Ignite Creation Date:
2024-05-06 @ 7:29 PM
Last Modification Date:
2024-10-26 @ 3:07 PM
Study NCT ID:
NCT06023160
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-02-16
First Post:
2023-08-19
Brief Title:
Predicting Functional Outcome and Response to Therapy of Anti-NMDAR Encephalitis at Diagnosis
Sponsor:
Erasmus Medical Center
Organization:
Erasmus Medical Center
Study Overview
Official Title:
Predicting Functional Outcome and Response to Therapy of Anti-NMDAR Encephalitis at Diagnosis The NEOSII Score
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-02
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 goodpoor 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 goodpoor 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:
None
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?:
None