Zheng Yan-Chao, Qian Jun-Wei, Li An-Ni, Yuan Yi-Nuo, Ma Sen-Lin, Chen Mingquan
Department of Emergency Medicine, Huashan Hospital, Fudan University, 12 Urumqi Middle Road, Jing 'an District, Shanghai, China.
Dept. of Emergency, Dept. of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
Sci Rep. 2025 Jan 20;15(1):2574. doi: 10.1038/s41598-025-85743-3.
Here we describe the derivation and validation of a prognostic nomogram for patients with Traumatic Intracranial Hemorrhage (tICH) after surgical evacuation. This is a retrospective study based on 245 patients admitted to the Department of Neurosurgery of Huashan Hospital affiliated to Fudan University, between August 2005, and August 2023. We divided the dataset into primary and validation data by the ratio of 7:3. The LASSO regression model was used for predictor selection. The nomogram was developed using Cox regression models. The predictive performance of the nomogram was assessed by concordance index (C index) and calibration in the primary and validation cohorts. We also used decision curve analysis (DCA) to describe the clinical value. The main outcome was death related to tICH. The nomogram incorporated age, GCS-E, history of hypertension, and cerebellar hematoma, which was selected by the LASSO regression model. The nomogram showed good calibration and discrimination in the primary and validation data, with a 1-year C-index of 0.882 (95% CI, 0.777 to 0.987) and 0.818 (95% CI, 0.669 to 0.968), respectively. Decision curve analysis indicated that the nomogram is clinically useful when the patient or doctor's threshold probability ranges from 10 to 100%. In this study, we found that the tICH-related mortality rate was 11.42% (28/245). In the elderly cohort aged ≥ 65 years, the mortality rate increased to 28.13%(18/64). The nomogram we developed here can be conveniently used to predict the long-term prognosis of patients with tICH after surgical evacuation.Retrospectively registered: KY2024-860.
在此，我们描述了一种用于创伤性颅内出血（tICH）患者手术清除术后预后列线图的推导和验证。这是一项回顾性研究，基于2005年8月至2023年8月期间复旦大学附属华山医院神经外科收治的245例患者。我们按7:3的比例将数据集分为原始数据和验证数据。采用LASSO回归模型进行预测变量选择。列线图使用Cox回归模型构建。通过一致性指数（C指数）和原始队列及验证队列中的校准来评估列线图的预测性能。我们还使用决策曲线分析（DCA）来描述其临床价值。主要结局是与tICH相关的死亡。该列线图纳入了年龄、格拉斯哥昏迷量表扩展版（GCS-E）、高血压病史和小脑血肿，这些是通过LASSO回归模型选择的。列线图在原始数据和验证数据中显示出良好的校准和区分能力，1年C指数分别为0.882（95%CI，0.777至0.987）和0.818（95%CI，0.669至0.968）。决策曲线分析表明，当患者或医生的阈值概率在10%至100%范围内时，该列线图具有临床实用性。在本研究中，我们发现tICH相关死亡率为11.42%（28/245）。在年龄≥65岁的老年队列中，死亡率增至28.13%（18/64）。我们在此开发的列线图可方便地用于预测tICH患者手术清除术后的长期预后。回顾性注册：KY2024-860。
