Yang Yang, Wang Li-Chun, Yu Xin-Yang, Zhang Xiao-Fei, Yang Zhong-Qing, Zheng Yang-Zi, Jiang Bin-Yan, Chen Lei
Department of Critical Care Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China.
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Gastroenterol Rep (Oxf). 2022 Aug 11;10:goac038. doi: 10.1093/gastro/goac038. eCollection 2022.
Fournier's gangrene (FG) is a rare life-threatening form of necrotizing fasciitis. The risk factors for septic shock in patients with FG are unclear. This study aimed to identify potential risk factors and develop a prediction model for septic shock in patients with FG.
This retrospective cohort study included patients who were treated for FG between May 2013 and May 2020 at the Sixth Affiliated Hospital, Sun Yat-sen University (Guangzhou, China). The patients were divided into a septic shock group and a non-septic shock group. An L1-penalized logistic regression model was used to detect the main effect of important factors and a penalized Quadratic Discriminant Analysis method was used to identify possible interaction effects between different factors. The selected main factors and interactions were used to obtain a logistic regression model based on the Bayesian information criterion.
A total of 113 patients with FG were enrolled and allocated to the septic shock group ( = 24) or non-septic shock group ( = 89). The best model selected identified by backward logistic regression based on Bayesian information criterion selected temperature, platelets, total bilirubin (TBIL) level, and pneumatosis on pelvic computed tomography/magnetic resonance images as the main linear effect and Na × TBIL as the interaction effect. The area under the ROC curve of the probability of FG with septic shock by our model was 0.84 (95% confidence interval, 0.78-0.95). The Harrell's concordance index for the nomogram was 0.864 (95% confidence interval, 0.78-0.95).
We have developed a prediction model for evaluation of the risk of septic shock in patients with FG that could assist clinicians in identifying critically ill patients with FG and prevent them from reaching a crisis state.
福尼尔坏疽（FG）是一种罕见的危及生命的坏死性筋膜炎形式。FG患者发生感染性休克的危险因素尚不清楚。本研究旨在确定潜在的危险因素，并建立FG患者感染性休克的预测模型。
这项回顾性队列研究纳入了2013年5月至2020年5月在中山大学附属第六医院（中国广州）接受FG治疗的患者。将患者分为感染性休克组和非感染性休克组。使用L1惩罚逻辑回归模型检测重要因素的主要效应，并使用惩罚二次判别分析方法识别不同因素之间可能的交互效应。基于贝叶斯信息准则，将选定的主要因素和交互作用用于获得逻辑回归模型。
共纳入113例FG患者，分为感染性休克组（n = 24）和非感染性休克组（n = 89）。基于贝叶斯信息准则的向后逻辑回归选择的最佳模型确定体温、血小板、总胆红素（TBIL）水平以及盆腔计算机断层扫描/磁共振图像上的气体肿作为主要线性效应，Na×TBIL作为交互效应。我们模型预测FG合并感染性休克概率的ROC曲线下面积为0.84（95%置信区间，0.78 - 0.95）。列线图的Harrell一致性指数为0.864（95%置信区间，0.78 - 0.95）。
我们建立了一个用于评估FG患者感染性休克风险的预测模型，该模型可协助临床医生识别FG重症患者，并防止他们进入危急状态。
