Development and validation of a nomogram to provide individualized predictions of functional outcomes in patients with convulsive status epilepticus at 3 months: The modified END-IT tool.

CNS neuroscience & therapeutics(2023)

Cited 1|Views5
No score
Abstract
AIMS:The prediction of outcomes in convulsive status epilepticus (CSE) remains a constant challenge. The Encephalitis-Nonconvulsive Status Epilepticus-Diazepam Resistance-Image Abnormalities-Tracheal Intubation (END-IT) score was a useful tool for predicting the functional outcomes of CSE patients, excluding cerebral hypoxia patients. With further understanding of CSE, and in view of the deficiencies of END-IT itself, we consider it necessary to modify the prediction tool. METHODS:The prediction model was designed from a cohort of CSE patients from Xijing Hospital (China), between 2008 and 2020. The enrolled subjects were randomly divided into training cohort and validation cohort as a ratio of 2:1. The logistic regression analysis was performed to identify the predictors and construct the nomogram. The performance of the nomogram was assessed by calculating the concordance index, and creating calibration plots to check the consistency between the predicted probabilities of poor prognosis and the actual outcomes of CSE. RESULTS:The training cohort included 131 patients and validation cohort included 66 patients. Variables included in the nomogram were age, etiology of CSE, non-convulsive SE, mechanical ventilation, abnormal albumin level at CSE onset. The concordance index of the nomogram in the training and validation cohorts was 0.853 (95% CI, 0.787-0.920) and 0.806 (95% CI, 0.683-0.923), respectively. The calibration plots showed an adequate consistency between the reported and predicted unfavorable outcomes of patients with CSE at 3 months after discharge. CONCLUSIONS:A nomogram for predicting the individualized risks of poor functional outcomes in CSE was constructed and validated, which has been an important modification of END-IT score.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined