Department of Graduate School, Qinghai University, Xining, 810016, Qinghai, China.
Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining, 810007, Qinghai, China.
BMC Neurol. 2021 Nov 29;21(1):463. doi: 10.1186/s12883-021-02482-4.
Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients.
A total of 426 patients with cerebral contusion (CC) admitted to the People's Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).
Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026-0.952) in the training cohort and (0.93, 95% CI, 0.032-0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients.
This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.
脑挫裂伤（CC）是颅脑损伤（TBI）患者最严重的损伤类型之一。本研究总结分析了 CC 患者的基线资料、影像学特征和实验室检查，旨在建立并验证一种预测模型（列线图），以评估患者的临床结局。
纳入 2018 年 1 月至 2021 年 1 月期间青海省人民医院和青岛大学附属医院收治的 426 例脑挫裂伤患者，按照 2:1 的比例将患者随机分为训练队列（n=284）和验证队列（n=142）。采用最小绝对收缩和选择算子（Lasso）回归筛选影响患者预后的高危因素，并建立预测模型。通过受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）分析预测模型的识别能力和临床应用价值。
Lasso 回归分析筛选出年龄、格拉斯哥昏迷评分（GCS）、基底池状态、中线移位（MLS）、第三脑室状态、颅内压（ICP）和脑水肿 CT 分级等 12 个独立预后因素，并纳入列线图。模型在训练队列中的预测性能良好，C 指数为（0.87，95%CI，0.026-0.952），在验证队列中的 C 指数为（0.93，95%CI，0.032-0.965）。临床决策曲线分析（DCA）也表明，该模型为患者带来了较高的临床获益。
本研究建立了一种预测 CC 患者预后的高准确性列线图模型，该模型成本低、易于推广，尤其适用于急性环境。同时，本研究首次纳入 CSF-葡萄糖/乳酸比值（C-G/L）、挫伤体积和水肿区平均 CT 值作为 CC 患者预后不良的独立预测因子。但该模型仍存在一定的局限性和不足，需要大样本、多中心前瞻性研究来验证和改进我们的结果。
