A novel nomogram for early prediction of death in severe neurological disease patients with electroencephalographic periodic discharges. Issue 6 (June 2021) - Li, Feng; Huang, Lihong; Yan, Yin; Wang, Xuefeng; Hu, Yida
Highlights: It is important to evaluate the early prognosis of patients with periodic discharges. The prediction model based on the risk factors for death has high accuracy. EEG feature classification is convenient for clinical application and verification. Abstract: Objective: To investigate death-related factors in patients with electroencephalographic (EEG) periodic discharges (PDs) and to construct a model for death prediction. Methods: This case-control study enrolled a total of 80 severe neurological disease patients with EEG PDs within 72 h of admission to the neuroscience intensive care unit (NICU). According to modified Rankin scale (mRS) scores half a year after discharge, patients were divided into a survival group (<6 points) and a death group (6 points). Their relevant clinical and biochemical indicators as well as EEG characteristics were retrospectively analyzed. Logistic regression analysis was used to identify the risk factors associated with the death of patients with EEG PDs. A death risk prediction model and an individualized nomogram prediction model were constructed, and the prediction performance and concordance of the models were evaluated. Results: Multivariate logistic regression analysis showed that the involvement of both gray and white matter in imaging, disappearance of EEG reactivity, occurrence of stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and an interval time of 0.5–4 s were independent risk factors for death. A regression model was established according to the multivariate logistic regression analysis, and the area under the curve of this model was 0.9135. The accuracy of the model was 87.01%, the sensitivity was 87.17%, and the specificity was 89.17%. A nomogram model was constructed, and a concordance index of 0.914 was obtained after internal validation. Conclusion: The regression model based on risk factors has high accuracy in predicting the risk of death of patients with EEG PDs. Significance: This model can help clinicians in the early assessment of the prognosis of severe neurological disease patients with EEG PDs.