Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study - Zhao, Lina; Li, Yun; Wang, Yunying; Gao, Qian; Ge, Zengzheng; Sun, Xibo; Li, Yi (Frontiers in Microbiology, 2021)
Background: Hospital mortality risk is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system is called sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, and there is no clinical tool to predict hospital mortality for SAE patients. This study aimed to explore the relationship between pathogenic microorganisms and the prognosis of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients. Methods: The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. The predictive model was developed by multivariable Cox regression analysis. Calibration and discrimination were performed to assess the performance of the nomogram. The clinical utility of this nomogram was assessed by decision curve analysis. Results: Unfortunately, the results of our study did not find microbes and infection sites that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate (lac), international normalized ratio (INR), albumin, SpO2, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of the nomogram (0.812) was more than that of the Simplified Acute Physiology Score (SAPS II) (0.745), indicating excellent discrimination. A decision curve analysis (DCA) demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality. Conclusion: We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.