Aaron S. Eisman, MD, PhD
Hospital ResidentCards
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Research
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Featured Publications
Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma
Benson M, Eisman A, Tahir U, Katz D, Deng S, Ngo D, Robbins J, Hofmann A, Shi X, Zheng S, Keyes M, Yu Z, Gao Y, Farrell L, Shen D, Chen Z, Cruz D, Sims M, Correa A, Tracy R, Durda P, Taylor K, Liu Y, Johnson W, Guo X, Yao J, Chen Y, Manichaikul A, Jain D, Yang Q, Consortium N, Bouchard C, Sarzynski M, Rich S, Rotter J, Wang T, Wilson J, Clish C, Sarkar I, Natarajan P, Gerszten R. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma. Cell Metabolism 2023, 35: 1646-1660.e3. PMID: 37582364, PMCID: PMC11118091, DOI: 10.1016/j.cmet.2023.07.012.Peer-Reviewed Original ResearchConceptsAssociation studiesPlasma metabolomics studyKnockout strainGene-metabolitesProtein regulationHuman geneticsProteomic determinantsHuman plasmaMendelian randomizationSignaling pathwayThyroxine-binding globulinMetabolomics studiesMR associationsProteinBioactive proteinsMetabolite levelsHuman cohortsPlasma samplesUnrecognized associationCirculating proteinsHuman metabolismBiochemical profileMetabolitesAssociationPlasmaClinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types.
Eisman A, Brown K, Chen E, Sarkar I. Clinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types. AMIA Annual Symposium Proceedings 2022, 2021: 418-427. PMID: 35308919, PMCID: PMC8861726.Peer-Reviewed Original ResearchConceptsNatural language processingUnified Medical Language SystemNatural language processing tasksMedical Language SystemSources of biomedical dataClinical note sectionsUnified Medical Language System semantic typesHidden Markov ModelNLP toolsLanguage processingBiomedical dataSection detectionClinical notesSemantic typesHMMLanguage systemMedical Information Mart for Intensive Care IIIExtracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.
Eisman A, Shah N, Eickhoff C, Zerveas G, Chen E, Wu W, Sarkar I. Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures. AMIA Annual Symposium Proceedings 2021, 2020: 412-421. PMID: 33936414, PMCID: PMC8075440.Peer-Reviewed Original ResearchConceptsChest painAnginal symptomsPalliation of chest painSubsternal chest painShortness of breathPrimary care physicians' notes.Consecutive patientsCardiac testingCardiac riskAngina symptomsPainCardiovascular managementPhysician notesSymptomsClinical notesIllness sectionTransformer architectureSample sizePulmonary Capillary Wedge Pressure Patterns During Exercise Predict Exercise Capacity and Incident Heart Failure
Eisman A, Shah R, Dhakal B, Pappagianopoulos P, Wooster L, Bailey C, Cunningham T, Hardin K, Baggish A, Ho J, Malhotra R, Lewis G. Pulmonary Capillary Wedge Pressure Patterns During Exercise Predict Exercise Capacity and Incident Heart Failure. Circulation Heart Failure 2018, 11: e004750. PMID: 29695381, PMCID: PMC5937988, DOI: 10.1161/circheartfailure.117.004750.Peer-Reviewed Original ResearchConceptsHeart failure outcomesExercise capacityCardiac outcomesHeart failureLeft ventricular ejection fractionLeft ventricular filling pressureMeasure of left ventricular filling pressureComposite cardiac outcomeDefinitions of HFpEFExercise hemodynamic measurementsPeak VO<sub>2</sub>Ventricular ejection fractionAdverse cardiac outcomesVentricular filling pressureFailure outcomesCardiopulmonary exercise testingBody mass indexPredicting exercise capacityIncident heart failureDifferentiate HFpEFNon-HFpEFEjection fractionHFpEF diagnosisMm HgFollow-upLearning health system linchpins: information exchange and a common data model
Eisman A, Chen E, Wu W, Crowley K, Aluthge D, Brown K, Sarkar I. Learning health system linchpins: information exchange and a common data model. Journal Of The American Medical Informatics Association 2024, ocae277. PMID: 39538369, DOI: 10.1093/jamia/ocae277.Peer-Reviewed Original ResearchLearning health systemObservational Medical Outcomes PartnershipAtherosclerotic cardiovascular diseaseHealth systemHealth information technology infrastructureFederally Qualified Health CentersASCVD riskPopulation health researchHealth information exchangeData partnersQualified Health CentersAtherosclerotic cardiovascular disease risk factorsPrimary prevention practicesRhode IslandIntervention developmentHealth recordsHealth centersHealth researchHIES dataIncident diseasePrevention practicesHealth dataPrimary preventionHealthRisk factors
2022
Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals
Tahir U, Katz D, Avila-Pachecho J, Bick A, Pampana A, Robbins J, Yu Z, Chen Z, Benson M, Cruz D, Ngo D, Deng S, Shi X, Zheng S, Eisman A, Farrell L, Hall M, Correa A, Tracy R, Durda P, Taylor K, Liu Y, Johnson W, Guo X, Yao J, Chen Y, Manichaikul A, Ruberg F, Blaner W, Jain D, Bouchard C, Sarzynski M, Rich S, Rotter J, Wang T, Wilson J, Clish C, Natarajan P, Gerszten R. Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals. Nature Communications 2022, 13: 4923. PMID: 35995766, PMCID: PMC9395431, DOI: 10.1038/s41467-022-32275-3.Peer-Reviewed Original ResearchConceptsGenome association studiesGenomic diversityAssociation studiesWhole-genome association studiesLocus-metabolite associationsIndividuals of European ancestryIntegrative omics approachMetabolite peaksGenetic informationCardiometabolic diseasesSpecific allelesEuropean ancestryOmics approachesSickle cell diseaseMulti-ethnic cohortTransthyretin amyloidosisAncestryHereditary diseaseIdentified metabolitesCell diseaseHuman metabolismUnknown metabolitesDiversityBlack individualsJackson Heart Study
2020
E-Consult Protocoling to Improve the Quality of Cardiac Stress Tests
Shah N, Eisman A, Winchester D, Morrison A, Qureshi R, Sarkar I, Wu W. E-Consult Protocoling to Improve the Quality of Cardiac Stress Tests. JACC Cardiovascular Imaging 2020, 14: 512-514. PMID: 33011119, PMCID: PMC8045139, DOI: 10.1016/j.jcmg.2020.08.009.Peer-Reviewed Original ResearchExercise Pulmonary Hypertension Predicts Clinical Outcomes in Patients With Dyspnea on Effort
Ho J, Zern E, Lau E, Wooster L, Bailey C, Cunningham T, Eisman A, Hardin K, Farrell R, Sbarbaro J, Schoenike M, Houstis N, Baggish A, Shah R, Nayor M, Malhotra R, Lewis G. Exercise Pulmonary Hypertension Predicts Clinical Outcomes in Patients With Dyspnea on Effort. Journal Of The American College Of Cardiology 2020, 75: 17-26. PMID: 31918830, PMCID: PMC7043927, DOI: 10.1016/j.jacc.2019.10.048.Peer-Reviewed Original ResearchConceptsExercise pulmonary hypertensionPulmonary artery pressurePulmonary hypertensionAbnormal pulmonary artery pressureCardiac outputChronic exertional dyspneaEvent-free survivalInvasive hemodynamic monitoringPredicting clinical outcomesBurden of PHCV event-free survivalCardiopulmonary exercise testingPredicting adverse eventsTargeted therapeutic interventionsPH subtypesExertional dyspneaPrognostic implicationsClinical outcomesAdverse eventsResting pHResponse to exerciseDeath eventsFollow-upHemodynamic assessmentArterial pressure
2019
Differential Clinical Profiles, Exercise Responses, and Outcomes Associated With Existing HFpEF Definitions
Ho J, Zern E, Wooster L, Bailey C, Cunningham T, Eisman A, Hardin K, Zampierollo G, Jarolim P, Pappagianopoulos P, Malhotra R, Nayor M, Lewis G. Differential Clinical Profiles, Exercise Responses, and Outcomes Associated With Existing HFpEF Definitions. Circulation 2019, 140: 353-365. PMID: 31132875, PMCID: PMC6684250, DOI: 10.1161/circulationaha.118.039136.Peer-Reviewed Original ResearchConceptsHeart Failure Society of AmericaEuropean Society of CardiologyClinical profileHeart failureCardiovascular outcomesComprehensive cardiopulmonary exercise testingHemodynamic profile of patientsChronic exertional dyspneaDefinitions of HFpEFExercise responseSuspected heart failureAmerican College of Cardiology/American Heart AssociationInvasive hemodynamic monitoringPhenotype of patientsIncidence of cardiovascular outcomesCardiopulmonary exercise testingProfile of patientsClinical trial criteriaSociety of CardiologyPeak oxygen uptakeConsecutive patientsHFpEF subgroupsEjection fractionExertional dyspneaPrognostic implications
2018
Impaired right ventricular reserve predicts adverse cardiac outcomes in adults with congenital right heart disease
Yeh D, Schmidt A, Eisman A, Serfas J, Naqvi M, Youniss M, Ryfa A, Khan A, Safi L, Tabtabai S, Bhatt A, Lewis G. Impaired right ventricular reserve predicts adverse cardiac outcomes in adults with congenital right heart disease. Heart 2018, 104: 2044. PMID: 30030334, DOI: 10.1136/heartjnl-2017-312572.Peer-Reviewed Original ResearchConceptsCardiopulmonary exercise testingRV reserveRight ventricleHeart failureNew York Heart Association classPrimary composite clinical outcomeFirst-pass radionuclide ventriculographyPrevalence of heart failureRV ejection fractionRV systolic functionEvent-free survivalMedian follow-upCongenital heart diseaseReserve groupComposite clinical outcomeAdverse cardiac outcomesTertiary care centreRight ventricular reserveAdverse cardiovascular outcomesPredicting adverse cardiovascular outcomesSystolic function assessmentRV dilatationACHD populationPrognostic impactAsymptomatic patients