Development of a Prognostic Model to Predict Mortality after Traumatic Brain Injury in Intensive Care Setting in a Developing Country.
Mini JayanDhaval P ShuklaBhagavatula Indira DeviDhananjaya I BhatSubhas K KonarPublished in: Journal of neurosciences in rural practice (2021)
Objectives We aimed to develop a prognostic model for the prediction of in-hospital mortality in patients with traumatic brain injury (TBI) admitted to the neurosurgery intensive care unit (ICU) of our institute. Materials and Methods The clinical and computed tomography scan data of consecutive patients admitted after a diagnosis TBI in ICU were reviewed. Construction of the model was done by using all the variables of Corticosteroid Randomization after Significant Head Injury and International Mission on Prognosis and Analysis of Clinical Trials in TBI models. The endpoint was in-hospital mortality. Results A total of 243 patients with TBI were admitted to ICU during the study period. The in-hospital mortality was 15.3%. On multivariate analysis, the Glasgow coma scale (GCS) at admission, hypoxia, hypotension, and obliteration of the third ventricle/basal cisterns were significantly associated with mortality. Patients with hypoxia had eight times, with hypotensions 22 times, and with obliteration of the third ventricle/basal cisterns three times more chance of death. The TBI score was developed as a sum of individual points assigned as follows: GCS score 3 to 4 (+2 points), 5 to 12 (+1), hypoxia (+1), hypotension (+1), and obliteration third ventricle/basal cistern (+1). The mortality was 0% for a score of "0" and 85% for a score of "4." Conclusion The outcome of patients treated in ICU was based on common admission variables. A simple clinical grading score allows risk stratification of patients with TBI admitted in ICU.
Keyphrases
- traumatic brain injury
- intensive care unit
- computed tomography
- mechanical ventilation
- severe traumatic brain injury
- clinical trial
- endothelial cells
- mild traumatic brain injury
- mitral valve
- cardiovascular events
- emergency department
- pulmonary hypertension
- pulmonary artery
- risk factors
- magnetic resonance imaging
- machine learning
- open label
- data analysis
- pulmonary arterial hypertension
- atrial fibrillation
- congenital heart disease
- electronic health record
- acute respiratory distress syndrome
- deep learning
- extracorporeal membrane oxygenation
- cardiovascular disease
- heart failure
- type diabetes
- randomized controlled trial