Explainable machine learning of PROGRESS-Plus social factors predicts cognitive trajectories after traumatic brain injury

Scientific research on social parameters for prognosis after traumatic brain injury (TBI) is evolving, yet results remain heterogeneous, and predictors for risk stratification are lacking. To understand how social parameters are linked to cognitive outcomes after TBI, we applied machine learning (ML) algorithms using cohort-level data from 30 published studies including 2,364 participants with TBI (72% male; 55% mild, 45% moderate-severe injury). We extracted and harmonised longitudinal data following the PROGRESS-Plus framework, and used the data as predictors of rate of change in cognition post-TBI. We developed random forest, gradient boosting (GB), and extreme GB predictive models, accounting for time from injury to baseline assessment, time between assessments, and country-level structural indicators. We used mean absolute error and root mean squared error to evaluate model performance, and Shapley Additive Explanations analysis for explanatory model predictions. Results highlighted time interval, country-level structural indicators, age, and variation in education as key predictors for rate of change for both injury severities. Sensitivity analyses for predicting rate of change in executive function and learning and memory confirmed the robustness of the results. Our work contributes to novel ML meta-research for understanding prognosis and advancing precision in predicting cognitive outcomes after TBI.