Construction and verification of a nomogram model for the risk of death in sepsis patients.

At present, there is insufficient evidence to evaluate the prognosis of patients with sepsis. This study anazed the clinical data of 822 sepsis patients in the ICU of a tertiary Grade A hospital to construct and validate a nomogram model for predicting the 28-day mortality risk in sepsis patients. The model was constructed using multivariate logistic regression analysis to screen for independent risk factors affecting prognosis, and a mortality risk prediction model was built based on these independent risk factors. The performance of the model was evaluated using the Hosmer-Lemeshow test, receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA). Multivariate logistic regression identified five independent risk factors for 28-day mortality in sepsis patients: Age, SOFA score, CRP, Mechanical ventilation, and the use of Vasoactive drugs. The odds ratios (OR) and 95% confidence intervals (95% CI) for these factors were 1.037 (1.024-1.050), 1.093 (1.044-1.145), 1.034 (1.026-1.042), 1.967 (1.176-3.328), and 2.515 (1.611-3.941), respectively, with all P-values < 0.05. Based on these five independent risk factors, a nomogram model was constructed, with the area under the ROC curve (AUC) in the training set and external validation set being 0.849 (95% CI 0.818-0.880) and 0.837 (95% CI 0.887-0.886), respectively. Both the DCA curve and calibration plot confirmed that the model has good clinical efficacy. The nomogram prediction model established in this study has excellent predictive ability, which can help clinicians identify high-risk patients early and provide guidance for clinical decision-making.