Andreas Coppi
Associate Research Scientist (Cardiovascular Medicine)Cards
About
Research
Publications
2026
Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Coppi A, Shankar S, Rockers E, Mortazavi B, Bhatt D, Krumholz H, Al-Kindi S, Nadkarni G, Vaid A, Khera R. Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT. European Heart Journal - Digital Health 2026, 7: ztag031. PMID: 41853639, PMCID: PMC12993923, DOI: 10.1093/ehjdh/ztag031.Peer-Reviewed Original ResearchBundle branch blockEncoder-decoder modelBranch blockFascicular blockUS health systemClinical assessmentLeft anterior fascicular blockRight bundle branch blockLeft posterior fascicular blockAssessment of electrocardiogramsLeft bundle branch blockAnterior fascicular blockPosterior fascicular blockPremature atrial contractionsPremature ventricular contractionsHealth systemPTB-XL datasetLow-resource settingsAtrioventricular blockConduction abnormalitiesSinus bradycardiaAtrial contractionSinus tachycardiaAtrial fibrillationDiagnosis statementsTARGET-AI: A Foundational Approach for the Targeted Deployment of Artificial Intelligence Electrocardiography in the Electronic Health Record
Oikonomou E, Batinica B, Dhingra L, Aminorroaya A, Coppi A, Khera R. TARGET-AI: A Foundational Approach for the Targeted Deployment of Artificial Intelligence Electrocardiography in the Electronic Health Record. NEJM AI 2026, 3 DOI: 10.1056/aioa2500588.Peer-Reviewed Original ResearchClinical Trial Patient Matching: A Real-Time, Common Data Model and Artificial Intelligence–Driven System for Semiautomated Patient Prescreening in Cancer Clinical Trials
Gong G, Liu J, Pandya S, Taborda C, Wiesendanger N, Price N, Byron W, Coppi A, Young P, Wiess C, Dunning H, Barganier C, Brodeur R, Fischbach N, LoRusso P, Pusztai L, Kim S, Rozenblit M, Cecchini M, Mongiu A, Mendez L, Kaftan E, Torre C, Krumholz H, Krop I, Schulz W, Lustberg M, Kunz P. Clinical Trial Patient Matching: A Real-Time, Common Data Model and Artificial Intelligence–Driven System for Semiautomated Patient Prescreening in Cancer Clinical Trials. JCO Clinical Cancer Informatics 2026, 10: e2500262. PMID: 41512229, DOI: 10.1200/cci-25-00262.Peer-Reviewed Original ResearchConceptsObservational Medical Outcomes PartnershipHealth systemColorectal cancerElectronic health record dataCancer clinical trial enrollmentChart reviewHealth record dataManual chart reviewClinical trial recruitmentClinical trialsCancer clinical trialsCancer specialtiesCommon data modelScreen timeColorectal cancer trialsClinical trial enrollmentTrial recruitmentClinical chart reviewConsent ratesPatient accessExhaustive chart reviewMetastatic colorectal cancerEnrollment challengesRecord dataTrial enrollmentAssessment of the integrity of real-time electronic health record data used in clinical research
Liu J, Pandya S, Coppi A, Young H, Krumholz H, Schulz W, Gong G. Assessment of the integrity of real-time electronic health record data used in clinical research. PLOS ONE 2026, 21: e0340287. PMID: 41511976, PMCID: PMC12788664, DOI: 10.1371/journal.pone.0340287.Peer-Reviewed Original ResearchConceptsElectronic health recordsEHR dataReal-time electronic health recordsElectronic health record dataSecondary useHealth record dataClinical actionsIntegration of real-time dataClinical trial readinessCommon data modelHealth recordsHealth systemOMOP Common Data ModelDischarge informationClinical careResearch readinessRecord dataTrial readinessSynthetic datasetsEHR datasetData warehouseDemographic variablesReal-time dataPost-encounterAutomated framework
2025
Artificial intelligence-enabled electrocardiography and echocardiography to track preclinical progression of transthyretin amyloid cardiomyopathy
Oikonomou E, Sangha V, 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, 46: 3651-3662. PMID: 40679604, PMCID: PMC12488324, 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 stageDevelopment 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, 6: 554-566. PMID: 40703117, PMCID: PMC12282373, 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 ECGNirmatrelvir–ritonavir versus placebo–ritonavir in individuals with long COVID in the USA (PAX LC): a double-blind, randomised, placebo-controlled, phase 2, decentralised trial
Sawano M, Bhattacharjee B, Caraballo C, Khera R, Li S, Herrin J, Christian D, Coppi A, Warner F, Holub J, Henriquez Y, Johnson M, Goddard T, Rocco E, Hummel A, Mouslmani M, Hooper W, Putrino D, Carr K, Charnas L, De Jesus M, Nepert D, Abreu P, Ziegler F, Spertus J, Iwasaki A, Krumholz H. Nirmatrelvir–ritonavir versus placebo–ritonavir in individuals with long COVID in the USA (PAX LC): a double-blind, randomised, placebo-controlled, phase 2, decentralised trial. The Lancet Infectious Diseases 2025, 25: 936-946. PMID: 40188838, DOI: 10.1016/s1473-3099(25)00073-8.Peer-Reviewed Original ResearchPhysical health summary scoreBaseline to dayAdverse eventsNirmatrelvir-ritonavirSARS-CoV-2 infectionDouble-blindStudy drug-related treatment-emergent adverse eventsDrug-related treatment-emergent adverse eventsTreatment-emergent adverse eventsIntention-to-treat populationWeek 6Baseline to week 6Documented SARS-CoV-2 infectionActive liver diseaseEffective pharmacological interventionsLong COVIDAcute medical illnessSafety populationPatient-Reported Outcomes Measurement Information SystemEarly treatment terminationRenal impairmentTreat long-COVIDPlacebo-ControlledEfficacy endpointRandomised controlled trialsEnsemble 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 cohortAssessment 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 characteristicsArtificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
Oikonomou E, Vaid A, Holste G, Coppi A, McNamara R, Baloescu C, Krumholz H, Wang Z, Apakama D, Nadkarni G, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health 2025, 7: e113-e123. PMID: 39890242, PMCID: PMC12084816, DOI: 10.1016/s2589-7500(24)00249-8.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemPoint-of-care ultrasonographyMount Sinai Health SystemTransthyretin amyloid cardiomyopathyArtificial intelligenceHealth systemAmyloid cardiomyopathyHypertrophic cardiomyopathyRetrospective cohort of individualsCardiomyopathy casesTesting artificial intelligenceConvolutional neural networkSinai Health SystemCohort of individualsOpportunistic screeningHypertrophic cardiomyopathy casesMulti-labelPositive screenAI frameworkEmergency departmentMortality riskNeural networkLoss functionCardiac ultrasonographyAugmentation approach