2023
Assessment of Atherothrombotic Risk in Patients With Type 2 Diabetes Mellitus
Berg D, Moura F, Bellavia A, Scirica B, Wiviott S, Bhatt D, Raz I, Bohula E, Giugliano R, Park J, Feinberg M, Braunwald E, Morrow D, Sabatine M. Assessment of Atherothrombotic Risk in Patients With Type 2 Diabetes Mellitus. Journal Of The American College Of Cardiology 2023, 81: 2391-2402. PMID: 37344040, PMCID: PMC11466046, DOI: 10.1016/j.jacc.2023.04.031.Peer-Reviewed Original ResearchConceptsType 2 diabetes mellitusSecondary preventionIschemic strokePredictor of MIValidation cohortClinical decision-makingRates of MIMyocardial infarctionAtherothrombotic eventsRisk quintileRisk of atherothrombotic eventsRisk modelSelection of pharmacotherapyRisk scoreAssessed clinical variablesMultivariate Cox regressionClinical trial cohortPooled cohortCandidate variablesCox regressionIS ratesDECLARE-TIMIAbsolute reductionTrial cohortCohort
2022
How Early Adolescent Skills and Preferences Shape Economics Education Choices
Fiala L, Humphries J, Joensen J, Karna U, List J, Veramendi G. How Early Adolescent Skills and Preferences Shape Economics Education Choices. AEA Papers And Proceedings 2022, 112: 609-613. DOI: 10.1257/pandp.20221037.Peer-Reviewed Original Research
2020
Diagnosis of Transient Ischemic Attack
Gocan S, Fitzpatrick T, Wang CQ, Taljaard M, Cheng W, Bourgoin A, Dowlatshahi D, Stotts G, Shamy M. Diagnosis of Transient Ischemic Attack. Stroke 2020, 51: 3371-3374. PMID: 32993462, DOI: 10.1161/strokeaha.120.031510.Peer-Reviewed Original ResearchConceptsTransient ischemic attackIschemic attackSex-specific differencesStroke diagnosisTIA/strokeUnilateral sensory lossRetrospective cohort studyStroke prevention clinicComplete patient recordsSex-based differencesStroke manifestationPrevention clinicCohort studySymptom onsetStroke symptomsPatient sexSensory lossFinal diagnosisMultinomial logistic regressionPatient recordsLogistic regressionDiagnosisSymptomsCandidate variablesSex
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 OUTCOME
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
2015
Baseline Cluster Membership Demonstrates Positive Associations with First Occurrence of Multiple Gerontologic Outcomes Over 10 Years
Fodeh SJ, Trentalange M, Allore HG, Gill TM, Brandt CA, Murphy TE. Baseline Cluster Membership Demonstrates Positive Associations with First Occurrence of Multiple Gerontologic Outcomes Over 10 Years. Experimental Aging Research 2015, 41: 177-192. PMID: 25724015, PMCID: PMC4347941, DOI: 10.1080/0361073x.2015.1001655.Peer-Reviewed Original ResearchConceptsBaseline valuesCommunity-living personsProportional hazards regressionPositive associationLevel of impairmentHazards regressionChronic conditionsBaseline predictorsFollowing outcomesSlow gaitDaily livingDepressive symptomsCognitive statusOutcomesFirst occurrenceDichotomous indicatorsMobility measuresAssociationDeathCandidate variablesDisabilityBaseline cluster
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