METHODS: Baseline data collected on admission of 296 moderate TBI patients with Glasgow Coma Scale (GCS) score of 9-11 was collected and analyzed. Multi-variable logistic regression modeling with backward stepwise elimination was used to develop a prediction model for IHT. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. Finally, the prediction model was validated in a separate cohort of 122 patients from 3 hospitals.
RESULTS: Four independent prognostic factors for IHT were identified: GCS score, Marshall head computed tomography score, injury severity score and location of contusion. The C-statistic of the prediction model in internal validation was 84.30% (95% confidence interval [CI]: 0.794-0.892). The area under the curve for the prediction model in external validation was 82.80% (95% CI: 0.747∼0.909).
CONCLUSIONS: A prediction model based on baseline parameters was found to be highly sensitive in distinguishing moderate TBI patients with GCS score of 9-11 who would suffer IHT. The high discriminative ability of the prediction model supports its use in identifying moderate TBI patients with GCS score of 9-11 who need ICP-lowering therapy or invasive ICP monitoring.
方法:收集并分析296例格拉斯哥昏迷量表(GCS)评分为9-11的中度TBI患者入院时的基线数据。使用具有反向逐步消除的多变量逻辑回归模型来建立IHT的预测模型。辨别功效,校准功效,并对预测模型的临床实用性进行了评价。最后,该预测模型在来自3家医院的122例患者的单独队列中得到验证.
结果:确定了IHT的四个独立预后因素:GCS评分,马歇尔头部计算机断层扫描评分,损伤严重程度评分和挫伤部位。内部验证预测模型的C统计量为84.30%(95%置信区间[CI]:0.794-0.892)。外部验证预测模型的曲线下面积为82.80%(95%CI:0.747~0.909)。
结论:发现基于基线参数的预测模型在区分将遭受IHT的GCS评分为9-11的中度TBI患者方面高度敏感。预测模型的高辨别能力支持其用于识别需要降低ICP治疗或侵入性ICP监测的GCS评分为9-11的中度TBI患者。