Wojcik Susan M
Department of Emergency Medicine, Upstate Medical University , Syracuse, NY , USA.
Brain Inj. 2014;28(4):422-30. doi: 10.3109/02699052.2014.884241. Epub 2014 Feb 24.
To identify factors that can predict which emergency department (ED) patients with mTBI are likely to develop persistent post-concussion symptoms (PPCS).
A matched case-control study was conducted at a Level 1 trauma centre between June 2006 and July 2009. Patients diagnosed with mTBI in the ED and diagnosed at a concussion management programme with at least one PPCS (85 cases) were compared to patients diagnosed with mTBI in the ED (340 controls) to determine if factors assessed at the time of ED presentation could predict patients likely to develop persistent symptoms.
Multivariable hierarchical logistic regression with variables indicating increased risk for PPCS (prior mTBI, history of depression, history of anxiety, multiple injury, forgetfulness/poor memory, noise sensitivity, or light sensitivity) resulted in a final predictive model including prior mTBI, history of anxiety, forgetfulness/poor memory and light sensitivity. The final model had a specificity of 87.9% and a sensitivity of 69.9%.
A strong prediction model to identify those ED patients with mTBI at risk for PPCS was developed and could be easily implemented in the ED; therefore, helping to target those patients who would potentially benefit from close follow-up.
确定能够预测哪些急诊科（ED）中轻度创伤性脑损伤（mTBI）患者可能会出现持续性脑震荡后症状（PPCS）的因素。
2006年6月至2009年7月期间，在一家一级创伤中心进行了一项匹配病例对照研究。将在急诊科被诊断为mTBI且在脑震荡管理项目中被诊断出至少有一种PPCS的患者（85例）与在急诊科被诊断为mTBI的患者（340例对照）进行比较，以确定在急诊科就诊时评估的因素是否能够预测可能出现持续性症状的患者。
对表明PPCS风险增加的变量（既往mTBI、抑郁症病史、焦虑症病史、多发伤、健忘/记忆力差、噪声敏感或光敏感）进行多变量分层逻辑回归分析，得出一个最终预测模型，该模型包括既往mTBI、焦虑症病史、健忘/记忆力差和光敏感。最终模型的特异性为87.9%，敏感性为69.9%。
开发了一个强大的预测模型，用于识别有PPCS风险的急诊科mTBI患者，并且该模型可在急诊科轻松实施；因此，有助于确定那些可能从密切随访中获益的患者。
