2024
Artificial 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 Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression
Oikonomou E, Holste G, Yuan N, Coppi A, McNamara R, Haynes N, Vora A, Velazquez E, Li F, Menon V, Kapadia S, Gill T, Nadkarni G, Krumholz H, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiology 2024, 9: 534-544. PMID: 38581644, PMCID: PMC10999005, DOI: 10.1001/jamacardio.2024.0595.Peer-Reviewed Original ResearchCardiac magnetic resonanceAortic valve replacementCardiac magnetic resonance imagingAV VmaxSevere ASAortic stenosisCohort studyPeak aortic valve velocityCohort study of patientsAortic valve velocityCohort of patientsTraditional cardiovascular risk factorsAssociated with faster progressionStudy of patientsCedars-Sinai Medical CenterAssociated with AS developmentCardiovascular risk factorsCardiovascular imaging modalitiesIndependent of ageModerate ASEjection fractionEchocardiographic studiesValve replacementRisk stratificationCardiac structureAutomated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing
Nargesi A, Adejumo P, Dhingra L, Rosand B, Hengartner A, Coppi A, Benigeri S, Sen S, Ahmad T, Nadkarni G, Lin Z, Ahmad F, Krumholz H, Khera R. Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing. JACC Heart Failure 2024 PMID: 39453355, DOI: 10.1016/j.jchf.2024.08.012.Peer-Reviewed Original ResearchReduced ejection fractionEjection fractionHeart failureLeft ventricular ejection fractionVentricular ejection fractionYale-New Haven HospitalIdentification of patientsCommunity hospitalIdentification of heart failureLanguage modelNorthwestern MedicineMeasure care qualityQuality of careNew Haven HospitalDeep learning-based natural language processingHFrEFGuideline-directed careDeep learning language modelsMIMIC-IIIDetect HFrEFNatural language processingReclassification improvementHospital dischargePatientsCare qualityThe PAX LC Trial: A Decentralized, Phase 2, Randomized, Double-blind Study of Nirmatrelvir/Ritonavir Compared with Placebo/Ritonavir for Long COVID
Krumholz H, Sawano M, Bhattacharjee B, Caraballo C, Khera R, Li S, Herrin J, Coppi A, Holub J, Henriquez Y, Johnson M, Goddard T, Rocco E, Hummel A, Al Mouslmani M, Putrino D, Carr K, Carvajal-Gonzalez S, Charnas L, De Jesus M, Ziegler F, Iwasaki A. The PAX LC Trial: A Decentralized, Phase 2, Randomized, Double-blind Study of Nirmatrelvir/Ritonavir Compared with Placebo/Ritonavir for Long COVID. The American Journal Of Medicine 2024 PMID: 38735354, DOI: 10.1016/j.amjmed.2024.04.030.Peer-Reviewed Original ResearchLC trialPROMIS-29Participants' homesTargeting viral persistencePlacebo-controlled trialDouble-blind studyElectronic health recordsCore Outcome MeasuresLong COVIDEQ-5D-5LRepeated measures analysisEvidence-based treatmentsPhase 2Double-blindParticipant-centred approachStudy drugPrimary endpointSecondary endpointsCommunity-dwellingHealth recordsHealthcare utilizationContiguous US statesViral persistencePatient groupDrug treatment
2023
Severe aortic stenosis detection by deep learning applied to echocardiography
Holste G, Oikonomou E, Mortazavi B, Coppi A, Faridi K, Miller E, Forrest J, McNamara R, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz H, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. European Heart Journal 2023, 44: 4592-4604. PMID: 37611002, PMCID: PMC11004929, DOI: 10.1093/eurheartj/ehad456.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 electrocardiographyTraining
2021
Temporal relationship of computed and structured diagnoses in electronic health record data
Schulz WL, Young HP, Coppi A, Mortazavi BJ, Lin Z, Jean RA, Krumholz HM. Temporal relationship of computed and structured diagnoses in electronic health record data. BMC Medical Informatics And Decision Making 2021, 21: 61. PMID: 33596898, PMCID: PMC7890604, DOI: 10.1186/s12911-021-01416-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsStructured diagnosisOutpatient blood pressureElectronic health record dataAcademic health systemLow-density lipoproteinHealth record dataBlood pressureStructured data elementsAdministrative claimsHypertensionClinical informationHyperlipidemiaClinical phenotypeEquivalent diagnosisVital signsHealth systemDiagnosisProblem listAdditional studiesHealth recordsRecord dataTimely accessEHR dataPatientsToward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft
Mori M, Durant TJS, Huang C, Mortazavi BJ, Coppi A, Jean RA, Geirsson A, Schulz WL, Krumholz HM. Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft. Circulation Cardiovascular Quality And Outcomes 2021, 14: e007363. PMID: 34078100, PMCID: PMC8635167, DOI: 10.1161/circoutcomes.120.007363.Peer-Reviewed Original ResearchConceptsCoronary artery bypass graftArtery bypass graftIntraoperative variablesBypass graftLogistic regression modelsOperative mortalityC-statisticCoronary artery bypass graft casesThoracic Surgeons Adult Cardiac Surgery DatabaseAdult Cardiac Surgery DatabaseMean patient ageGood c-statisticCardiac Surgery DatabaseBrier scoreRisk restratificationDynamic risk predictionIntraoperative deathsPostoperative complicationsPostoperative eventsAdverse eventsPatient agePreoperative variablesRegression modelsGraft casesSurgery Database
2017
Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures
Mortazavi B, Desai N, Zhang J, Coppi A, Warner F, Krumholz H, Negahban S. Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. IEEE Journal Of Biomedical And Health Informatics 2017, 21: 1719-1729. PMID: 28287993, DOI: 10.1109/jbhi.2017.2675340.Peer-Reviewed Original ResearchConceptsMajor cardiovascular proceduresElectronic health recordsRespiratory failureAdverse eventsCardiovascular proceduresYale-New Haven HospitalPostoperative respiratory failurePatient cohortHospital costsPatient outcomesSpecific patientPatientsHealth recordsCohort-specific modelsCharacteristic curveInfectionFailureHospitalCohortCliniciansSystolic Blood Pressure Response in SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to Control Cardiovascular Risk in Diabetes): A Possible Explanation for Discordant Trial Results
Huang C, Dhruva SS, Coppi AC, Warner F, Li S, Lin H, Nasir K, Krumholz HM. Systolic Blood Pressure Response in SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to Control Cardiovascular Risk in Diabetes): A Possible Explanation for Discordant Trial Results. Journal Of The American Heart Association 2017, 6: e007509. PMID: 29133522, PMCID: PMC5721802, DOI: 10.1161/jaha.117.007509.Peer-Reviewed Original ResearchConceptsSystolic blood pressure responseBlood pressure responseTreatment groupsCause deathVisit variabilityDiscordant trialsBlood pressure trialStandard treatment groupPressure responseACCORD participantsPressure trialSBP responseHeart failureMean SBPPrimary outcomeSBPDiscordant resultsMean differenceSimilar interventionsTrial resultsTrialsSimilar mean differencesTreatment effectsSignificant differencesStrokeHeterogeneity in Early Responses in ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial)
Dhruva SS, Huang C, Spatz ES, Coppi AC, Warner F, Li SX, Lin H, Xu X, Furberg CD, Davis BR, Pressel SL, Coifman RR, Krumholz HM. Heterogeneity in Early Responses in ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial). Hypertension 2017, 70: 94-102. PMID: 28559399, DOI: 10.1161/hypertensionaha.117.09221.Peer-Reviewed Original ResearchConceptsAntihypertensive therapySystolic blood pressure responseAdverse cardiovascular eventsFavorable initial responseBlood pressure responseHigher hazard ratioCardiovascular eventsCardiovascular outcomesHazard ratioMultivariable adjustmentHeart failureAverage SBPRandomized trialsOdds ratioCardiovascular diseaseSBPStudy participantsRespondersMonthsPressure responseImmediate respondersALLHATEarly responseInitial responseSuperior discriminationDiscovery of temporal and disease association patterns in condition-specific hospital utilization rates
Haimovich JS, Venkatesh AK, Shojaee A, Coppi A, Warner F, Li SX, Krumholz HM. Discovery of temporal and disease association patterns in condition-specific hospital utilization rates. PLOS ONE 2017, 12: e0172049. PMID: 28355219, PMCID: PMC5371293, DOI: 10.1371/journal.pone.0172049.Peer-Reviewed Original Research
2020
Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment
Durant TJS, Jean RA, Huang C, Coppi A, Schulz WL, Geirsson A, Krumholz HM. Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment. JAMA Network Open 2020, 3: e2028361. PMID: 33284333, DOI: 10.1001/jamanetworkopen.2020.28361.Peer-Reviewed Original Research
2019
Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data
Krumholz HM, Warner F, Coppi A, Triche EW, Li SX, Mahajan S, Li Y, Bernheim SM, Grady J, Dorsey K, Desai NR, Lin Z, Normand ST. Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data. JAMA Network Open 2019, 2: e198406. PMID: 31411709, PMCID: PMC6694388, DOI: 10.1001/jamanetworkopen.2019.8406.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionHeart failurePopulation-based programsPOA codesSingle diagnostic codeDiagnostic codesComparative effectiveness research studyPublic reportingIndex admission diagnosisDays of hospitalizationClinical Modification codesService claims dataAcute care hospitalsMultiple care settingsPatient-level modelsAdmission diagnosisTotal hospitalizationsCare hospitalPrevious diagnosisNinth RevisionMyocardial infarctionCandidate variablesCare settingsClaims dataMAIN OUTCOMEComparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data
Krumholz HM, Coppi AC, Warner F, Triche EW, Li SX, Mahajan S, Li Y, Bernheim SM, Grady J, Dorsey K, Lin Z, Normand ST. Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data. JAMA Network Open 2019, 2: e197314. PMID: 31314120, PMCID: PMC6647547, DOI: 10.1001/jamanetworkopen.2019.7314.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionICD-9-CM codesMortality risk modelHeart failureHospital admissionC-statisticMAIN OUTCOMEMortality rateRisk-standardized mortality ratesHospital risk-standardized mortality ratesIndex admission diagnosisPatients 65 yearsDays of hospitalizationComparative effectiveness studiesClaims-based dataHospital-level performance measuresMedicare claims dataPatient-level modelsCMS modelRisk-adjustment modelsRisk modelHospital performance measuresAdmission diagnosisNinth RevisionMyocardial infarctionHealth Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform
McPadden J, Durant TJ, Bunch DR, Coppi A, Price N, Rodgerson K, Torre CJ, Byron W, Hsiao AL, Krumholz HM, Schulz WL. Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform. Journal Of Medical Internet Research 2019, 21: e13043. PMID: 30964441, PMCID: PMC6477571, DOI: 10.2196/13043.Peer-Reviewed Original ResearchConceptsData science platformOpen source technologiesHealth care dataSource technologiesModern big data platformScience platformData management approachTraditional computer systemsBig data workloadsBig data platformBig data sourcesReal-time datasetBiomedical research dataHealth care applicationsData-driven researchPatient monitoringScalable analyticsApache StormData lakeData workloadsAnalytics platformData acquisition workflowContinuous patient monitoringUse casesData platform
2018
Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators
Bates J, Parzynski CS, Dhruva SS, Coppi A, Kuntz R, Li S, Marinac‐Dabic D, Masoudi FA, Shaw RE, Warner F, Krumholz HM, Ross JS. Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators. Pharmacoepidemiology And Drug Safety 2018, 27: 848-856. PMID: 29896873, PMCID: PMC6436550, DOI: 10.1002/pds.4565.Peer-Reviewed Original ResearchConceptsAdverse event ratesSafety differencesEvent ratesMedical device utilizationICD utilizationRate ratioNational Cardiovascular Data RegistryICD modelsImplantable cardioverter defibrillatorEvent rate ratioMost patientsCardioverter defibrillatorProportion of individualsAmerican CollegeData registryRoutine surveillanceSample size estimatesAverage event rateDevice utilizationSignificance levelDifferencesPatientsRegistryDefibrillatorICD