertrophyEchocardiographic measurementsEchocardiogram studyCA subtypesMass indexNational Patterns of Remote Patient Monitoring Service Availability at US Hospitals.
Pedroso A, Lin Z, Ross J, Khera R. National Patterns of Remote Patient Monitoring Service Availability at US Hospitals. Circulation Cardiovascular Quality And Outcomes 2025, e012034. PMID: 40827414, PMCID: PMC12367071, DOI: 10.1161/circoutcomes.125.012034.Peer-Reviewed Original ResearchRemote patient monitoringUS hospitalsAmerican Hospital Association Annual SurveyTraditional health care settingsRemote patient monitoring servicesHealth care settingsNational studyCounty-level characteristicsCharacteristics of hospitalsMedian household incomeService availabilityMultivariate logistic regressionChronic careRural hospitalsCare settingsNational patternsLongitudinal careRural countiesUrban hospitalsNonteaching hospitalsHospital sizeTeaching statusCounty-level dataLow-incomeDisability statusScientific Writing in the Era of Large Language Models: A Computational Analysis of AI- Versus Human-Created Content
Khera R, Pedroso A, Keloth V, Xu H, Silva G, Schwamm L. Scientific Writing in the Era of Large Language Models: A Computational Analysis of AI- Versus Human-Created Content. Stroke 2025, 56: 3078-3083. PMID: 40814778, DOI: 10.1161/strokeaha.125.051913.Peer-Reviewed Original ResearchConceptsLanguage modelArtificial intelligenceAI-generatedLinguistic featuresDetection toolsAI-generated contentHuman-written textLanguage perplexityHuman expertsPerformance of expertsLinguistic differencesScientific textsGrade levelWord countEssayLanguageScientific communicationScientific writingComputer synthesisHigher grade levelsTextScientific contentReadability scoresPerplexityFlesch-KincaidArtificial intelligence-guided point-of-care ultrasonography for cardiomyopathy detection – Authors' reply
Oikonomou E, Khera R. Artificial intelligence-guided point-of-care ultrasonography for cardiomyopathy detection – Authors' reply. The Lancet Digital Health 2025, 7: 100893. PMID: 40769793, DOI: 10.1016/j.landig.2025.100893.Peer-Reviewed Original ResearchArtificial intelligence-enhanced echocardiography in cardiovascular disease management
Myhre P, Grenne B, Asch F, Delgado V, Khera R, Lafitte S, Lang R, Pellikka P, Sengupta P, Vemulapalli S, Lam C. Artificial intelligence-enhanced echocardiography in cardiovascular disease management. Nature Reviews Cardiology 2025, 1-19. PMID: 40764834, DOI: 10.1038/s41569-025-01197-0.Peer-Reviewed Original ResearchChanges in Cardiovascular Risk Factors and Health Care Expenditures Among Patients Prescribed Semaglutide
Lu Y, Liu Y, Totojani T, Kim C, Khera R, Xu H, Brush J, Krumholz H, Abaluck J. Changes in Cardiovascular Risk Factors and Health Care Expenditures Among Patients Prescribed Semaglutide. JAMA Network Open 2025, 8: e2526013. PMID: 40779264, PMCID: PMC12334959, DOI: 10.1001/jamanetworkopen.2025.26013.Peer-Reviewed Original ResearchConceptsHealth care expendituresCardiovascular risk factorsCare expendituresCohort studyRisk factorsYale New Haven Health SystemCohort study of adultsType 2 diabetes statusLong-term impactStudy of adultsHealth systemRetrospective cohort studyBlood pressureHemoglobin A1c reductionMain OutcomesTotal cholesterolSentara HealthcareInpatient staySecondary outcomesGlucagon-like peptide-1 receptor agonistsPrimary outcomeHealthPeptide-1 receptor agonistsAssociated with clinical outcomesAssociated with reductionsIdentification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning
Sangha V, Dhingra L, Aminorroaya A, Croon P, Sikand N, Sen S, Martinez M, Maron M, Krumholz H, Asselbergs F, Oikonomou E, Khera R. Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning. Nature Cardiovascular Research 2025, 4: 991-1000. PMID: 40696040, DOI: 10.1038/s44161-025-00685-3.Peer-Reviewed Original ResearchComplete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning
Holste G, Oikonomou E, Tokodi M, Kovács A, Wang Z, Khera R. Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning. JAMA 2025, 334: 306-318. PMID: 40549400, PMCID: PMC12186137, DOI: 10.1001/jama.2025.8731.Peer-Reviewed Original ResearchMultitask deep learningAI systemsDiagnostic classification tasksClassification taskDeep learningArtificial intelligenceArea under the receiver operating characteristic curveYale New Haven Health SystemTransthoracic echocardiography studyTransthoracic echocardiographyVentricular systolic dysfunctionParameter estimation taskSystolic dysfunctionDiagnosis tasksEchocardiographic videosRight ventricular systolic dysfunctionLeft ventricular ejection fractionAI predictionsEstimation taskVentricular ejection fractionSevere aortic stenosisManual reportingReceiver operating characteristic curveTaskClinical workflowArtificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy
Oikonomou E, Sangha V, Vasisht Shankar S, Coppi A, Krumholz H, Nasir K, Miller E, Gallegos Kattan C, Al-Mallah M, Al-Kindi S, Khera R. Artificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy. European Heart Journal 2025, ehaf450. PMID: 40679604, DOI: 10.1093/eurheartj/ehaf450.Peer-Reviewed Original ResearchTransthyretin amyloid cardiomyopathyTransthoracic echocardiographyATTR-CMAmyloid cardiomyopathyPreclinical progressAI-ECGRetrospective analysisDiagnosis of transthyretin amyloid cardiomyopathyDeep learning modelsAge/sex matched controlsRetrospective analysis of individualsLearning modelsPreclinical testingElectrocardiography imagingEchocardiographyHouston Methodist HospitalYale New Haven Health SystemAdvanced imagingElectrocardiographyPreclinical coursesCardiomyopathyPreclinical stageCardiac magnetic resonance markers of pre-clinical hypertrophic and dilated cardiomyopathy in genetic variant carriers
Croon P, van Vugt M, Allaart C, Ruijsink B, Elliott P, Asselbergs F, Khera R, Bezzina C, Winter M, Schmidt A. Cardiac magnetic resonance markers of pre-clinical hypertrophic and dilated cardiomyopathy in genetic variant carriers. BMC Medicine 2025, 23: 421. PMID: 40660250, PMCID: PMC12261545, DOI: 10.1186/s12916-025-04226-4.Peer-Reviewed Original ResearchConceptsCardiac magnetic resonance imagingCardiac magnetic resonance imaging measuresCardiac magnetic resonance markersDilated CardiomyopathyHypertrophic cardiomyopathyHeart failureUK Biobank participantsAtrial fibrillationLeft ventricular (LV) ejection fractionMitral annular plane systolic excursionAnnular plane systolic excursionLV) ejection fractionFunctional cardiac abnormalitiesEnd-systolic volumeCardiac risk factorsCardiomyopathy-associated variantsAssociated with incident AFAssociated with TTNIncident atrial fibrillationLV-ESVIMagnetic resonance imagingRV-ESVRV EFGenetic variant carriersSystolic excursionLeveraging AI-enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring
Pedroso A, Khera R. Leveraging AI-enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring. Npj Cardiovascular Health 2025, 2: 34. PMID: 40620667, PMCID: PMC12221986, DOI: 10.1038/s44325-025-00071-9.Peer-Reviewed Original ResearchArtificial intelligenceConsumer devicesConsumer wearablesPortable devicesCardiovascular careDigital healthTraditional care settingsDigital health toolsTraditional care modelResource-constrained settingsCare modelPersonalized risk assessmentCare settingsCardiovascular screeningHealth toolsWearableDevicesLow-cost alternativeIntelligenceCareTowards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention
Hurley N, Desai N, Dhruva S, Khera R, Schulz W, Huang C, Curtis J, Masoudi F, Rumsfeld J, Negahban S, Krumholz H, Mortazavi B. Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention. PLOS Digital Health 2025, 4: e0000906. PMID: 40560847, PMCID: PMC12193038, DOI: 10.1371/journal.pdig.0000906.Peer-Reviewed Original ResearchNational Cardiovascular Data RegistryPercutaneous coronary interventionPrescription of medicationsRisk predictionArea under the receiver operating characteristic curveRisk prediction modelTreatment decision makingIndividualized carePatient dischargeData registryRisk estimatesRisk modelPrimary outcomeIndex admissionRisk informationModerate riskCoronary interventionPercutaneous coronary intervention proceduresRisk factorsPatient characteristicsIn-hospital bleeding eventsHigh riskLow riskMedical historyRegistryUpdate to the JACC Report Card Excess Cardiovascular Mortality Among Black Americans, 2000-2023
Arun A, Sawano M, Lu Y, Warner F, Caraballo C, Khera R, Echols M, Yancy C, Krumholz H. Update to the JACC Report Card Excess Cardiovascular Mortality Among Black Americans, 2000-2023. Journal Of The American College Of Cardiology 2025, 86: 559-562. PMID: 40835364, DOI: 10.1016/j.jacc.2025.06.008.Peer-Reviewed Original ResearchRisk of Thyroid Tumors With GLP-1 Receptor Agonists: A Retrospective Cohort Study
Morales D, Bu F, Viernes B, DuVall S, Matheny M, Simon K, Falconer T, Richter L, Ostropolets A, Lau W, Man K, Chattopadhyay S, Mathioudakis N, Minty E, Nishimura A, Sun F, Yin C, Seager S, Chai Y, Zhou J, Lu Y, Reyes C, Pistillo A, Duarte-Salles T, Blacketer C, Schuemie M, Ryan P, Krumholz H, Hripcsak G, Khera R, Suchard M. Risk of Thyroid Tumors With GLP-1 Receptor Agonists: A Retrospective Cohort Study. Diabetes Care 2025, 48: 1386-1394. PMID: 40465422, PMCID: PMC12281980, DOI: 10.2337/dc25-0154.Peer-Reviewed Original ResearchRisk of thyroid tumorThyroid tumorsGLP-1RADPP-4isHazard ratioThyroid malignancyCohort studyIncreased riskSodium-glucose cotransporter 2 inhibitorsElectronic health record databaseGlucagon-like peptide 1 receptor agonistsEstimate hazard ratiosRandom-effects meta-analysisGLP-1 receptor agonistsPeptide 1 receptor agonistsDipeptidyl peptidase 4 inhibitorsThyroid tumor incidenceUsers of SGLT2isHealth record databaseSecond-line treatmentType 2 diabetes mellitusNew-user cohort studyRetrospective cohort studyUsers of sulfonylureasIntention-to-treatArtificial 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 ageA Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.
Zhou X, Dhingra L, Aminorroaya A, Adejumo P, Khera R. A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model. AMIA Annual Symposium Proceedings 2025, 2024: 1332-1339. PMID: 40417570.Peer-Reviewed Original ResearchConceptsCommon data modelElectronic health recordsOMOP Common Data ModelSchema mappingsMapping electronic health recordData modelTransformer-based deep learning modelsNatural language processing approachEnd-to-endDeep learning modelsHealth recordsEnhance interoperabilityTransformation pipelineLearning modelsOMOPProcessing approachSchemaStandard conceptsDiverse healthcare systemsInteroperabilityLarge-scaleStandard mapDatasetSoftwareHealthcare systemArtificial Intelligence in the Management of Heart Failure
Cheema B, Hourmozdi J, Kline A, Ahmad F, Khera R. Artificial Intelligence in the Management of Heart Failure. Journal Of Cardiac Failure 2025 PMID: 40345521, DOI: 10.1016/j.cardfail.2025.02.020.Peer-Reviewed Original ResearchArtificial intelligenceState-of-the-art algorithmsData privacy concernsState-of-the-artManagement of heart failureAI-based toolsElectronic health recordsAI solutionsMultimodal dataHeart failureHealth recordsIntegration challengesHeart failure syndromeStructural heart diseaseHeart failure treatmentIntelligenceImplementation challengesModel performanceModel governanceAdvanced diseaseFailure syndromeCardiomyopathy diagnosisFailure treatmentRisk factorsHeart disease
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