Titre :

Clinical and neuroimaging predictors of benzodiazepine response in catatonia: A machine learning approach.

Auteur(s) :

Badinier, Jane [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Lopes, Renaud [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Mastellari, Tomas [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Fovet, Thomas [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Williams, S. C. R. [Auteur]
Pruvo, Jean-Pierre [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172
Amad, Ali [Auteur]
Lille Neurosciences & Cognition (LilNCog) - U 1172

Titre de la revue :

Journal of Psychiatric Research

Nom court de la revue :

J Psychiatr Res

Numéro :

172

Pagination :

300-306

Date de publication :

2024-03-04

ISSN :

1879-1379

Discipline(s) HAL :

Sciences cognitives

Résumé en anglais : [en]

Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated ...
Lire la suite >Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated with benzodiazepine resistance remain unknown. We aimed to develop machine learning models using clinical and neuroimaging data to predict benzodiazepine response in catatonic patients. This study examined a cohort of catatonic patients who underwent standardized clinical evaluation, 3 T brain MRI, and benzodiazepine trial. Based on clinical response, patients were classified as benzodiazepine responders or non-responders. Cortical thickness and regional brain volumes were measured. Two machine learning models (linear model and gradient boosting tree model) were developed to identify predictors of treatment response using clinical, demographic, and neuroimaging data. The cohort included 65 catatonic patients, comprising 30 benzodiazepine responders and 35 non-responders. Using clinical data alone, the linear model achieved 63% precision, 51% recall, a specificity of 61%, and 58% AUC, while the gradient boosting tree (GBT) model attained 46% precision, 60% recall, a specificity of 62% and 64% AUC. Incorporating neuroimaging data improved model performance, with the linear model achieving 66% precision, 57% recall, a specificity of 67%, and 70% AUC, and the GBT model attaining 50% precision, 50% recall, a specificity of 62% and 70% AUC. The integration of imaging data with demographic and clinical information significantly enhanced the predictive performance of the models. The duration of the catatonic syndrome, along with the presence of mitgehen (passive obedience) and immobility/stupor, and the volume of the right medial orbito-frontal cortex emerged as important factors in predicting non-response to benzodiazepines.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
Inserm
CHU Lille

Collections :

• Lille Neurosciences & Cognition (LilNCog) - U 1172

Date de dépôt :

2024-03-07T22:01:01Z
2024-05-17T11:23:19Z