Ignite Creation Date:
2025-12-25 @ 2:13 AM
Ignite Modification Date:
2025-12-26 @ 12:41 AM
Study NCT ID:
NCT06023160
Status:
COMPLETED
Last Update Posted:
2025-03-04
First Post:
2023-08-19
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Predicting Functional Outcome and Response to Therapy of Anti-NMDAR Encephalitis at Diagnosis
Sponsor:
Erasmus Medical Center
Organization:
Study Overview
Official Title:
Predicting Functional Outcome and Response to Therapy of Anti-NMDAR Encephalitis at Diagnosis: The NEOSII Score
Status:
COMPLETED
Status Verified Date:
2025-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
NEOSII
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.
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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: