Xu Ligang, Kang Zhaofeng, Wang Dongfang, Liu Yukun, Wang Chuntao, Li Zhanfei, Bai Xiangjun, Wang Yuchang
Division of Trauma Surgery, Emergency Surgery and Surgical Critical, Tongji Trauma Center, Wuhan, China.
Department of Emergency and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Med (Lausanne). 2023 Aug 24;10:1249724. doi: 10.3389/fmed.2023.1249724. eCollection 2023.
Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS) is a significant contributor to adverse long-term outcomes in severe trauma patients.
The objective of this study was to establish and validate a PIICS predictive model in severe trauma patients, providing a practical tool for early clinical prediction.
Adult severe trauma patients with an Injury Severity Score (ISS) of ≥16, admitted between October 2020 and December 2022, were randomly divided into a training set and a validation set in a 7:3 ratio. Patients were classified into PIICS and non-PIICS groups based on diagnostic criteria. LASSO regression was used to select appropriate variables for constructing the prognostic model. A logistic regression model was developed and presented in the form of a nomogram. The performance of the model was evaluated using calibration and ROC curves.
A total of 215 patients were included, consisting of 155 males (72.1%) and 60 females (27.9%), with a median age of 51 years (range: 38-59). NRS2002, ISS, APACHE II, and SOFA scores were selected using LASSO regression to construct the prognostic model. The AUC of the ROC analysis for the predictive model in the validation set was 0.84 (95% CI 0.72-0.95). The Hosmer-Lemeshow test in the validation set yielded a χ value of 14.74, with a value of of 0.098.
An accurate and easily implementable PIICS risk prediction model was established. It can enhance risk stratification during hospitalization for severe trauma patients, providing a novel approach for prognostic prediction.
持续性炎症、免疫抑制和分解代谢综合征（PIICS）是严重创伤患者长期不良预后的重要因素。
本研究旨在建立并验证严重创伤患者的PIICS预测模型，为早期临床预测提供实用工具。
2020年10月至2022年12月期间收治的损伤严重程度评分（ISS）≥16的成年严重创伤患者，按7:3的比例随机分为训练集和验证集。根据诊断标准将患者分为PIICS组和非PIICS组。采用LASSO回归选择合适的变量构建预后模型。建立逻辑回归模型并以列线图的形式呈现。使用校准曲线和ROC曲线评估模型的性能。
共纳入215例患者，其中男性155例（72.1%），女性60例（27.9%），中位年龄51岁（范围：38 - 59岁）。使用LASSO回归选择NRS2002、ISS、APACHE II和SOFA评分来构建预后模型。验证集中预测模型的ROC分析AUC为0.84（95%CI 0.72 - 0.95）。验证集中Hosmer-Lemeshow检验的χ值为14.74，P值为0.098。
建立了一个准确且易于实施的PIICS风险预测模型。它可以加强严重创伤患者住院期间的风险分层，为预后预测提供一种新方法。
