2023
Distinguishing features of long COVID identified through immune profiling
Klein J, Wood J, Jaycox J, Dhodapkar R, Lu P, Gehlhausen J, Tabachnikova A, Greene K, Tabacof L, Malik A, Silva Monteiro V, Silva J, Kamath K, Zhang M, Dhal A, Ott I, Valle G, Peña-Hernández M, Mao T, Bhattacharjee B, Takahashi T, Lucas C, Song E, McCarthy D, Breyman E, Tosto-Mancuso J, Dai Y, Perotti E, Akduman K, Tzeng T, Xu L, Geraghty A, Monje M, Yildirim I, Shon J, Medzhitov R, Lutchmansingh D, Possick J, Kaminski N, Omer S, Krumholz H, Guan L, Dela Cruz C, van Dijk D, Ring A, Putrino D, Iwasaki A. Distinguishing features of long COVID identified through immune profiling. Nature 2023, 623: 139-148. PMID: 37748514, PMCID: PMC10620090, DOI: 10.1038/s41586-023-06651-y.Peer-Reviewed Original ResearchConceptsLong COVIDSARS-CoV-2Infection syndromeExaggerated humoral responseSoluble immune mediatorsEpstein-Barr virusPost-exertional malaiseCross-sectional studyHigher antibody responseImmune mediatorsImmune phenotypingImmune profilingHumoral responseAntibody responseLymphocyte populationsCOVID statusUnbiased machineCortisol levelsLC statusRelevant biomarkersViral pathogensSyndromeCOVIDFuture studiesBiological featuresNonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.
Liu Y, Herrin J, Huang C, Khera R, Dhingra L, Dong W, Mortazavi B, Krumholz H, Lu Y. Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys. Journal Of The American Medical Informatics Association 2023, 30: 943-952. PMID: 36905605, PMCID: PMC10114129, DOI: 10.1093/jamia/ocad035.Peer-Reviewed Original Research
2021
Assessing Performance of Machine Learning—Reply
Khera R, Mortazavi BJ, Krumholz HM. Assessing Performance of Machine Learning—Reply. JAMA Cardiology 2021, 6: 1466-1466. PMID: 34586344, DOI: 10.1001/jamacardio.2021.3715.Peer-Reviewed Original ResearchPerformance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning
Huang C, Li SX, Caraballo C, Masoudi FA, Rumsfeld JS, Spertus JA, Normand ST, Mortazavi BJ, Krumholz HM. Performance Metrics for the Comparative Analysis of Clinical Risk Prediction Models Employing Machine Learning. Circulation Cardiovascular Quality And Outcomes 2021, 14: e007526. PMID: 34601947, DOI: 10.1161/circoutcomes.120.007526.Peer-Reviewed Original ResearchUse of Machine Learning Models to Predict Death After Acute Myocardial Infarction
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, Rumsfeld JS, Masoudi FA, Huang C, Normand SL, Mortazavi BJ, Krumholz HM. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiology 2021, 6: 633-641. PMID: 33688915, PMCID: PMC7948114, DOI: 10.1001/jamacardio.2021.0122.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesFemaleHospital MortalityHumansMachine LearningMaleMyocardial InfarctionRegistriesRisk AssessmentUnited StatesConceptsMachine learning modelsMeta-classifier modelLearning modelNeural networkGradient descent boostingAcute myocardial infarctionContemporary machineGradient descentXGBoost modelXGBoostHospital mortalityCohort studyLogistic regressionMyocardial infarctionNetworkChest Pain-MI RegistryPrecise classificationIndependent validation dataInitial laboratory valuesNovel methodLarge national registryHigh-risk individualsData analysisValidation dataResolution of risk
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 nitrogenMortalityComparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention
Mortazavi BJ, Bucholz EM, Desai NR, Huang C, Curtis JP, Masoudi FA, Shaw RE, Negahban SN, Krumholz HM. Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention. JAMA Network Open 2019, 2: e196835. PMID: 31290991, PMCID: PMC6624806, DOI: 10.1001/jamanetworkopen.2019.6835.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionMajor bleedingC-statisticCoronary interventionMAIN OUTCOMEIndex percutaneous coronary interventionSubsequent coronary artery bypassPercutaneous coronary intervention (PCI) proceduresHospital major bleedingMajor bleeding ratesNationwide clinical registryCoronary artery bypassCoronary intervention proceduresComparative effectiveness studiesRisk score modelComplexity of presentationMean c-statisticCoronary angiography dataRegistry modelNCDR modelsArtery bypassBleeding eventsPrediction of riskClinical variablesBleeding rate
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort
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 ResearchMeSH KeywordsCardiac Surgical ProceduresElectronic Health RecordsHumansMachine LearningModels, StatisticalPostoperative ComplicationsConceptsMajor cardiovascular proceduresElectronic health recordsRespiratory failureAdverse eventsCardiovascular proceduresYale-New Haven HospitalPostoperative respiratory failurePatient cohortHospital costsPatient outcomesSpecific patientPatientsHealth recordsCohort-specific modelsCharacteristic curveInfectionFailureHospitalCohortClinicians
2016
Data Acquisition, Curation, and Use for a Continuously Learning Health System
Krumholz HM, Terry SF, Waldstreicher J. Data Acquisition, Curation, and Use for a Continuously Learning Health System. JAMA 2016, 316: 1669-1670. PMID: 27668668, DOI: 10.1001/jama.2016.12537.Peer-Reviewed Original Research