PW 2468 Development of a predictive modeling approach to analysis of patient outcomes related to traumatic brain injury at a emergency center in a low income country. (November 2018)

• Record Type: Journal Article
• Authors: Vissoci, João Ricardo Nickenig; Rocha, Thiago Augusto Hernandes; Elahi, Cyrus; Haglund, Michael M; Staton, Catherine
• Is Part Of: Injury prevention. Volume 24(2018)Supplement 2
• Page Start: A191
• Page End: A192
• Publication Date: 2018-11
• Subjects: Children's accidents -- Prevention -- Periodicals; Accidents -- Prevention -- Periodicals; 617.1
• DOI: 10.1136/injuryprevention-2018-safety.529
• Languages: English
• ISSNs: 1353-8047

Abstract

Abstract : Prognostic models to support clinical decisions is an innovative solution to mitigate the increasing burden of traumatic brain injury (TBI) in low and medium income countries (LMIC). Hospitals, equipped with predictive models, can make data driven decisions based on patient prognosis to improve efficiency, productivity and outcomes. Furthermore, attempts to implement TBI predictive models are limited in both high and LMIC. The main objective of this study was to develop a predictive model for TBI outcomes. Our study is categorized as retrospective, with diagnostic temporal perspective, using the classification approach. Data comprising 3138 patients admitted with TBI were collected from 2014 to 2017. Data about demographics, injury type, vital signs, Glasgow Coma Score, surgeries and outcome observed were analyzed for each patient. Our model considered the good recovery obtained through the Glasgow outcome score extended scale as a result measure. Data were analyzed using Bayesian Generalized Linear Model. The average age of patients were 31.18% and 82.02% were men. Of all TBI patients 67.87% were victims of road traffic injury. Considering the good recovery as outcome our model achieved an accuracy of 0.865 (95% CI 0.859, 0.870), with a sensitivity level of 0.890 and specificity of 0.713. our detection rate of a good recovery due TBI interventions based on admission data were of 0.762. The main predictors of a good recovery were: Glasgow coma level score, pulse oximetry, availability of intensive care unit bed. A predictive model with high accuracy and kappa level can be generated from data available at patient admission in a low resource setting such as Tanzania, Kilimanjaro Christian Medical Centre hospital. To our knowledge, this is the first prognostic model for severe TBI using machine learning techniques in Sub-Saharan Africa.