Abstract
Self-reported complaints are common after mild traumatic brain injury (mTBI). Particularly in the elderly with mTBI, the pre-injury status might play a relevant role in the recovery process. In most mTBI studies, however, pre-injury complaints are neither analyzed nor are the elderly included. Here, we aimed to identify which individual pre- and post-injury complaints are potential prognostic markers for incomplete recovery (IR) 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 (ML) 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 and 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 IR (GOSE ≤ 7). In total 148 elderly with mTBI (median age: 67 years, interquartile range [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 (the area under the receiver operating characteristic curve [ROC-AUC] = 0.80; 95% CI: 0.74–0.86) based on a subset of only 8 out of 40 pre- and post-injury complaints. We identified increased neck pain (p = 0.001) from pre- to post-injury as the 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 outcomes in elderly patients with mTBI. Particularly, post-injury neck pain, irritability, and forgetfulness scores were associated with IR and should be assessed early. The application of an ML approach holds promise for application in self-reported questionnaires to predict outcomes after mTBI.
Highlights
Traumatic brain injury (TBI) is one of the most important causes of morbidity and mortality in adults [1]
The diagnosis of mild traumatic brain injury (mTBI) was based on the following criteria: attending the hospital with an mTBI defined by a Glasgow Coma Scale (GCS) score of 13–15, loss of consciousness ≤30 min, and/or post-traumatic amnesia of ≤24 h [30]
148 elderly with mTBI were included in this study
Summary
Traumatic brain injury (TBI) is one of the most important causes of morbidity and mortality in adults [1]. Mild TBI (mTBI) accounts for 85% of the cases [2, 3]. Most patients who sustained mTBI report post-injury complaints within the first weeks after injury, which can involve cognitive (e.g., forgetfulness and poor concentration), emotional (e.g., irritability and anxiety), and/or physical domains (e.g., headaches and fatigue). Post-injury complaints usually resolve within 3 months after injury, they may persist for months to years in a subgroup (≈20%), which is often referred to as post-concussion syndrome (PCS) [4]. According to the International Classification of Diseases (ICD)-10 definition, the diagnosis of PCS requires the presence of three or more post-injury complaints for at least 4 weeks [5]. Individual complaints are non-specific to PCS and can be found in healthy individuals and across a variety of clinical populations [6,7,8]
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