2022
A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations
Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. Npj Digital Medicine 2022, 5: 27. PMID: 35260762, PMCID: PMC8904579, DOI: 10.1038/s41746-022-00570-4.Peer-Reviewed Original ResearchCOVID-19 hospitalizationMayo ClinicDiagnosis codesCOVID-19 diagnosisPositive SARS-CoV-2 PCRYale New Haven Health SystemPositive SARS-CoV-2 testSARS-CoV-2 infectionSARS-CoV-2 PCRSARS-CoV-2 testCOVID-19Higher inhospital mortalitySARS-CoV2 infectionElectronic health record dataICD-10 diagnosisPositive laboratory testsHealth record dataInhospital mortalityAdditional patientsAntigen testSecondary diagnosisPrincipal diagnosisMulticenter evaluationPositive testComputable phenotype definitionsAutomated multilabel diagnosis on electrocardiographic images and signals
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, Schulz WL, Krumholz HM, Ribeiro ALP, Khera R. Automated multilabel diagnosis on electrocardiographic images and signals. Nature Communications 2022, 13: 1583. PMID: 35332137, PMCID: PMC8948243, DOI: 10.1038/s41467-022-29153-3.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArtificial intelligenceApplication of AISignal-based dataSignal-based modelElectrocardiographic imagesECG imagesGrad-CAMImage-based modelsNeural networkDiagnosis modelECG signalsImagesClinical labelsValidation setLabelsExternal validation setMultilabelIntelligenceNetworkApplicationsModelBroad useSetBroader setting
2021
Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection
McPadden J, Warner F, Young HP, Hurley NC, Pulk RA, Singh A, Durant TJS, Gong G, Desai N, Haimovich A, Taylor RA, Gunel M, Dela Cruz CS, Farhadian SF, Siner J, Villanueva M, Churchwell K, Hsiao A, Torre CJ, Velazquez EJ, Herbst RS, Iwasaki A, Ko AI, Mortazavi BJ, Krumholz HM, Schulz WL. Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection. PLOS ONE 2021, 16: e0243291. PMID: 33788846, PMCID: PMC8011821, DOI: 10.1371/journal.pone.0243291.Peer-Reviewed Original ResearchConceptsSARS-CoV-2 infectionYale New Haven HealthSARS-CoV-2Hospital mortalityRisk of admissionMale sexRisk factorsSARS-CoV-2 testingInvasive mechanical ventilationSevere acute respiratory syndrome virusBurden of diseaseRT-PCR testingAcademic health systemDiverse patient populationsRespiratory syndrome virusEthnic groupsAdult patientsClinical characteristicsDischarge dispositionRespiratory supportPrimary outcomeTreatment guidelinesMechanical ventilationRetrospective studyPatient populationTemporal relationship of computed and structured diagnoses in electronic health record data
Schulz WL, Young HP, Coppi A, Mortazavi BJ, Lin Z, Jean RA, Krumholz HM. Temporal relationship of computed and structured diagnoses in electronic health record data. BMC Medical Informatics And Decision Making 2021, 21: 61. PMID: 33596898, PMCID: PMC7890604, DOI: 10.1186/s12911-021-01416-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsStructured diagnosisOutpatient blood pressureElectronic health record dataAcademic health systemLow-density lipoproteinHealth record dataBlood pressureStructured data elementsAdministrative claimsHypertensionClinical informationHyperlipidemiaClinical phenotypeEquivalent diagnosisVital signsHealth systemDiagnosisProblem listAdditional studiesHealth recordsRecord dataTimely accessEHR dataPatientsPrevalence of Dyslipidemia and Availability of Lipid-Lowering Medications Among Primary Health Care Settings in China
Lu Y, Zhang H, Lu J, Ding Q, Li X, Wang X, Sun D, Tan L, Mu L, Liu J, Feng F, Yang H, Zhao H, Schulz WL, Krumholz HM, Pan X, Li J, Huang C, Dong Z, Jiang B, Guo Z, Zhang Y, Sun J, Liu Y, Ren Z, Meng Y, Wang Z, Xi Y, Xing L, Tian Y, Liu J, Fu Y, Liu T, Sun W, Yan S, Jin L, Zheng Y, Wang J, Yan J, Xu X, Chen Y, Xing X, Zhang L, Zhong W, Fang X, Zhu L, Xu Y, Guo X, Xu C, Zhou G, Fan L, Qi M, Zhu S, Qi J, Li J, Yin L, Liu Q, Geng Q, Feng Y, Wang J, Wen H, Han X, Liu P, Ding X, Xu J, Deng Y, He J, Liu G, Jiang C, Zha S, Yang C, Bai G, Yu Y, Tashi Z, Qiu L, Hu Z, He H, Zhang J, Zhou M, Li X, Zhao J, Ma S, Ma Y, Huang Y, Zhang Y, Li F, Shen J. Prevalence of Dyslipidemia and Availability of Lipid-Lowering Medications Among Primary Health Care Settings in China. JAMA Network Open 2021, 4: e2127573. PMID: 34586366, PMCID: PMC8482054, DOI: 10.1001/jamanetworkopen.2021.27573.Peer-Reviewed Original ResearchConceptsAtherosclerotic cardiovascular diseaseLipid-lowering medicationsPrimary care institutionsPrevalence of dyslipidemiaControl of dyslipidemiaLipoprotein cholesterolCare institutionsControl rateFemale sexCardiovascular diseaseMAIN OUTCOMEHigh riskNonstatin lipid-lowering drugsHigh-density lipoprotein cholesterolLow-density lipoprotein cholesterolPrimary health care settingsMajor public health problemLipid lowering medicationsMillion Persons ProjectOverall control rateLDL-C levelsLipid-lowering drugsCross-sectional studyPublic health problemHealth care settingsToward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft
Mori M, Durant TJS, Huang C, Mortazavi BJ, Coppi A, Jean RA, Geirsson A, Schulz WL, Krumholz HM. Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft. Circulation Cardiovascular Quality And Outcomes 2021, 14: e007363. PMID: 34078100, PMCID: PMC8635167, DOI: 10.1161/circoutcomes.120.007363.Peer-Reviewed Original ResearchConceptsCoronary artery bypass graftArtery bypass graftIntraoperative variablesBypass graftLogistic regression modelsOperative mortalityC-statisticCoronary artery bypass graft casesThoracic Surgeons Adult Cardiac Surgery DatabaseAdult Cardiac Surgery DatabaseMean patient ageGood c-statisticCardiac Surgery DatabaseBrier scoreRisk restratificationDynamic risk predictionIntraoperative deathsPostoperative complicationsPostoperative eventsAdverse eventsPatient agePreoperative variablesRegression modelsGraft casesSurgery Database
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
2020
Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment
Durant TJS, Jean RA, Huang C, Coppi A, Schulz WL, Geirsson A, Krumholz HM. Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment. JAMA Network Open 2020, 3: e2028361. PMID: 33284333, PMCID: PMC11841993, DOI: 10.1001/jamanetworkopen.2020.28361.Peer-Reviewed Original ResearchAgile analytics to support rapid knowledge pipelines
Schulz WL, Kvedar JC, Krumholz HM. Agile analytics to support rapid knowledge pipelines. Npj Digital Medicine 2020, 3: 108. PMID: 32864471, PMCID: PMC7438323, DOI: 10.1038/s41746-020-00309-z.Peer-Reviewed Original ResearchThe Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers.
Mori M, Khera R, Lin Z, Ross JS, Schulz W, Krumholz HM. The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers. Methodist DeBakey Cardiovascular Journal 2020, 16: 212-219. PMID: 33133357, PMCID: PMC7587314, DOI: 10.14797/mdcj-16-3-212.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsLearning health systemLearning systemCommon data modelDynamic learning systemAdvanced analyticsBig dataData assetsData modelDigital solutionsCustomer interactionContinuous learningKnowledge generationEffective useConceptual modelAnalyticsSystemGoogleHealth systemLearningComparable scaleModelDataCompaniesRates 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
2019
Validation and Regulation of Clinical Artificial Intelligence
Schulz WL, Durant T, Krumholz HM. Validation and Regulation of Clinical Artificial Intelligence. Clinical Chemistry 2019, 65: 1336-1337. PMID: 32100825, DOI: 10.1373/clinchem.2019.308304.Commentaries, Editorials and LettersTapping Into Underutilized Healthcare Data in Clinical Research
Mori M, Schulz WL, Geirsson A, Krumholz HM. Tapping Into Underutilized Healthcare Data in Clinical Research. Annals Of Surgery 2019, Publish Ahead of Print: &na;. PMID: 30998537, DOI: 10.1097/sla.0000000000003329.Peer-Reviewed Original ResearchTraditional Chinese Medicine Use in the Treatment of Acute Heart Failure in Western Medicine Hospitals in China: Analysis From the China PEACE Retrospective Heart Failure Study
Yu Y, Spatz ES, Tan Q, Liu S, Lu Y, Masoudi FA, Schulz WL, Krumholz HM, Li J, Group T. Traditional Chinese Medicine Use in the Treatment of Acute Heart Failure in Western Medicine Hospitals in China: Analysis From the China PEACE Retrospective Heart Failure Study. Journal Of The American Heart Association 2019, 8: e012776. PMID: 31364457, PMCID: PMC6761625, DOI: 10.1161/jaha.119.012776.Peer-Reviewed Original ResearchConceptsTraditional Chinese medicineAcute heart failureHeart failureEvidence-based therapiesWestern Medicine HospitalTCM useMedicine HospitalEvidence-based therapy useTraditional Chinese medicine useChinese medicine useCoronary artery diseaseHeart Failure StudyHierarchical logistic regression modelsLogistic regression modelsSalvia miltiorrhizaRandom sampleHospital bleedingPatient's bleedingPatient characteristicsArtery diseaseTherapy useMedicine useHospital characteristicsRetrospective analysisHospital useHealth Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform
McPadden J, Durant TJ, Bunch DR, Coppi A, Price N, Rodgerson K, Torre CJ, Byron W, Hsiao AL, Krumholz HM, Schulz WL. Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform. Journal Of Medical Internet Research 2019, 21: e13043. PMID: 30964441, PMCID: PMC6477571, DOI: 10.2196/13043.Peer-Reviewed Original ResearchConceptsData science platformOpen source technologiesHealth care dataSource technologiesModern big data platformScience platformData management approachTraditional computer systemsBig data workloadsBig data platformBig data sourcesReal-time datasetBiomedical research dataHealth care applicationsData-driven researchPatient monitoringScalable analyticsApache StormData lakeData workloadsAnalytics platformData acquisition workflowContinuous patient monitoringUse casesData platformLEARNING HOW TO SUCCESSFULLY ENROLL AND ENGAGE PEOPLE IN A MOBILE SYNC-FOR-SCIENCE PLATFORM TO INFORM SHARED DECISION-MAKING
Dhruva S, Mena-Hurtado C, Curtis J, Krumholz L, Hutten D, Schulz W, Rumsfeld J, Masoudi F, Hewitt K, Bae J, Hsiao A, Krumholz H. LEARNING HOW TO SUCCESSFULLY ENROLL AND ENGAGE PEOPLE IN A MOBILE SYNC-FOR-SCIENCE PLATFORM TO INFORM SHARED DECISION-MAKING. Journal Of The American College Of Cardiology 2019, 73: 3039. DOI: 10.1016/s0735-1097(19)33645-9.Peer-Reviewed Original ResearchCharacteristics of High Cardiovascular Risk in 1.7 Million Chinese Adults.
Lu J, Lu Y, Yang H, Bilige W, Li Y, Schulz W, Masoudi FA, Krumholz HM. Characteristics of High Cardiovascular Risk in 1.7 Million Chinese Adults. Annals Of Internal Medicine 2019, 170: 298-308. PMID: 30776800, DOI: 10.7326/m18-1932.Peer-Reviewed Original ResearchConceptsHigh CVD riskCVD riskHigh riskHigh cardiovascular disease riskCardiovascular risk increasesHigh cardiovascular riskOverall study populationCardiovascular disease riskBody mass indexMultivariable mixed modelsNational Health CommissionAntihypertensive medicationsAspirin useCardiovascular riskCVD screeningMass indexStudy populationChinese adultsHan ethnicityDisease riskStatinsMixed modelsHealth CommissionSocioeconomic statusPopulation subgroups
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