Featured Publications
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 population
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 measurementsSOFA score performs worse than age for predicting mortality in patients with COVID-19
Sherak R, Sajjadi H, Khimani N, Tolchin B, Jubanyik K, Taylor R, Schulz W, Mortazavi B, Haimovich A. SOFA score performs worse than age for predicting mortality in patients with COVID-19. PLOS ONE 2024, 19: e0301013. PMID: 38758942, PMCID: PMC11101117, DOI: 10.1371/journal.pone.0301013.Peer-Reviewed Original ResearchConceptsCrisis standards of careIn-hospital mortalityIntensive care unitAcademic health systemSequential Organ Failure Assessment scoreCohort of intensive care unitSequential Organ Failure AssessmentStandard of careLogistic regression modelsMortality predictionPredicting in-hospital mortalityHealth systemUnivariate logistic regression modelCrisis standardsDisease morbidityCOVID-19
2023
Computational phenotypes for patients with opioid-related disorders presenting to the emergency department
Taylor R, Gilson A, Schulz W, Lopez K, Young P, Pandya S, Coppi A, Chartash D, Fiellin D, D’Onofrio G. Computational phenotypes for patients with opioid-related disorders presenting to the emergency department. PLOS ONE 2023, 18: e0291572. PMID: 37713393, PMCID: PMC10503758, DOI: 10.1371/journal.pone.0291572.Peer-Reviewed Original ResearchMeSH KeywordsAnalgesics, OpioidEmergency Service, HospitalHumansOpioid-Related DisordersPhenotypeRetrospective StudiesConceptsSubstance use disordersUse disordersED visitsPatient presentationCarlson comorbidity indexOpioid-related diagnosesOpioid-related disordersOne-year survivalRate of medicationOpioid use disorderElectronic health record dataPatient-oriented outcomesYears of ageHealth record dataChronic substance use disordersED returnComorbidity indexAcute overdoseMedical managementClinical entityRetrospective studyEmergency departmentChronic conditionsInclusion criteriaUnique cohort
2022
MIF is a common genetic determinant of COVID-19 symptomatic infection and severity
Shin JJ, Fan W, Par-Young J, Piecychna M, Leng L, Israni-Winger K, Qing H, Gu J, Zhao H, Schulz WL, Unlu S, Kuster J, Young G, Liu J, Ko AI, Garcia A, Sauler M, Wisnewski AV, Young L, Orduña A, Wang A, Klementina O, Garcia AB, Hegyi P, Armstrong ME, Mitchell P, Ordiz DB, Garami A, Kang I, Bucala R. MIF is a common genetic determinant of COVID-19 symptomatic infection and severity. QJM 2022, 116: 205-212. PMID: 36222594, PMCID: PMC9620729, DOI: 10.1093/qjmed/hcac234.Peer-Reviewed Original ResearchConceptsMacrophage migration inhibitory factorLow-expression MIF alleleCOVID-19 infectionMIF allelesCATT7 alleleHealthy controlsCOVID-19Serum macrophage migration inhibitory factorSymptomatic SARS-CoV-2 infectionHigher serum MIF levelsHigh-expression MIF allelesRetrospective case-control studySARS-CoV-2 infectionFunctional polymorphismsAvailable clinical characteristicsMultinational retrospective studySerum MIF levelsUninfected healthy controlsSymptomatic COVID-19Tertiary medical centerHealthy control subjectsCase-control studyMigration inhibitory factorCoronavirus disease 2019Common functional polymorphisms
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, DOI: 10.1001/jamanetworkopen.2020.28361.Peer-Reviewed Original ResearchDevelopment and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation
Haimovich AD, Ravindra NG, Stoytchev S, Young HP, Wilson FP, van Dijk D, Schulz WL, Taylor RA. Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation. Annals Of Emergency Medicine 2020, 76: 442-453. PMID: 33012378, PMCID: PMC7373004, DOI: 10.1016/j.annemergmed.2020.07.022.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedBetacoronavirusClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19COVID-19 TestingEmergency Service, HospitalFemaleHumansMaleMiddle AgedOxygen Inhalation TherapyPandemicsPneumonia, ViralRespiratory InsufficiencyRetrospective StudiesRisk AssessmentSARS-CoV-2Severity of Illness IndexYoung AdultConceptsCOVID-19 Severity IndexQuick COVID-19 severity indexQuick Sequential Organ Failure AssessmentSequential Organ Failure AssessmentOrgan Failure AssessmentHours of admissionRespiratory failureSeverity IndexScoring systemSevere acute respiratory syndrome coronavirus 2Acute respiratory syndrome coronavirus 2Respiratory syndrome coronavirus 2Bedside scoring systemOxygen requirementPneumonia severity scoresHours of hospitalizationElixhauser Comorbidity IndexEmergency department patientsSeverity Index scoreCOVID-19 patientsSyndrome coronavirus 2Coronavirus disease 2019Failure AssessmentSimple scoring systemIndependent test cohortImpact of COVID-19 Pandemic on Laboratory Utilization
Durant TJS, Peaper DR, Ferguson D, Schulz WL. Impact of COVID-19 Pandemic on Laboratory Utilization. The Journal Of Applied Laboratory Medicine 2020, 5: 1194-1205. PMID: 32663258, PMCID: PMC7454564, DOI: 10.1093/jalm/jfaa121.Peer-Reviewed Original ResearchMeSH KeywordsBetacoronavirusClinical Laboratory ServicesClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19COVID-19 TestingFacilities and Services UtilizationHumansIncidencePandemicsPneumonia, ViralPolymerase Chain ReactionReagent Kits, DiagnosticRetrospective StudiesSARS-CoV-2Specimen HandlingLeveraging 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
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
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 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 cohortPrediction of ICU Readmissions Using Data at Patient Discharge
Pakbin A, Rafi P, Hurley N, Schulz W, Krumholz M, Mortazavi J. Prediction of ICU Readmissions Using Data at Patient Discharge. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2018, 00: 4932-4935. PMID: 30441449, DOI: 10.1109/embc.2018.8513181.Peer-Reviewed Original ResearchConceptsICU readmissionHigher health care costsSame hospital admissionElectronic health record dataPoor patient outcomesHealth record dataLong-term riskHealth care costsICU dischargeUnplanned readmissionHospital admissionPatient dischargePatient outcomesICU casesClinical careReadmissionCare costsRecord data