2024
A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device
Sangha V, Dhingra L, Khera R. A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device. European Heart Journal 2024, 45: ehae666.3437. DOI: 10.1093/eurheartj/ehae666.3437.Peer-Reviewed Original ResearchDeep learning modelsPDF outputArtificial intelligenceLearning modelsConvolutional neural networkPortable devicesPortable ECG deviceMultilabel modelEfficientNet-B3Neural networkECG outputWearable ECGHeld-out subsetCurrent algorithmsSingle-lead ECGSynthetic ECGECG deviceWearableClinical ECGClinical labelsAlgorithmCommercial devicesDetection of conductivityNo current algorithmPDFCharacterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms
Oikonomou E, Sangha V, Coppi A, Krumholz H, Miller E, Khera R. Characterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms. European Heart Journal 2024, 45: ehae666.2089. DOI: 10.1093/eurheartj/ehae666.2089.Peer-Reviewed Original ResearchDiagnosis of ATTR-CMATTR-CMBone scintigraphy scansClinical diagnosisTransthyretin amyloid cardiomyopathyMonths of diagnosisSex-matched controlsElectrocardiographic (ECGIndolent courseCardiac amyloidosisScintigraphy scanAmyloid cardiomyopathyEchocardiographic studiesAI-ECGEchocardiogramEventual diagnosisDetect longitudinal changesConfirmatory testDiagnosisClinical diseasePercentage of individualsLongitudinal changesECGMedianMonthsArtificial intelligence applied to electrocardiographic images for the risk stratification of cancer therapeutics-related cardiac dysfunction
Oikonomou E, Sangha V, Dhingra L, Aminorroaya A, Coppi A, Krumholz H, Baldassarre L, Khera R. Artificial intelligence applied to electrocardiographic images for the risk stratification of cancer therapeutics-related cardiac dysfunction. European Heart Journal 2024, 45: ehae666.3190. DOI: 10.1093/eurheartj/ehae666.3190.Peer-Reviewed Original ResearchCancer therapeutics-related cardiac dysfunctionImmune checkpoint inhibitorsGlobal longitudinal strainLeft ventricular systolic dysfunctionNon-Hodgkin's lymphomaCardiac dysfunctionAI-ECGNegative control analysesAssociated with higher incidenceVentricular systolic dysfunctionCohort of patientsRisk stratification strategiesCheckpoint inhibitorsTrastuzumab exposureSystolic dysfunctionRisk stratificationBreast cancerRisk biomarkersSecondary outcomesLongitudinal strainStratification strategiesTrastuzumabPatientsHigher incidenceAnthracyclinesArtificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Sangha V, Oikonomou E, Krumholz H, Miller E, Khera R. Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis. European Heart Journal 2024, 45: ehae666.3436. DOI: 10.1093/eurheartj/ehae666.3436.Peer-Reviewed Original ResearchATTR-CMBone scintigraphy scansTransthyretin amyloid cardiomyopathyPositive predictive valueAI-ECG algorithmCardiac amyloidosisScintigraphy scanAmyloid cardiomyopathyAI-ECGSex-matchedDevelopment cohortMyocardial remodelingUnder-diagnosedUnder-treatedMatched controlsPredictive valueUnder-recognizedTransthyretin stabilizersConvolutional neural networkPatientsECGArtificial intelligenceHospitalPrevalenceTransthyretinDetection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms
Sangha V, Oikonomou E, Khunte A, Miller E, Khera R. Detection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms. European Heart Journal 2024, 45: ehae666.3438. DOI: 10.1093/eurheartj/ehae666.3438.Peer-Reviewed Original ResearchReal-world noiseSingle-lead ECGArtificial intelligence algorithmsMultiple signal-to-noise ratiosCommunity-dwelling adultsSignal-to-noise ratioIntelligence algorithmsATTR-CMMatched controlsECG signalsDevelopment cohortPreventive careHealthcare servicesBlack adultsHospital systemCommunity screeningAlgorithmBone scintigraphy scansATTR cardiac amyloidosisPrevalence levelsOnset of symptomsPositive predictive valuePrevalenceAI-ECG algorithmSex-matchedArtificial 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 screenA WEARABLE-ADAPTED ARTIFICIAL INTELLIGENCE ALGORITHM FOR HEART FAILURE PREDICTION FROM SINGLE-LEAD ELECTROCARDIOGRAMS IN A LARGE NATIONWIDE COHORT STUDY
Dhingra L, Aminorroaya A, Oikonomou E, Sangha V, Khunte A, Khera R. A WEARABLE-ADAPTED ARTIFICIAL INTELLIGENCE ALGORITHM FOR HEART FAILURE PREDICTION FROM SINGLE-LEAD ELECTROCARDIOGRAMS IN A LARGE NATIONWIDE COHORT STUDY. Journal Of The American College Of Cardiology 2024, 83: 2341. DOI: 10.1016/s0735-1097(24)04331-6.Peer-Reviewed Original ResearchHEART FAILURE RISK PREDICTION USING ARTIFICIAL INTELLIGENCE ON ECG PHOTOS IN LARGE CONTEMPORARY COHORT
Dhingra L, Sangha V, Aminorroaya A, Camargos A, Oikonomou E, Khera R. HEART FAILURE RISK PREDICTION USING ARTIFICIAL INTELLIGENCE ON ECG PHOTOS IN LARGE CONTEMPORARY COHORT. Journal Of The American College Of Cardiology 2024, 83: 277. DOI: 10.1016/s0735-1097(24)02267-8.Peer-Reviewed Original ResearchUSING PHOTOS OF ELECTROCARDIOGRAMS AS A BIOMARKER FOR CARDIOVASCULAR RISK - A MULTINATIONAL ASSESSMENT OF A NOVEL ARTIFICIAL INTELLIGENCE APPROACH
Sangha V, Dhingra L, Aminorroaya A, Khera R. USING PHOTOS OF ELECTROCARDIOGRAMS AS A BIOMARKER FOR CARDIOVASCULAR RISK - A MULTINATIONAL ASSESSMENT OF A NOVEL ARTIFICIAL INTELLIGENCE APPROACH. Journal Of The American College Of Cardiology 2024, 83: 2443. DOI: 10.1016/s0735-1097(24)04433-4.Peer-Reviewed Original ResearchBiometric contrastive learning for data-efficient deep learning from electrocardiographic images
Sangha V, Khunte A, Holste G, Mortazavi B, Wang Z, Oikonomou E, Khera R. Biometric contrastive learning for data-efficient deep learning from electrocardiographic images. Journal Of The American Medical Informatics Association 2024, 31: 855-865. PMID: 38269618, PMCID: PMC10990541, DOI: 10.1093/jamia/ocae002.Peer-Reviewed Original ResearchLabeled training dataContrastive learningECG imagesLabeled dataTraining dataDeep learningProportions of labeled dataArtificial intelligenceSelf-supervised contrastive learningTraditional supervised learningConvolutional neural networkHeld-out test setSupervised learningPretraining strategyBiometric signatureImageNet initializationPretraining approachNeural networkImageNetAI modelsImage objectsTest setLearningDetect atrial fibrillationEquivalent performance
2023
Augmenting reality in echocardiography
Sangha V. Augmenting reality in echocardiography. Heart 2023, 110: 387-388. PMID: 37940380, DOI: 10.1136/heartjnl-2023-323443.Peer-Reviewed Original ResearchDetection 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 electrocardiographyTrainingDEEP LEARNING-BASED DETECTION OF LEFT VENTRICULAR SYSTOLIC DYSFUNCTION FROM NOISY SINGLE LEAD ELECTROCARDIOGRAPHY ADAPTED FOR WEARABLE DEVICES
Khunte A, Sangha V, Dhingra L, Oikonomou E, Mortazavi B, Khera R. DEEP LEARNING-BASED DETECTION OF LEFT VENTRICULAR SYSTOLIC DYSFUNCTION FROM NOISY SINGLE LEAD ELECTROCARDIOGRAPHY ADAPTED FOR WEARABLE DEVICES. Journal Of The American College Of Cardiology 2023, 81: 2262. DOI: 10.1016/s0735-1097(23)02706-7.Peer-Reviewed Original ResearchSMART-AS: A NOVEL ARTIFICIAL INTELLIGENCE TOOL TO DETECT SEVERE AORTIC STENOSIS FROM ELECTROCARDIOGRAPHIC IMAGES
Sangha V, Oikonomou E, Khunte A, Gupta K, Mortazavi B, Khera R. SMART-AS: A NOVEL ARTIFICIAL INTELLIGENCE TOOL TO DETECT SEVERE AORTIC STENOSIS FROM ELECTROCARDIOGRAPHIC IMAGES. Journal Of The American College Of Cardiology 2023, 81: 2409. DOI: 10.1016/s0735-1097(23)02853-x.Peer-Reviewed Original ResearchBIOMETRIC CONTRASTIVE MODELING FOR DATA-EFFICIENT DEEP LEARNING FROM ELECTROCARDIOGRAPHIC IMAGES
Sangha V, Khunte A, Holste G, Mortazavi B, Wang Z, Oikonomou E, Khera R. BIOMETRIC CONTRASTIVE MODELING FOR DATA-EFFICIENT DEEP LEARNING FROM ELECTROCARDIOGRAPHIC IMAGES. Journal Of The American College Of Cardiology 2023, 81: 2403. DOI: 10.1016/s0735-1097(23)02847-4.Peer-Reviewed Original Research
2022
County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries
Hanna J, Nargesi AA, Essien UR, Sangha V, Lin Z, Krumholz HM, Khera R. County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries. American Journal Of Preventive Cardiology 2022, 11: 100370. PMID: 35968531, PMCID: PMC9364091, DOI: 10.1016/j.ajpc.2022.100370.Peer-Reviewed Original ResearchAutomated multilabel diagnosis on electrocardiographic images and signals
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, Schulz WL, Krumholz HM, Ribeiro ALP, Khera R. Automated multilabel diagnosis on electrocardiographic images and signals. Nature Communications 2022, 13: 1583. PMID: 35332137, PMCID: PMC8948243, DOI: 10.1038/s41467-022-29153-3.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArtificial intelligenceApplication of AISignal-based dataSignal-based modelElectrocardiographic imagesECG imagesGrad-CAMImage-based modelsNeural networkDiagnosis modelECG signalsImagesClinical labelsValidation setLabelsExternal validation setMultilabelIntelligenceNetworkApplicationsModelBroad useSetBroader settingA multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations
Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. Npj Digital Medicine 2022, 5: 27. PMID: 35260762, PMCID: PMC8904579, DOI: 10.1038/s41746-022-00570-4.Peer-Reviewed Original ResearchCOVID-19 hospitalizationMayo ClinicDiagnosis codesCOVID-19 diagnosisPositive SARS-CoV-2 PCRYale New Haven Health SystemPositive SARS-CoV-2 testSARS-CoV-2 infectionSARS-CoV-2 PCRSARS-CoV-2 testCOVID-19Higher inhospital mortalitySARS-CoV2 infectionElectronic health record dataICD-10 diagnosisPositive laboratory testsHealth record dataInhospital mortalityAdditional patientsAntigen testSecondary diagnosisPrincipal diagnosisMulticenter evaluationPositive testComputable phenotype definitions
2021
Patterns of Prescribing Sodium-Glucose Cotransporter-2 Inhibitors for Medicare Beneficiaries in the United States
Sangha V, Lipska K, Lin Z, Inzucchi SE, McGuire DK, Krumholz HM, Khera R. Patterns of Prescribing Sodium-Glucose Cotransporter-2 Inhibitors for Medicare Beneficiaries in the United States. Circulation Cardiovascular Quality And Outcomes 2021, 14: e008381. PMID: 34779654, PMCID: PMC9022137, DOI: 10.1161/circoutcomes.121.008381.Peer-Reviewed Original ResearchConceptsType 2 diabetesMedicare beneficiariesSodium-glucose cotransporter 2 inhibitorsLarge randomized clinical trialsMedicare Part D prescriber dataChronic kidney diseaseCotransporter 2 inhibitorsAtherosclerotic cardiovascular diseasePercent of cliniciansRandomized clinical trialsUS Medicare beneficiariesAdvanced practice providersCross-sectional studyKidney outcomesSGLT2i useSulfonylurea prescriptionUnique cliniciansCardiovascular deathMedication classesKidney diseaseLabel indicationsClinical trialsSGLT2iCardiovascular diseasePractice providers