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Background Sepsis-associated encephalopathy (SAE) occurs as a result of systemic inflammation caused by sepsis. It has been observed that the majority of sepsis patients experience SAE while being treated in the intensive care unit (ICU), and a significant number of survivors continue suffering from cognitive impairment even after recovering from the illness. The objective of this study was to create a predictive nomogram that could be used to identify SAE risk factors in patients with ICU sepsis. Methods We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We defined SAE as a Glasgow Coma Scale (GCS) score of 15 or less, or delirium. The patients were randomly divided into training and validation cohorts. We used least absolute shrinkage and selection operator (LASSO) regression modeling to optimize feature selection. Independent risk factors were determined through a multivariable logistic regression analysis, and a prediction model was built. The performance of the nomogram was evaluated using various metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results Among the 4,476 sepsis patients screened, 2,781 (62.1%) developed SAE. In-hospital mortality was higher in the SAE group compared to the non-SAE group (9.5% vs. 3.7%, p 0.001). Several variables were analyzed, including the patient’s age, gender, BMI on admission, mean arterial pressure, body temperature, platelet count, sodium level, and use of midazolam. These variables were used to create and validate a nomogram. The nomogram’s performance, assessed by AUC, NRI, IDI, and DCA, was found to be superior to the conventional SOFA score combined with delirium. Calibration plots and the Hosmer-Lemeshow test confirmed the accuracy of the nomogram. The enhanced NRI and IDI values demonstrated that our scoring system outperformed traditional diagnostic approaches. Additionally, the DCA curve indicated the practicality of the nomogram in clinical settings. Conclusion This study successfully identified autonomous risk factors associated with the emergence of SAE in sepsis patients and utilized them to formulate a predictive model. The outcomes of this investigation have the potential to serve as a valuable clinical resource for the timely detection of SAE in patients.
Jin et al. (Tue,) studied this question.