An Individualized Nomogram for Predicting Mortality Risk of Septic Shock Patients During Hospitalization: A ten Years Retrospective Analysis

Abstract

Mengqi Wang,1 Yunzhen Shi,2 Xinling Pan,3 Bin Wang,4 Bin Lu,2 Jian Ouyang4 1Department of Neurology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China; 2Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of China; 3Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, People’s Republic of China; 4Department of Emergency, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang Province, People’s Republic of ChinaCorrespondence: Bin Lu, Department of Infectious Diseases, Affiliated Dongyang Hospital of Wenzhou Medical University, No. 60 Wuningxi Road, Dongyang, People’s Republic of China, Email [email protected] Jian Ouyang, Department of Emergency, Affiliated Dongyang Hospital of Wenzhou Medical University, No. 60 Wuningxi Road, Dongyang, Zhejiang, People’s Republic of China, Email [email protected]: We intend to develop a nomogram for predicting the mortality risk of hospitalized septic shock patients.Patients and Methods: 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.Results: 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.Conclusion: 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.Keywords: septic shock, prediction model, mortality risk, nomogram, SOFA

Keywords

• septic shock
• prediction model
• mortality risk
• nomogram
• sofa.