Development and validation of a nomogram model for predicting 28-day mortality in patients with sepsis.

Background: This study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit (ICU). Methods: We retrospectively analyzed data from 331 patients with sepsis admitted to the ICU as a training set and collected a validation set of 120 patients. Both groups were followed for 28 days. Logistic regression analyses were performed to identify the potential prognostic factors for sepsis-related 28-day mortality. A nomogram model was generated to predict 28-day mortality in patients with sepsis in the ICU. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to evaluate the model's prediction performance and clinical application. In addition, we used ROC curve analysis and DCA to compare this model with the sequential organ failure assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE II) scores and further assessed the clinical value of our model. Results: Logistic multivariate regression analysis revealed that mechanical ventilation, oxygenation index, and lactate and blood urea nitrogen (BUN) levels were independent predictors of 28-day mortality in patients with sepsis in the ICU. We developed a nomogram model based on these results to further predict 28-day mortality. The model demonstrated satisfactory calibration curves for both training and validation sets. Additionally, in the training set, the area under the ROC curve (AUC) for this model was 0.80. In the validation set, the AUC was 0.82. DCA showed that the high-risk thresholds ranged between 0 and 0.86 in the training set and between 0 and 0.75 in the validation set. We compared the ROC curve and DCA of this model with those of SOFA and APACHE II scores in both the training and validation sets. In the training set, the AUC of this model was significantly higher than those of the SOFA ( Conclusion: The present study used four common clinical variables, including mechanical ventilation, oxygenation index and lactate and BUN levels, to develop a nomogram model to predict 28-day mortality in patients with sepsis in the ICU. Our model demonstrated robust prediction performance and clinical application after validation and comparison.