A simple nomogram for predicting the mortality of PICU patients with sepsis-associated encephalopathy: a multicenter retrospective study - Wang, Guan; Gao, Yan; Fu, Yanan; Huo, Qin; Guo, Enyu; Jiang, Qin; Liu, Jing; Jiang, Xinzhu; Liu, Xinjie (Frontiers in Neurology, 2024)
Background As one of the serious complications of sepsis in children, sepsis-associated encephalopathy (SAE) is associated with significantly poor prognosis and increased mortality. However, predictors of outcomes for pediatric SAE patients have yet to be identified. The aim of this study was to develop nomograms to predict the 14-day and 90-day mortality of children with SAE, providing early warning to take effective measures to improve prognosis and reduce mortality. Methods In this multicenter, retrospective study, we screened 291 patients with SAE admitted to the PICU between January 2017 and September 2022 in Shandong Province. A least absolute shrinkage and selector operation (LASSO) method was used to identify the optimal prognostic factors predicting the outcomes in pediatric patients with SAE. Then, multivariable logistic regression analysis was performed based on these variables, and two nomograms were built for visualization. We used the area under the curve (AUC), calibration curves and decision curves to test the accuracy and discrimination of the nomograms in predicting outcomes. Results There were 129 patients with SAE in the training cohort, and there were 103 and 59 patients in the two independent validation cohorts, respectively. Vasopressor use, procalcitonin (PCT), lactate and pediatric critical illness score (PCIS) were independent predictive factors for 14-day mortality, and vasopressor use, PCT, lactate, PCIS and albumin were independent predictive factors for 90-day mortality. Based on the variables, we generated two nomograms for the early identification of 14-day mortality (AUC 0.853, 95% CI 0.787–0.919, sensitivity 72.4%, specificity 84.5%) and 90-day mortality (AUC 0.857, 95% CI 0.792–0.923, sensitivity 72.3%, specificity 90.6%), respectively. The calibration plots for nomograms showed excellent agreement of mortality probabilities between the observed and predicted values in both training and validation cohorts. Decision curve analyses (DCA) indicated that nomograms conferred high clinical net benefit. Conclusion The nomograms in this study revealed optimal prognostic factors for the mortality of pediatric patients with SAE, and individualized quantitative risk evaluation by the models would be practical for treatment management.