2022
The Beirut ammonium nitrate blast: A multicenter study to assess injury characteristics and outcomes
Al-Hajj S, Farran SH, Zgheib H, Tfaily MA, Halaoui A, Wehbe S, Karam S, Fadlallah Y, Fahd F, Toufaili L, Arjinian S, Al-Zaghrini E, Al Hariri M, El Hussein M, Souaiby N, Mowafi H, Mufarrij AJ. The Beirut ammonium nitrate blast: A multicenter study to assess injury characteristics and outcomes. Journal Of Trauma And Acute Care Surgery 2022, 94: 328-335. PMID: 35999664, DOI: 10.1097/ta.0000000000003745.Peer-Reviewed Original ResearchConceptsInjury characteristicsInjury predictorsBlast injuryMulticenter cross-sectional studyMajor acute care hospitalsHospital chart reviewInjury Severity ScoreIntensive care unitAcute care hospitalsCross-sectional studyHead/faceSecondary blast injuriesPrimary blast injuryChart reviewNeurologic disabilityEye injuriesTrauma patientsCare unitMultiple injuriesBlast lungMulticenter studyMean ageFrequent siteMost injuriesSeverity scoreNeurotrauma in the Syrian War: analysis of 41,143 cases from July 2013–July 2015
Fatima N, Mowafi H, Hariri M, Alnahhas H, Al-Kassem A, Saqqur M. Neurotrauma in the Syrian War: analysis of 41,143 cases from July 2013–July 2015. Neurological Sciences 2022, 43: 3769-3774. PMID: 35018549, DOI: 10.1007/s10072-022-05878-3.Peer-Reviewed Original ResearchMeSH KeywordsAgedChildCraniocerebral TraumaFemaleHumansLength of StayMaleRetrospective StudiesSyriaWounds, GunshotConceptsNeurotrauma patientsTimes higher mortalityHospital stayBlunt injuryMedian lengthTrauma presentationsPatient presentationMethodsSecondary analysisPatientsGeneral traumaHigh mortalityNeurotraumaHospitalNeurosurgical proceduresLogistic regressionInjuryAdministrative datasetsFurther studiesMortalityUnknown dispositionTraumaPresentationLong-term needsMalesFemales
2020
Defining High‐risk Emergency Chief Complaints: Data‐driven Triage for Low‐ and Middle‐income Countries
Rice B, Leanza J, Mowafi H, Kamara N, Mulogo EM, Bisanzo M, Nikam K, Kizza H, Newberry JA, Strehlow M, Group G, Kohn M. Defining High‐risk Emergency Chief Complaints: Data‐driven Triage for Low‐ and Middle‐income Countries. Academic Emergency Medicine 2020, 27: 1291-1301. PMID: 32416022, PMCID: PMC7818254, DOI: 10.1111/acem.14013.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedDeveloping CountriesEmergency Medical ServicesEmergency Service, HospitalFemaleHumansMaleRandom AllocationRetrospective StudiesTriageVital SignsConceptsHigh-risk chief complaintsChief complaintMiddle-income countriesPatient outcomesLMIC settingsVital signsMortality odds ratioLevel of consciousnessLogistic regression modelsLocal disease patternsHIV statusMortality oddsOdds ratioDerivation data setsEmergency unitMortality riskEmergency careEmergency training programsDerivation dataDisease patternsTriage systemRisk categoriesComplaintsPatient dataTriage data
2018
Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda
Rice BT, Bisanzo M, Maling S, Joseph R, Mowafi H, Chamberlain S, Dreifuss B, Hammerstedt H, Langevin M, Nelson S, Periyanayagam U. Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda. BMJ Open 2018, 8: e020188. PMID: 29950461, PMCID: PMC6020949, DOI: 10.1136/bmjopen-2017-020188.Peer-Reviewed Original ResearchConceptsChief complaintPatient visitsInter-rater reliabilityEmergency careQuality assurance databaseEmergency care practitionersConsensus processSubstantial inter-rater reliabilityRetrospective reviewDerivation datasetCare practitionersComplaintsValidation datasetSouthwestern UgandaSecond validation datasetVisitsCohen's kappaCareKappaSeparate validation datasetData entryProfit hospitalsStataHospitalUganda
2016
Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Hall MK. Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic Emergency Medicine 2016, 23: 269-278. PMID: 26679719, PMCID: PMC5884101, DOI: 10.1111/acem.12876.Peer-Reviewed Original ResearchConceptsMachine learning approachesElectronic health recordsLearning approachPredictive analyticsMachine learning techniquesRandom forest modelClinical decision support systemBig Data DrivenDecision support systemForest modelLearning techniquesUse casesData-DrivenFacilitate automationTraditional analytic techniquesAnalyticsSupport systemSimple heuristicsNew analyticsHealth recordsSmall setTree modelQuestion of generalizabilityPrediction modelDecision rules