Risk factors and a predictive model for the occurrence of adverse outcomes in patients with new-onset refractory status epilepsy.

Abstract

To determine risk factors for the occurrence of adverse outcomes in patients with new-onset refractory status epilepsy (NORSE) and to construct a concomitant nomogram. Seventy-six adult patients with NORSE who were admitted to the Department of Neurology, First Affiliated Hospital of Sun Yat-sen University between January 2016 and December 2022 were enrolled for the study. Participants were divided into two-those with good and poor functional outcomes-and their pertinent data was obtained from the hospital medical recording system. Univariate analysis was used to identify potential causes of poor outcomes in both groups and a multivariate logistic regression model was used to identify risk factors for the occurrence of poor outcomes. Using the R programming language RMS package, a nomogram was created to predict the occurrence of poor outcomes. The NORSE risk of adverse outcome nomogram model included four predictors, namely duration of mechanical ventilation (OR = 4.370, 95% CI 1.221-15.640, p = 0.023), antiviral therapy (OR = 0.045, 95% CI 0.005-0.399, p = 0.005), number of anesthetics (OR = 13.428, 95% CI 2.16-83.48, p = 0.005) and neutrophil count/lymphocyte count ratio (NLR) (OR = 5.248, 95% CI 1.509-18.252, p = 0.009). The nomogram had good consistency and discrimination in predicting risk and can thus assist clinical care providers to assess outcomes for NORSE patients. Through ordinary bootstrap analyses, the results of the original set prediction were confirmed as consistent with those of the test set. The nomogram model of risk of adverse outcomes in NORSE adult patients developed in this study can facilitate clinicians to predict the risk of adverse outcomes in NORSE patients and make timely and reasonable interventions for patients at high risk of adverse outcomes.