Featured Publications
Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha V, Nargesi A, Dhingra L, Khunte A, Mortazavi B, Ribeiro A, Banina E, Adeola O, Garg N, Brandt C, Miller E, Ribeiro A, Velazquez E, Giatti L, Barreto S, Foppa M, Yuan N, Ouyang D, Krumholz H, Khera R. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images. Circulation 2023, 148: 765-777. PMID: 37489538, PMCID: PMC10982757, DOI: 10.1161/circulationaha.122.062646.Peer-Reviewed Original ResearchConceptsLV systolic dysfunctionYale-New Haven HospitalVentricular systolic dysfunctionSystolic dysfunctionLV ejection fractionBrazilian Longitudinal StudyNew Haven HospitalEjection fractionCardiology clinicRegional hospitalLeft ventricular systolic dysfunctionCedars-Sinai Medical CenterAdult Health (ELSA-Brasil) cohort
2024
Automated 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 qualityA 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 structureArtificial Intelligence for Cardiovascular Care—Part 1: Advances JACC Review Topic of the Week
Elias P, Jain S, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein A, Avram R, Tison G, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva J, Maddox T. Artificial Intelligence for Cardiovascular Care—Part 1: Advances JACC Review Topic of the Week. Journal Of The American College Of Cardiology 2024, 83: 2472-2486. PMID: 38593946, DOI: 10.1016/j.jacc.2024.03.400.Peer-Reviewed Original ResearchEnhanced image qualityHuman expertsLeverage AIEvaluation benchmarkArtificial intelligenceAI modelsAI advancementsDetect diseaseTraining methodsImage qualityReduced ejection fractionEvolving technologyValvular heart diseaseReal-world efficacyEjection fractionProvider experienceHeart diseaseTechnologyCardiovascular carePatient careUnique characteristicsIntelligenceBenchmarks
2023
Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography
Oikonomou E, Holste G, Mcnamara R, Velazquez E, Nadkarni G, Ouyang D, Krumholz H, Wang Z, Khera R. Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography. European Heart Journal 2023, 44: ehad655.040. DOI: 10.1093/eurheartj/ehad655.040.Peer-Reviewed Original ResearchLeft ventricular ejection fractionSevere aortic stenosisAortic stenosisAS progressionAV VmaxTransthoracic echocardiographyYale New Haven Health SystemBaseline left ventricular ejection fractionAortic stenosis progressionModerate aortic stenosisRetrospective cohort studyVentricular ejection fractionTwo-dimensional echocardiographyMean rateModerate ASAS severityCohort studyEjection fractionPatient sexStenosis progressionTTE studiesEligible participantsSerial monitoringSpecialized centersTimely diagnosisDEEP LEARNING-BASED IDENTIFICATION OF HEART FAILURE WITH REDUCED EJECTION FRACTION IN CLINICAL DOCUMENTATION TO AUTOMATE CARE QUALITY ASSESSMENT IN HEART FAILURE
Nargesi A, Rosand B, Hengartner A, Adejumo P, Khera R. DEEP LEARNING-BASED IDENTIFICATION OF HEART FAILURE WITH REDUCED EJECTION FRACTION IN CLINICAL DOCUMENTATION TO AUTOMATE CARE QUALITY ASSESSMENT IN HEART FAILURE. Journal Of The American College Of Cardiology 2023, 81: 2302. DOI: 10.1016/s0735-1097(23)02746-8.Peer-Reviewed Original Research
2021
Nonalcoholic Fatty Liver Disease and Risk of Heart Failure Among Medicare Beneficiaries
Fudim M, Zhong L, Patel KV, Khera R, Abdelmalek MF, Diehl AM, McGarrah RW, Molinger J, Moylan CA, Rao VN, Wegermann K, Neeland IJ, Halm EA, Das SR, Pandey A. Nonalcoholic Fatty Liver Disease and Risk of Heart Failure Among Medicare Beneficiaries. Journal Of The American Heart Association 2021, 10: e021654. PMID: 34755544, PMCID: PMC8751938, DOI: 10.1161/jaha.121.021654.Peer-Reviewed Original ResearchConceptsNonalcoholic fatty liver diseaseIncident heart failureReduced ejection fractionFatty liver diseaseHeart failureEjection fractionMedicare beneficiariesHF subtypesLiver diseaseHigh riskBackground Nonalcoholic fatty liver diseaseBaseline NAFLDAssociation of NAFLDNew-onset heart failureConclusions PatientsCohort studyPrior diagnosisBlack patientsNinth RevisionKidney diseaseOutpatient claimsRisk factorsIndependent associationHigh burdenMedicare patients
2019
Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction
Angraal S, Mortazavi BJ, Gupta A, Khera R, Ahmad T, Desai NR, Jacoby DL, Masoudi FA, Spertus JA, Krumholz HM. Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction. JACC Heart Failure 2019, 8: 12-21. PMID: 31606361, DOI: 10.1016/j.jchf.2019.06.013.Peer-Reviewed Original ResearchConceptsHF hospitalizationRisk of mortalityEjection fractionBlood urea nitrogen levelsLogistic regressionPrevious HF hospitalizationHeart failure hospitalizationReduced ejection fractionReceiver-operating characteristic curveRisk of deathBody mass indexBlood urea nitrogenUrea nitrogen levelsHealth status dataMean c-statisticKCCQ scoresTOPCAT trialFailure hospitalizationHeart failureHemoglobin levelsMass indexC-statisticHospitalizationUrea nitrogenMortality
2018
Relative Impairments in Hemodynamic Exercise Reserve Parameters in Heart Failure With Preserved Ejection Fraction A Study-Level Pooled Analysis
Pandey A, Khera R, Park B, Haykowsky M, Borlaug BA, Lewis GD, Kitzman DW, Butler J, Berry JD. Relative Impairments in Hemodynamic Exercise Reserve Parameters in Heart Failure With Preserved Ejection Fraction A Study-Level Pooled Analysis. JACC Heart Failure 2018, 6: 117-126. PMID: 29413366, PMCID: PMC8135913, DOI: 10.1016/j.jchf.2017.10.014.Peer-Reviewed Original ResearchConceptsPulmonary capillary wedge pressureCapillary wedge pressurePeak oxygen uptakePooled analysisWedge pressureEchocardiographic parametersHeart failureExercise intoleranceExaggerated increaseReserve parametersControl groupLower peak oxygen uptakeRelative impairmentStroke volume reserveOxygen uptakeChronic HFpEFHemodynamic reserveExercise toleranceExercise capacityEjection fractionChronotropic reserveControl subjectsCardinal manifestationsHFpEFSignificant abnormalities