2024
Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurementsHypertension 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
Delayed production of neutralizing antibodies correlates with fatal COVID-19
Lucas C, Klein J, Sundaram ME, Liu F, Wong P, Silva J, Mao T, Oh JE, Mohanty S, Huang J, Tokuyama M, Lu P, Venkataraman A, Park A, Israelow B, Vogels CBF, Muenker MC, Chang CH, Casanovas-Massana A, Moore AJ, Zell J, Fournier JB, Wyllie A, Campbell M, Lee A, Chun H, Grubaugh N, Schulz W, Farhadian S, Dela Cruz C, Ring A, Shaw A, Wisnewski A, Yildirim I, Ko A, Omer S, Iwasaki A. Delayed production of neutralizing antibodies correlates with fatal COVID-19. Nature Medicine 2021, 27: 1178-1186. PMID: 33953384, PMCID: PMC8785364, DOI: 10.1038/s41591-021-01355-0.Peer-Reviewed Original ResearchConceptsDeceased patientsAntibody levelsAntibody responseDisease severityAnti-S IgG levelsCOVID-19 disease outcomesFatal COVID-19Impaired viral controlWorse clinical progressionWorse disease severitySevere COVID-19Length of hospitalizationImmunoglobulin G levelsHumoral immune responseCoronavirus disease 2019COVID-19 mortalityCOVID-19Domain (RBD) IgGSeroconversion kineticsDisease courseIgG levelsClinical parametersClinical progressionHumoral responseDisease onset
2020
Rates and Predictors of Patient Underreporting of Hospitalizations During Follow-Up After Acute Myocardial Infarction
Caraballo C, Khera R, Jones PG, Decker C, Schulz W, Spertus JA, Krumholz HM. Rates and Predictors of Patient Underreporting of Hospitalizations During Follow-Up After Acute Myocardial Infarction. Circulation Cardiovascular Quality And Outcomes 2020, 13: e006231. PMID: 32552061, PMCID: PMC9465954, DOI: 10.1161/circoutcomes.119.006231.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial infarctionHospitalization eventsMedical recordsLongitudinal multicenter cohort studyMulticenter cohort studyMedical record abstractionDifferent patient characteristicsHealth care eventsPatients' underreportingTRIUMPH registryAccuracy of reportingCohort studyPatient characteristicsRecord abstractionProspective studyClinical studiesClinical investigationHospitalizationPatientsCare eventsInfarctionEvent ratesParticipantsPredictorsAgile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform
Schulz WL, Durant T, Torre CJ, Hsiao AL, Krumholz HM. Agile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform. Journal Of Medical Internet Research 2020, 22: e18707. PMID: 32442130, PMCID: PMC7257473, DOI: 10.2196/18707.Peer-Reviewed Original ResearchConceptsReal-time dataHealth information technologyReal-world dataHealth platformInformation technologyCombination of technologiesReal timeSevere acute respiratory syndrome coronavirus 2Acute respiratory syndrome coronavirus 2Timely informationRespiratory syndrome coronavirus 2PlatformRespiratory tract infectionsSyndrome coronavirus 2Health care systemTract infectionsCoronavirus disease (COVID-19) outbreakIncident casesCoronavirus 2Novel applicationTechnologyAnalyticsHealth systemCare systemSpecific pathogensLeveraging 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
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