2024
Hypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records
Brush J, Lu Y, Liu Y, Asher J, Li S, Sawano M, Young P, Schulz W, Anderson M, Burrows J, Krumholz H. Hypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records. Journal Of The American Heart Association 2024, 13: e033253. PMID: 38686864, PMCID: PMC11179912, DOI: 10.1161/jaha.123.033253.Peer-Reviewed Original ResearchConceptsElectronic health recordsRegional health systemImprove hypertension careHealth systemHealth recordsHypertension careDiastolic blood pressureAge-adjusted prevalence ratesNon-Hispanic Black patientsPrevalence ratesLarger health systemCross-sectional analysisTransformation of medical dataLeveraging real-world dataHigh prevalence rateHypertension trendsHypertension prevalenceBlood pressureBlood pressure measurementsHypertension diagnosisPrimary outcomeNational trendsProportion of patientsAntihypertensive medicationsBlack patients
2021
Delays in antibiotic redosing: Association with inpatient mortality and risk factors for delay
Kemmler CB, Sangal RB, Rothenberg C, Li SX, Shofer FS, Abella BS, Venkatesh AK, Foster SD. Delays in antibiotic redosing: Association with inpatient mortality and risk factors for delay. The American Journal Of Emergency Medicine 2021, 46: 63-69. PMID: 33735698, DOI: 10.1016/j.ajem.2021.02.058.Peer-Reviewed Original ResearchConceptsSecond dose administrationEmergency departmentDose administrationRisk factorsEmergency Severity IndexHospital mortalityFirst doseSecond doseED boardingAntibiotic dosesEnd-stage renal diseaseExtremes of weightHigh acuity presentationsRetrospective cohort studyStage renal diseaseWorse clinical outcomesSerious bacterial infectionsOdds of delayEarly hospital courseSingle healthcare systemAntibiotic redosingDosing intervalHospital courseCohort studyInpatient mortality
2020
Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data
Li SX, Wang Y, Lama SD, Schwartz J, Herrin J, Mei H, Lin Z, Bernheim SM, Spivack S, Krumholz HM, Suter LG. Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data. BMC Health Services Research 2020, 20: 733. PMID: 32778098, PMCID: PMC7416804, DOI: 10.1186/s12913-020-05611-w.Peer-Reviewed Original Research
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
2016
Analysis of Machine Learning Techniques for Heart Failure Readmissions
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circulation Cardiovascular Quality And Outcomes 2016, 9: 629-640. PMID: 28263938, PMCID: PMC5459389, DOI: 10.1161/circoutcomes.116.003039.Peer-Reviewed Original ResearchThe china patient‐centered evaluative assessment of cardiac events (PEACE) prospective study of percutaneous coronary intervention: Study design
Du X, Pi Y, Dreyer RP, Li J, Li X, Downing NS, Li L, Feng F, Zhan L, Zhang H, Guan W, Xu X, Li S, Lin Z, Masoudi FA, Spertus JA, Krumholz HM, Jiang L, Group F. The china patient‐centered evaluative assessment of cardiac events (PEACE) prospective study of percutaneous coronary intervention: Study design. Catheterization And Cardiovascular Interventions 2016, 88: e212-e221. PMID: 26945565, PMCID: PMC5215582, DOI: 10.1002/ccd.26461.Peer-Reviewed Original ResearchMeSH KeywordsChinaClinical ProtocolsCoronary AngiographyHealth StatusHealthcare DisparitiesHumansMedication AdherenceMyocardial InfarctionPatient Reported Outcome MeasuresPatient-Centered CarePercutaneous Coronary InterventionPredictive Value of TestsProspective StudiesResearch DesignRisk AssessmentRisk FactorsSecondary PreventionTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionPatient-reported outcomesCardiovascular risk factor controlRisk factor controlProspective studyHealth statusMedical historyLong-term clinical outcomesLong-term patient outcomesHospital-level factorsIndependent core laboratoryNationwide prospective studyLong-term outcomesPatient's medical historyHospital outcomesCoronary interventionPatient demographicsSecondary preventionConsecutive patientsMedical chartsPCI indicationPrimary outcomeClinical outcomesClinical presentationHealthcare utilization
2012
Procedure Intensity and the Cost of Care
Chen SI, Dharmarajan K, Kim N, Strait KM, Li SX, Safavi KC, Lindenauer PK, Krumholz HM, Lagu T. Procedure Intensity and the Cost of Care. Circulation Cardiovascular Quality And Outcomes 2012, 5: 308-313. PMID: 22576844, PMCID: PMC3415230, DOI: 10.1161/circoutcomes.112.966069.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overCosts and Cost AnalysisCross-Sectional StudiesFemaleHeart FailureHospital Bed CapacityHospital CostsHospital MortalityHospitalizationHospitals, RuralHospitals, TeachingHospitals, UrbanHumansLength of StayLinear ModelsMaleMiddle AgedModels, EconomicOutcome and Process Assessment, Health CareResidence CharacteristicsRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUnited StatesYoung AdultConceptsHF hospitalizationHeart failureInvasive proceduresHospital groupRisk-standardized mortality ratesProportion of patientsLength of stayCost of careWilcoxon rank sum testHigher procedure ratesRank sum testPatient demographicsPerspective databaseMedian lengthSurgical proceduresProcedure ratesHospitalizationOutcome differencesMortality rateHospitalPatientsPractice styleProcedure useSum testOverall use
2011
Long-term Trends in Short-term Outcomes in Acute Myocardial Infarction
Nguyen HL, Saczynski JS, Gore JM, Waring ME, Lessard D, Yarzebski J, Reed G, Spencer FA, Li SX, Goldberg RJ. Long-term Trends in Short-term Outcomes in Acute Myocardial Infarction. The American Journal Of Medicine 2011, 124: 939-946. PMID: 21962314, PMCID: PMC3185241, DOI: 10.1016/j.amjmed.2011.05.023.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionShort-term outcomesOlder patientsAtrial fibrillationMyocardial infarctionAdverse short-term outcomesGreater Worcester medical centersShort-term death ratesShort-term mortality rateMen 75 yearsShort-term mortalityTargeted treatment approachCardiogenic shockHeart failureMajor complicationsAge differencesElderly menMedical CenterStudy populationMortality rateTreatment approachesPatientsDeath rateFemale residentsWomen