Wang Mengqi, Shi Yunzhen, Pan Xinling, Wang Bin, Lu Bin, Ouyang Jian
Department of Neurology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People's Republic of China.
Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People's Republic of China.
Infect Drug Resist. 2023 Sep 20;16:6247-6257. doi: 10.2147/IDR.S427790. eCollection 2023.
We intend to develop a nomogram for predicting the mortality risk of hospitalized septic shock patients.
Data were collected from patients hospitalized with septic shock in Affiliated Dongyang Hospital of Wenzhou Medical University in China, over 10 years between January 2013 and January 2023. The eligible study participants were divided into modeling and validation groups. Factors independently related to the mortality in the modeling group were obtained by stepwise regression analysis. A logistic regression model and a nomogram were built. The model was evaluated based on the discrimination power (the area under the curve of the receiver operating characteristic, AUC), the calibration degree and decision curve analysis. In the validation group, the discrimination powers of the logistic regression model, the sequential organ failure assessment (SOFA) scoring model and machine learning model were compared.
A total of 1253 patients, including 878 patients in the modeling group and 375 patients in the validation group, were included in this study. Age, respiratory failure, serum cholinesterase, lactic acid, blood phosphorus, blood magnesium, total bilirubin, and pH were independent risk factors related to the mortality risk of septic shock. The AUCs of the prediction model for the modeling and validation groups were 0.881 and 0.868, respectively. The models had a good calibration degree and clinical applicability. The AUC of the SOFA model for the validation population was 0.799, significantly lower than that of our model. The AUCs of the random forest and ensemble models were 0.865 and 0.863, respectively, comparable to that of our logistical prediction model.
The model established in this study can effectively predict the mortality risk in patients hospitalized with septic shock. Thus, the model could be used clinically to determine the best therapy or management for patients with septic shock.
我们旨在开发一种用于预测住院感染性休克患者死亡风险的列线图。
收集了2013年1月至2023年1月期间在中国温州医科大学附属东阳医院住院的感染性休克患者的数据，为期10多年。符合条件的研究参与者被分为建模组和验证组。通过逐步回归分析获得建模组中与死亡率独立相关的因素。构建了逻辑回归模型和列线图。基于辨别力（受试者操作特征曲线下面积，AUC）、校准度和决策曲线分析对模型进行评估。在验证组中，比较了逻辑回归模型、序贯器官衰竭评估（SOFA）评分模型和机器学习模型的辨别力。
本研究共纳入1253例患者，其中建模组878例，验证组375例。年龄、呼吸衰竭、血清胆碱酯酶、乳酸、血磷、血镁、总胆红素和pH是与感染性休克死亡风险相关的独立危险因素。建模组和验证组预测模型的AUC分别为0.881和0.868。模型具有良好的校准度和临床适用性。验证人群中SOFA模型的AUC为0.799，显著低于我们的模型。随机森林模型和集成模型的AUC分别为0.865和0.863，与我们的逻辑预测模型相当。
本研究建立的模型可有效预测住院感染性休克患者的死亡风险。因此，该模型可在临床上用于确定感染性休克患者的最佳治疗或管理方案。
