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Prediction of Persistent Post-Concussion Symptoms after Mild Traumatic Brain Injury
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Prediction of Persistent Post-Concussion Symptoms after Mild Traumatic Brain Injury
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Prediction of Persistent Post-Concussion Symptoms after Mild Traumatic Brain Injury
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Journal Article
Prediction of Persistent Post-Concussion Symptoms after Mild Traumatic Brain Injury
2018
Overview
Persistent post-concussion symptoms (PPCS) occur frequently after mild traumatic brain injury (mTBI). The identification of patients at risk for poor outcome remains challenging because valid prediction models are missing. The objectives of the current study were to assess the quality and clinical value of prediction models for PPCS and to develop a new model based on the synthesis of existing models and addition of complaints at the emergency department (ED). Patients with mTBI (Glasgow Coma Scale score 13–15) were recruited prospectively from three Dutch level I trauma centers between 2013 and 2015 in the UPFRONT study. PPCS were assessed using the Head Injury Severity Checklist at six months post-injury. Two prediction models (Stulemeijer 2008; Cnossen 2017) were examined for calibration and discrimination. The final model comprised variables of existing models with the addition of headache, nausea/vomiting, and neck pain at ED, using logistic regression and bootstrap validation.
Overall, 591 patients (mean age 51years, 41% female) were included; PPCS developed in 241 (41%). Existing models performed poorly at external validation (area under the curve [AUC]: 0.57–0.64). The newly developed model included female sex (odds ratio [OR] 1.48, 95% confidence interval [CI] [1.01–2.18]), neck pain (OR 2.58, [1.39–4.78]), two-week post-concussion symptoms (OR 4.89, [3.19–7.49]) and two-week post-traumatic stress (OR 2.98, [1.88-4.73]) as significant predictors. Discrimination of this model was adequate (AUC after bootstrap validation: 0.75). Existing prediction models for PPCS perform poorly. A new model performs reasonably with predictive factors already discernible at ED warranting further external validation. Prediction research in mTBI should be improved by standardizing definitions and data collection and by using sound methodology.
