Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 2:13 AM
Ignite Modification Date:
2025-12-25 @ 2:13 AM
NCT ID:
NCT06023160
Brief Summary:
The goal of this international cohort study is to develop a prediction model for long-term outcome and response to first-line immunotherapy of anti-NMDAR Encephalitis, already at the moment of diagnosis.
Detailed Description:
Anti-NMDARE is a severe, but treatable neurological condition, with considerable and variable long-term disability. The previously developed anti-NMDAR Encephalitis One-Year Functional Status (NEOS) score predicts outcome a month into treatment. To predict outcome and response to immunotherapy at the time of diagnosis would be a serious improvement. This would timely identify patients in need for aggressive treatment and avoid harmful side-effects in those with good outcome. International data from five anti-NMDAR encephalitis cohorts will be combined to attain these goals.
The investigators strive to have less than 10% missing data on all variables and will impute data were needed. The datasets will then be split - with equal distributions of cohorts and good/poor outcome - to develop (70%) and validate (30%) the NEOS2 model. The primary outcome is functioning one year after diagnosis. A secondary analysis is targeted to predict the effect of first-line therapy. Potentially relevant predictive variables are identified with a univariable analysis on the original data, confirmed with backwards selection on the imputed datasets. After checking for multicollinearity and linearity of the variables, identified variables are added to a mixed effects logistic regression model on the original and imputed datasets, to identify the final set of predictive variables. To make the models opportune for daily medical practice, the investigators will assign points to (categories of) the included variables, based on the coefficients.
Study:
NCT06023160
Study Brief:
Protocol Section:
NCT06023160