2016
Epidemiologic Trends of Chemical Ocular Burns in the United States
Haring R, Sheffield I, Channa R, Canner J, Schneider E. Epidemiologic Trends of Chemical Ocular Burns in the United States. JAMA Ophthalmology 2016, 134: 1119-1124. PMID: 27490908, DOI: 10.1001/jamaophthalmol.2016.2645.Peer-Reviewed Original ResearchConceptsChemical ocular burnsEmergency departmentOcular burnsEpidemiologic trendsAcid injuryHealth care insuranceChemical burnsNationwide Emergency Department SampleChemical eye injuriesEmergency department chargesTotal emergency departmentOcular chemical burnsOcular chemical injuryEmergency Department SampleHigh-risk groupSingle high-risk groupAge-specific ratesPrivate health care insuranceCare insuranceAlkali injuryED presentationsEye injuriesMedian agePatient ageFemale patients30‐Day In‐hospital Trauma Mortality in Four Urban University Hospitals Using an Indian Trauma Registry
Roy N, Gerdin M, Ghosh S, Gupta A, Kumar V, Khajanchi M, Schneider E, Gruen R, Tomson G, von Schreeb J. 30‐Day In‐hospital Trauma Mortality in Four Urban University Hospitals Using an Indian Trauma Registry. World Journal Of Surgery 2016, 40: 1299-1307. PMID: 26911610, DOI: 10.1007/s00268-016-3452-y.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overBlood PressureChildChild, PreschoolDeveloping CountriesFemaleGlasgow Coma ScaleHospital MortalityHospitalizationHospitals, PublicHospitals, UniversityHospitals, UrbanHumansIndiaInfantMaleMiddle AgedProspective StudiesRegistriesTime-to-TreatmentWounds and InjuriesYoung AdultConceptsHospital trauma mortalityTrauma mortalityUniversity HospitalTrauma systemMortality rateAdmission systolic blood pressureHospital mortality rateDays of hospitalizationGlasgow Coma ScoreSystolic blood pressureUrban university hospitalTrauma mortality ratesPublic university hospitalPhysiological scoringCare delaysLate mortalityComa ScoreBlood pressureMedian ageTrauma patientsTrauma registryAdmission vitalsTrauma careTraumatic injuryHigh-income countries“Halo effect” in trauma centers: does it extend to emergent colectomy?
Nagarajan N, Selvarajah S, Gani F, Alshaikh HN, Giuliano K, Zogg CK, Schneider EB, Haider AH. “Halo effect” in trauma centers: does it extend to emergent colectomy? Journal Of Surgical Research 2016, 203: 231-237. PMID: 27125867, DOI: 10.1016/j.jss.2016.01.037.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overColectomyDatabases, FactualDiverticulitis, ColonicEmergenciesFemaleHospital ChargesHospital MortalityHumansLength of StayLinear ModelsLogistic ModelsMaleMiddle AgedPoisson DistributionQuality Assurance, Health CareQuality Indicators, Health CareTrauma CentersTreatment OutcomeUnited StatesYoung AdultConceptsLength of stayNontrauma centersHospital-level characteristicsTrauma centerEmergent colectomyEmergency general surgery conditionsEmergency general surgery proceduresNationwide Emergency Department SampleEmergency surgical interventionOdds of mortalityEmergency Department SampleGeneral surgery proceduresNontrauma conditionsHospital mortalityMedian ageSurgical interventionSurgical conditionsImproved outcomesSex distributionSurgical careMedian chargePatientsSurgery proceduresSurgery conditionsCase mix
2014
Variation in Readmission by Hospital After Colorectal Cancer Surgery
Lucas D, Ejaz A, Bischof D, Schneider E, Pawlik T. Variation in Readmission by Hospital After Colorectal Cancer Surgery. JAMA Surgery 2014, 149: 1272-1277. PMID: 25337956, DOI: 10.1001/jamasurg.2014.988.Peer-Reviewed Original ResearchConceptsReadmission ratesColorectal surgeryAppropriate risk adjustmentHierarchical multivariable logistic regression analysisMultivariable logistic regression analysisRisk-adjusted readmission ratesRisk adjustmentRepresentative cancer registryColorectal cancer surgeryEnd Results-MedicareHospital readmission ratesHospital quality metricsRisk-adjusted variationLogistic regression analysisColorectal resectionStudy patientsHospital readmissionMedian agePatient characteristicsCancer surgeryCancer RegistryMAIN OUTCOMEReadmissionUS hospitalsHospitalThe Epidemiology of Childhood and Adolescent Traumatic Spinal Cord Injury in the United States: 2007–2010
Selvarajah S, Schneider E, Becker D, Sadowsky C, Haider A, Hammond E. The Epidemiology of Childhood and Adolescent Traumatic Spinal Cord Injury in the United States: 2007–2010. Journal Of Neurotrauma 2014, 31: 1548-1560. PMID: 24811704, DOI: 10.1089/neu.2014.3332.Peer-Reviewed Original ResearchConceptsTraumatic spinal cord injuryAcute traumatic spinal cord injuryNew Injury Severity ScoreSpinal cord injuryCumulative incidenceCord injuryMedian New Injury Severity ScoreNationwide Emergency Department SampleChildren age 5 yearsConcurrent brain injuryNinth Revision diagnosisInjury Severity ScoreMajority of patientsOverall injury severityEmergency Department SampleInflation-adjusted chargesEpidemiology of childhoodChildren 5 yearsEmergency department dataAge 17 yearsAge 5 yearsRoad traffic accidentsDischarge dispositionMedian ageRevision diagnosis
2013
Influence of Patient, Physician, and Hospital Factors on 30-Day Readmission Following Pancreatoduodenectomy in the United States
Hyder O, Dodson R, Nathan H, Schneider E, Weiss M, Cameron J, Choti M, Makary M, Hirose K, Wolfgang C, Herman J, Pawlik T. Influence of Patient, Physician, and Hospital Factors on 30-Day Readmission Following Pancreatoduodenectomy in the United States. JAMA Surgery 2013, 148: 1095-1102. PMID: 24108580, PMCID: PMC3983984, DOI: 10.1001/jamasurg.2013.2509.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAttitude of Health PersonnelCohort StudiesComorbidityCross InfectionFemaleHealth Care SurveysHospital MortalityHumansLength of StayMaleMedicareOutcome Assessment, Health CarePancreatic NeoplasmsPancreaticoduodenectomyPatient ReadmissionPostoperative ComplicationsPractice Patterns, Physicians'Retrospective StudiesRisk FactorsSEER ProgramSurvival RateTime FactorsUnited StatesWorkloadConceptsPancreatoduodenectomy proceduresMedical comorbiditiesHospital factorsSurgeon volumeMedicare dataPopulation-based cancer registry dataPreoperative medical comorbiditiesRetrospective cohort studyDays of dischargeHospital-level factorsLow-volume hospitalsPatient-related factorsChance of readmissionLength of stayInfluence of patientCancer registry dataPancreatoduodenectomy patientsHospital morbidityCohort studyHospital volumeHospital readmissionMedian agePhysician factorsDistinct hospitalsPatient levelPost-treatment surveillance of patients with colorectal cancer with surgically treated liver metastases
Hyder O, Dodson R, Mayo S, Schneider E, Weiss M, Herman J, Wolfgang C, Pawlik T. Post-treatment surveillance of patients with colorectal cancer with surgically treated liver metastases. Surgery 2013, 154: 256-265. PMID: 23889953, PMCID: PMC4048030, DOI: 10.1016/j.surg.2013.04.021.Peer-Reviewed Original ResearchConceptsColorectal liver metastasesMagnetic resonance imagingPositron emission tomographyPost-treatment surveillanceComputed tomographyLiver metastasesLiver resectionSurveillance imagingTreatment of CRLMMore frequent surveillanceMedian survival durationAbdominal computed tomographyPopulation-based patternsLong-term survivalIntensity of surveillanceOverall survivalMedian ageSurveillance guidelinesColorectal cancerMedicare databaseSecondary interventionsSurvival durationProcedure typeFrequent surveillanceHealthcare resourcesProvider versus patient factors impacting hospital length of stay after pancreaticoduodenectomy
Schneider E, Hyder O, Wolfgang C, Dodson R, Haider A, Herman J, Pawlik T. Provider versus patient factors impacting hospital length of stay after pancreaticoduodenectomy. Surgery 2013, 154: 152-161. PMID: 23889945, DOI: 10.1016/j.surg.2013.03.013.Peer-Reviewed Original ResearchConceptsDuration of stayHigh-volume hospitalsHigh-volume surgeonsHospital volumeMedian durationPD volumeMedian annual surgeon volumeAnnual hospital volumeAnnual surgeon volumePatient-level factorsNationwide Inpatient SampleCost-saving implicationsLow surgeonComorbid illnessesHospital lengthPerioperative outcomesVolume hospitalsHospital durationOlder patientsPatient ageMedian ageNonclinical factorsPatient factorsSurgeon volumePD patientsPredictors of outcome in acute encephalitis
Thakur K, Motta M, Asemota A, Kirsch H, Benavides D, Schneider E, McArthur J, Geocadin R, Venkatesan A. Predictors of outcome in acute encephalitis. Neurology 2013, 81: 793-800. PMID: 23892708, PMCID: PMC3908458, DOI: 10.1212/wnl.0b013e3182a2cc6d.Peer-Reviewed Original ResearchConceptsMultivariate logistic regression analysisPredictors of outcomeStatus epilepticusLogistic regression analysisCerebral edemaHospital dischargePoor outcomeJohns Hopkins Bayview Medical CenterPredictors of deathFurther prospective studiesIntensive care unitJohns Hopkins HospitalRegression analysisIntubation requirementAutoimmune etiologyAcute encephalitisAggressive managementVentilator supportMedian ageCare unitViral encephalitisProspective studyEncephalitis casesUnknown causeRetrospective analysis