Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients.

OBJECTIVE: To develop and validate practical prognostic models for death at 14 days and for death or severe disability six months after traumatic brain injury. DESIGN: Multivariable logistic regression to select variables that were independently associated with two patient outcomes. Two models designed: "basic" model (demographic and clinical variables only) and "CT" model (basic model plus results of computed tomography). The models were subsequently developed for high and low-middle income countries separately. SETTING: Medical Research Council (MRC) CRASH Trial. SUBJECTS: 10,008 patients with traumatic brain injury. Models externally validated in a cohort of 8509. RESULTS: The basic model included four predictors: age, Glasgow coma scale, pupil reactivity, and the presence of major extracranial injury. The CT model also included the presence of petechial haemorrhages, obliteration of the third ventricle or basal cisterns, subarachnoid bleeding, midline shift, and non-evacuated haematoma. In the derivation sample the models showed excellent discrimination (C statistic above 0.80). The models showed good calibration graphically. The Hosmer-Lemeshow test also indicated good calibration, except for the CT model in low-middle income countries. External validation for unfavourable outcome at six months in high income countries showed that basic and CT models had good discrimination (C statistic 0.77 for both models) but poorer calibration. CONCLUSION: Simple prognostic models can be used to obtain valid predictions of relevant outcomes in patients with traumatic brain injury

Monitoring of Intracranial Pressure in Meningitis

The literature on intracranial pressure (ICP) monitoring in meningitis is limited to case reports and a handful of descriptive series. The aim of this study is to investigate relationships among ICP, cerebral perfusion pressure (CPP), and outcome in meningitis and to identify whether ICP affected clinical decisions.status: publishe

Multivariable and Bayesian network analysis of outcome predictors in acute aneurysmal subarachnoid hemorrhage: review of a pure surgical series in the Post- International Subarachnoid Aneurysm Trial Era

Background: Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatmentmodalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. Objective: To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. Methods: We reviewed a single surgeon\u27s case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Results: Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong\u27s P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Conclusion: Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation