Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population.

Background: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members. Level of evidence: This observational study provides a level IV evidence on prognosis after TBI

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population

Background: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members. Level of evidence: This observational study provides a level IV evidence on prognosis after TBI

Transcranial Direct Current Stimulation for Post-Concussion Syndrome: Study Protocol for a Randomized Crossover Trial

BackgroundMild traumatic brain injury (MTBI) represents 70–80% of all treated brain injuries. A considerable proportion of MTBI patients experience post-concussion symptoms for a prolonged period after MTBI, and these symptoms are diagnosed as persistent post-concussion syndrome (PPCS). PPCS is defined as a range of physical, cognitive, and emotional symptoms. However, memory and executive dysfunction seems to be one of the most debilitating symptoms. Recently, non-invasive brain stimulation has been studied as a potential treatment method for traumatic brain injury (TBI) patients. Therefore, our primary goal is to verify the effects of transcranial direct current stimulation (tDCS) in patients with PPCS who demonstrate cognitive deficits in long-term episodic memory, working memory, and executive function following MTBI.Methods/designThis is a randomized crossover trial of patients with a history of MTBI with cognitive deficits in memory and executive function. Thirty adult patients will be randomized in a crossover manner to receive three weekly sessions of anodal tDCS (2 mA) at left dorsolateral prefrontal cortex, left temporal cortex, and sham stimulation that will be performed at 7-day intervals (washout period). The clinical diagnosis of PPCS will be determined using the Rivermead Post-Concussion Symptoms Questionnaire. Patients who meet the inclusion criteria will be assessed with a neuropsychological evaluation. A new battery of computerized neuropsychological tests will be performed before and immediately after each stimulation. Statistical analysis will be performed to determine trends of cognitive improvement.DiscussionThere is paucity of studies regarding the use of tDCS in TBI patients, and although recent results showed controversial data regarding the effects of tDCS in such patients, we will address specifically patients with PPCS and MTBI and no brain abnormalities on CT scan other than subarachnoid hemorrhage. Moreover, due to the missing information on literature regarding the best brain region to be studied, we will evaluate two different regions to find immediate effects of tDCS on memory and executive dysfunction.Clinical Trial Registrationwww.ClinicalTrials.gov, identifier NCT02292589 (https://register.clinicaltrials.gov)