Prediction of 28-day mortality in patients with sepsis based on a predictive model: A retrospective cohort study - Yi Sun; Tingting Wang; Mengna Zhang; Shuchen Cao; Liwei Hua; Kun Zhang (Journal of International Medical Research)
Objective This study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit. Methods The health care records of 613 patients with sepsis who were hospitalized at the Affiliated Hospital of Chengde Medical University from 2022 to 2024 were retrospectively reviewed. Patients were randomly divided into training and testing sets in a 7:3 ratio. The least absolute shrinkage and selection operator regression method was used to identify potential prognostic factors for sepsis, followed by multivariate logistic regression to construct a nomogram prediction model. The predictive performance of the developed model was evaluated via receiver operating characteristic curves, decision curve analysis, and calibration curves. Results The predictive factors included the platelet distribution width to count ratio, mean platelet volume, N-terminal proB-type natriuretic peptide level, lactate level, respiratory tract infections, and diabetes. The area under the receiver operating characteristic curve for the nomogram model in the training set was 0.907, with sensitivity and specificity values of 0.846 and 0.831, respectively. The calibration curve demonstrated that the prediction results were consistent with the actual findings. Decision curve analysis revealed that the model showed robust performance in practical applications. Conclusions Platelet distribution width to count ratio, mean platelet volume, N-terminal proB-type natriuretic peptide level, lactate level, respiratory tract infection, and diabetes are closely associated with sepsis. A nomogram model based on these six variables demonstrates remarkable predictive performance and may assist clinicians in identifying high-risk patients and optimizing personalized therapy.