Characterizing 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 intelligenceHospitalPrevalenceTransthyretinArtificial 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 screen