Research Reports - Prediction of persistent post-concussion symptoms following mild traumatic brain injury

Journal of Neurotrauma
Published Online:25 Apr 2018

Ms. Maryse C. Cnossen, Dr. Joukje van der Naalt, ; Dr. Jacoba M SpikmanMr. Daan NieboerMr. John K Yue; Ethan A. WinklerDr. Geoffrey ManleyProf. Nicole von SteinbuechelDr. Suzanne PolinderMr. Ewout W Steyerberg, Ms. Hester Lingsma

Persistent post-concussion symptoms (PPCS) occur frequently after mild traumatic brain injury (mTBI). The identification of patients at risk for poor outcome remains challenging since 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 emergency department (ED).
 MTBI patients (Glasgow Coma Scale score 13-15) were prospectively recruited from three Dutch level I trauma centers between 2013-2015 in the UPFRONT study. PPCS were assessed using the Head Injury Severity Checklist at six-month 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; 241 (41%) developed PPCS. Existing models performed poorly at external validation (AUC: 0.57-0.64). The newly developed model included female sex (OR 1.48, 95%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 posttraumatic 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. 

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