Cheng Li, Bai Wenhui, Song Ping, Zhou Long, Li Zhiyang, Gao Lun, Zhou Chenliang, Cai Qiang
Department of Critical Care Medicine, Eastern Campus, Renmin Hospital of Wuhan University, Wuhan 430200, China.
Department of Hepatobiliary Surgery, Eastern Campus, Renmin Hospital of Wuhan University, Wuhan 430200, China.
Diagnostics (Basel). 2023 Jun 29;13(13):2207. doi: 10.3390/diagnostics13132207.
A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated.
The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September 2022 were collected, of whom 998 patients admitted to Shouyi Hospital District were used as the training set and 866 patients admitted to Guanggu Hospital District were used as the validation set. Lasso regression was applied to screen the independent variables in the training set, and the model was externally validated in the validation set.
A total of 1864 patients after craniocerebral surgery were included in this study, of whom 219 (11.75%) had PCNSI. Multivariate logistic regression analysis showed that age > 70 years, a previous history of diabetes, emergency operation, an operation time ≥ 4 h, insertion of a lumbar cistern drainage tube ≥ 72 h, insertion of an intracranial drainage tube ≥ 72 h, intraoperative blood loss ≥ 400 mL, complicated with shock, postoperative albumin ≤ 30 g/L, and an ICU length of stay ≥ 3 days were independent risk factors for PCNSI. The area under the curve (AUC) of the training set was 0.816 (95% confidence interval (95%CI), 0.773-0.859, and the AUC of the validation set was 0.760 (95%CI, 0.715-0.805). The calibration curves of the training set and the validation set showed -values of 0.439 and 0.561, respectively, with the Hosmer-Lemeshow test. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value.
The nomograph model constructed in this study to predict the risk of PCNSI after craniocerebral surgery has a good predictive ability.
建立并验证预测颅脑手术后中枢神经系统感染（PCNSI）风险的列线图模型。
收集武汉大学人民医院2020年1月至2022年9月颅脑手术后患者的临床病历，其中收治于首义院区的998例患者作为训练集，收治于光谷院区的866例患者作为验证集。应用Lasso回归筛选训练集中的自变量，并在验证集中对模型进行外部验证。
本研究共纳入1864例颅脑手术后患者，其中219例（11.75%）发生PCNSI。多因素logistic回归分析显示，年龄>70岁、既往糖尿病史、急诊手术、手术时间≥4小时、腰大池引流管置入≥72小时、颅内引流管置入≥72小时、术中失血≥400毫升、并发休克、术后白蛋白≤30克/升及入住重症监护病房（ICU）时间≥3天是PCNSI的独立危险因素。训练集的曲线下面积（AUC）为0.816（95%置信区间（95%CI），0.773 - 0.859），验证集的AUC为0.760（95%CI，0.715 - 0.805）。训练集和验证集的校准曲线经Hosmer-Lemeshow检验，P值分别为0.439和0.561。临床决策曲线分析显示列线图模型具有较高的临床应用价值。
本研究构建的预测颅脑手术后PCNSI风险的列线图模型具有良好的预测能力。
