[Analysis of 28 day-mortality risk factors in sepsis patients and construction and validation of predictive model].

Objective: To construct and validate a nomogram model for predicting the risk of 28-day mortality in sepsis patients.

Methods: A retrospective cohort study was conducted. 281 sepsis patients admitted to the department of intensive care unit (ICU) of the 940th Hospital of the Joint Logistics Support Force of PLA from January 2017 to December 2022 were selected as the research subjects. The patients were divided into a training set (197 cases) and a validation set (84 cases) according to a 7 : 3 ratio. The general information, clinical treatment measures and laboratory examination results within 24 hours after admission to ICU were collected. Patients were divided into survival group and death group based on 28-day outcomes. The differences in various data were compared between the two groups. The optimal predictive variables were selected using Lasso regression, and univariate and multivariate Logistic regression analyses were performed to identify factors influencing the mortality of sepsis patients and to establish a nomogram model. Receiver operator characteristic curve (ROC curve), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the nomogram model.

Results: Out of 281 cases of sepsis, 82 cases died with a mortality of 29.18%. The number of patients who died in the training and validation sets was 54 and 28, with a mortality of 27.41% and 33.33% respectively. Lasso regression, univariate and multivariate Logistic regression analysis screened for 5 independent predictors associated with 28-day mortality. There were use of vasoactive drugs [odds ratio (OR) = 5.924, 95% confidence interval (95%CI) was 1.244-44.571, P = 0.043], acute physiology and chronic health evaluation II (APACHE II: OR = 1.051, 95%CI was 1.000-1.107, P = 0.050), combined with multiple organ dysfunction syndrome (MODS: OR = 17.298, 95%CI was 5.517-76.985, P < 0.001), neutrophil count (NEU: OR = 0.934, 95%CI was 0.879-0.988, P = 0.022) and oxygenation index (PaO₂/FiO₂: OR = 0.994, 95%CI was 0.988-0.998, P = 0.017). A nomogram model was constructed using the independent predictive factors mentioned above, ROC curve analysis showed that the AUC of the nomogram model was 0.899 (95%CI was 0.856-0.943) and 0.909 (95%CI was 0.845-0.972) for the training and validation sets respectively. The C-index was 0.900 and 0.920 for the training and validation sets respectively, with good discrimination. The Hosmer-Lemeshoe tests both showed P > 0.05, indicating good calibration. Both DCA and CIC plots demonstrate the model's good clinical utility.

Conclusions: The use of vasoactive, APACHE II score, comorbid MODS, NEU and PaO₂/FiO₂ are independent risk factors for 28-day mortality in patients with sepsis. The nomogram model based on these 5 indicators has a good predictive ability for the occurrence of mortality in sepsis patients.