PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: A total of 113 patients with FG were enrolled and allocated to the septic shock group (n = 24) or non-septic shock group (n = 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: A total of 113 patients with FG were enrolled and allocated to the septic shock group (n = 24) or non-septic shock group (n = 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).
UNASSIGNED: 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.
摘要：
■Fournier坏疽（FG）是一种罕见的危及生命的坏死性筋膜炎。FG患者感染性休克的危险因素尚不清楚。本研究旨在确定FG患者感染性休克的潜在危险因素并建立预测模型。
这项回顾性队列研究包括2013年5月至2020年5月在第六附属医院接受FG治疗的患者，中山大学(广州,中国）。将患者分为感染性休克组和非感染性休克组。使用L1惩罚逻辑回归模型来检测重要因素的主要影响，并使用惩罚二次判别分析方法来识别不同因素之间可能的交互影响。选择的主要因素和相互作用被用来获得基于贝叶斯信息准则的逻辑回归模型。
■共纳入113例FG患者，分为脓毒性休克组（n=24）或非脓毒性休克组（n=89）。通过基于贝叶斯信息准则的反向逻辑回归确定的最佳模型选择温度，血小板,总胆红素（TBIL）水平，盆腔计算机断层扫描/磁共振图像上的肺炎为主要线性效应，Na×TBIL为相互作用效应。通过我们的模型，FG合并感染性休克的概率的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）。
■我们开发了一种用于评估FG患者感染性休克风险的预测模型，该模型可以帮助临床医生识别FG危重患者并防止他们达到危机状态。
这项回顾性队列研究包括2013年5月至2020年5月在第六附属医院接受FG治疗的患者，中山大学(广州,中国）。将患者分为感染性休克组和非感染性休克组。使用L1惩罚逻辑回归模型来检测重要因素的主要影响，并使用惩罚二次判别分析方法来识别不同因素之间可能的交互影响。选择的主要因素和相互作用被用来获得基于贝叶斯信息准则的逻辑回归模型。
■共纳入113例FG患者，分为脓毒性休克组（n=24）或非脓毒性休克组（n=89）。通过基于贝叶斯信息准则的反向逻辑回归确定的最佳模型选择温度，血小板,总胆红素（TBIL）水平，盆腔计算机断层扫描/磁共振图像上的肺炎为主要线性效应，Na×TBIL为相互作用效应。通过我们的模型，FG合并感染性休克的概率的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）。
■我们开发了一种用于评估FG患者感染性休克风险的预测模型，该模型可以帮助临床医生识别FG危重患者并防止他们达到危机状态。
