Data_Sheet_1.DOCX

Abstract

Self-reported complaints are common after mild traumatic brain injury (mTBI). Particularly in elderly with mTBI, pre-injury status might play a relevant role in the recovery process. In most mTBI studies, however, pre-injury complaints are not analyzed nor are elderly included. Here, we aimed to identify which individual pre- and post-injury complaints are potential prognostic markers for incomplete recovery in elderly patients who sustained an mTBI. Since patients report many complaints across several domains that are strongly related, we used an interpretable machine learning approach to robustly deal with correlated predictors and boost classification performance. Pre- and post-injury levels of 20 individual complaints, as self-reported in the acute phase were analyzed. We used data from two independent studies separately: UPFRONT-study was used for training and validation; ReCONNECT-study for independent testing. Functional outcome was assessed with the Glasgow Outcome Scale Extended (GOSE). We dichotomized functional outcome into complete recovery (CR; GOSE=8) and incomplete recovery (IR; GOSE≤7). In total 148 elderly with mTBI (median age: 67 years, IQR: 9 years; UPFRONT: N=115; ReCONNECT: N=33) were included in this study. IR was observed in 74 (50%) patients. The classification model (IR vs. CR) achieved a good performance (ROC-AUC = 0.80; 95% CI: 0.74-0.86) based on a subset of only eight out of 40 pre- and post-injury complaints. We identified increased neck pain (p=0.001) from pre- to post-injury as strongest predictor of IR, followed by increased irritability (p=0.011) and increased forgetfulness (p=0.035) from pre- to post-injury. Our findings indicate that a subset of pre- and post-injury physical, emotional and cognitive complaints has predictive value for determining long-term functional outcome in elderly patients with mTBI. Particularly, post-injury neck pain, irritability and forgetfulness scores were associated with incomplete recovery and should be assessed early. The application of a machine learning approach holds promise for application in self-reported questionnaires to predict outcome after mTBI.