Development of a Nomogram for Predicting Mortality Risk in Sepsis Patients During Hospitalization: A Retrospective Study

Abstract

Purpose We attempted to establish a model for predicting the mortality risk of sepsis patients during hospitalization. Patients and Methods Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method. Results A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively. Conclusion The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.

LASSO-Based Identification of Risk Factors and Development of a Prediction Model for Sepsis Patients

“…The area under the ROC curve was 0.847 in the modeling group and 0.826 in the validation group. 16 Given the heterogeneity observed in sepsis research, with patients presenting at various stages of the disease course, predicting outcomes in sepsis has produced diverse findings. 17 One study identified age, Sequential Organ Failure Assessment (SOFA) score, serum myoglobin (MYO), use of vasopressors, and mechanical ventilation as independent risk factors for one-year mortality.…”

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

“…Although septic shock is a progressed status of sepsis, they share common mortality-related predictive factors, including respiratory failure, total bilirubin, cholinesterase and lactic acid as described in our previous study. 9 The different variables enrolled in the final models reflect the disease-status specific factors in predicting the mortality risk. The patients in this study were diagnosed in the emergency department, and several septic shock related variables have been corrected by emergent clinical intervenes, including mean arterial pressure and procalcitonin.…”

“…The relevant variables were processed as described in our previous work. 9 Briefly, missing data were interpolated through multiple imputations ( Figure S1 ). Secondly, factors related to the prognosis of septic shock patients were selected by univariate and multivariate analysis, and those with non-linearity to logitp or with multicollinearity among the enrolled variables were excluded.…”

“…The logistic regression model was evaluated for the predictive power, a goodness of fit and net clinical benefit as previously. 9 Finally, a nomogram graph was constructed.…”

“…The model also has huge clinical application potential because it is easy to use. In our previous study, a nomogram has been established to predict the mortality risk in patients with sepsis, 9 but whether it could be used for death prediction in progressed status of sepsis (septic shock) remained unknown. In this study, a model for predicting the death risk of septic shock patients was established by incorporating several variables following hospitalization.…”