Abstract：
OBJECTIVE: Patients with moderate traumatic brain injury (TBI) are under the threat of intracranial hypertension (IHT). However, it is unclear which moderate TBI patient will develop IHT and should receive intracranial pressure (ICP)-lowering treatment or invasive ICP monitoring after admission. The purpose of the present study was to develop and validate a prediction model that estimates the risk of IHT in moderate TBI patients.
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.
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.
摘要：
目的：中度创伤性脑损伤（TBI）患者面临颅内高压（IHT）的威胁。然而,目前尚不清楚哪个中度TBI患者会发生IHT,入院后应接受降低颅内压(ICP)治疗或有创ICP监测.本研究的目的是开发和验证一种预测模型，该模型可以估计中度TBI患者的IHT风险。
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。
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。
