The Second Affiliated Hospital, Department of Emergency, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Comput Math Methods Med. 2022 Aug 8;2022:7107063. doi: 10.1155/2022/7107063. eCollection 2022.
Herein, we purposed to establish a nomogram model capable of assessing the probability of in-hospital survival in patients with multiple trauma.
Our retrospective study is associated with 286 multiple trauma patients with 21 variables from 2017 to 2021 in The Second Affiliated Hospital, Hengyang Medical School, University of South China. We performed the univariate and multivariate logistic regression analyses for investigating the risk factors of multiple trauma. Further, we constructed a novel nomogram model, and this nomogram was evaluated by a calibration plot. Based on the multivariate analysis or the nomogram prediction model, we calculated the risk score of each patient for multiple trauma. Moreover, we compared the survival probability between the high-risk score and low-risk score groups. Finally, we assessed the discrimination of the risk score by using the C-index and the time-dependent receiver operating characteristics (ROC) curve.
Multivariate regression analysis revealed that the age and ISS scores were the independent risk factors, while the GCS score had protective effects on in-hospital survival. The high C-index and area under the curve (AUC) of the ROC curve confirmed reasonable discrimination for the multivariate analysis and the nomogram prediction model. Further, the calibration plot indicated reasonable accuracy of the nomogram predicting 30-day and 60-day survival probabilities.
The nomogram model established here has good predictive efficacy for in-hospital survival of patients with multiple injuries.
在此，我们旨在建立一个能够评估多发伤患者住院期间生存率的列线图模型。
本回顾性研究纳入了 2017 年至 2021 年期间在南华大学附属第二医院的 286 例多发伤患者，共涉及 21 个变量。我们对这些患者进行了单因素和多因素 logistic 回归分析，以探讨多发伤的危险因素。此外，我们构建了一个新的列线图模型，并通过校准图对其进行评估。基于多因素分析或列线图预测模型，我们计算了每位患者多发伤的风险评分。然后，我们比较了高风险评分组和低风险评分组的生存概率。最后，我们通过 C 指数和时间依赖性接受者操作特征（ROC）曲线评估了风险评分的区分度。
多因素回归分析显示，年龄和 ISS 评分是独立的危险因素，而 GCS 评分对住院期间的生存率有保护作用。ROC 曲线的高 C 指数和 AUC 证实了多因素分析和列线图预测模型具有合理的区分度。此外，校准图表明列线图预测 30 天和 60 天生存率的准确性合理。
本研究建立的列线图模型对多发伤患者的住院期间生存率具有良好的预测效果。
