2020
Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure
Lu Y, Huang C, Mahajan S, Schulz WL, Nasir K, Spatz ES, Krumholz HM. Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure. Journal Of The American Heart Association 2020, 9: e015033. PMID: 32200730, PMCID: PMC7428633, DOI: 10.1161/jaha.119.015033.Peer-Reviewed Original ResearchConceptsDiastolic blood pressureSystolic blood pressureElevated blood pressureBlood pressureElectronic health recordsPopulation health surveillanceHealth recordsYale New Haven Health SystemHealth surveillanceHealth systemPatterns of patientsLarge health systemUsual careOutpatient encountersControl ratePatientsCare patternsPopulation healthMonthsHgSurveillancePrevalenceRecordsVisitsCare
2019
Prevalence, Awareness, and Treatment of Isolated Diastolic Hypertension: Insights From the China PEACE Million Persons Project
Mahajan S, Zhang D, He S, Lu Y, Gupta A, Spatz ES, Lu J, Huang C, Herrin J, Liu S, Yang J, Wu C, Cui J, Zhang Q, Li X, Nasir K, Zheng X, Krumholz HM, Li J, Dong Z, Jiang B, Zhang Y, Liu Y, Meng Y, Xi Y, Tian Y, Fu Y, Liu T, Yan S, Jin L, Wang J, Xu X, Xing X, Zhang L, Fang X, Xu Y, Xu C, Fan L, Qi M, Qi J, Li J, Liu Q, Feng Y, Wang J, Wen H, Xu J, He J, Jiang C, Yang C, Yu Y, Tashi Z, Hu Z, Zhang J, Li X, Ma S, Ma Y, Huang Y, Zhang Y, Shen J. Prevalence, Awareness, and Treatment of Isolated Diastolic Hypertension: Insights From the China PEACE Million Persons Project. Journal Of The American Heart Association 2019, 8: e012954. PMID: 31566101, PMCID: PMC6806046, DOI: 10.1161/jaha.119.012954.Peer-Reviewed Original ResearchConceptsMillion Persons ProjectPrior cardiovascular eventsBody mass indexAntihypertensive medicationsDiastolic hypertensionCardiovascular eventsDiabetes mellitusMass indexIsolated diastolic hypertensionDiastolic blood pressureSelf-reported diagnosisTreatment of peoplePersons ProjectBlood pressureTreatment patternsHypertensionLeast collegeHigher likelihoodMellitusMedicationsPrevalenceTreatmentDiagnosisSubstantial numberCurrent use
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