[article]
Titre : |
Le « Cognitive Assessment scale for Stroke Patients » (CASP) vs le MMSE et la MoCA : comparaison de la faisabilité chez des patients cérébro-lésés gauches vasculaires aphasiques |
Type de document : |
texte imprimé |
Auteurs : |
José Luis Barnay, Auteur |
Année de publication : |
2014 |
Article en page(s) : |
p.422 - p.435 |
Langues : |
Français (fre) |
Mots-clés : |
Comparaison Faisabilité CASP MMSE MoCA Aphasie |
Résumé : |
Introduction
L’aphasie post-AVC rend difficile l’évaluation des autres fonctions cognitives. Nous avons élaboré le CASP qui peut être passé sans utiliser le langage. Nous avons comparé les faisabilités du CASP, du Mini Mental State Examination (MMSE) et de la Montreal Cognitive Assessment (MoCA) chez des patients cérébro-lésés gauches vasculaires aphasiques.
Patients et méthodes
Tous les patients hospitalisés sur une période de quatre mois dans sept services de rééducation français pour premier AVC hémisphérique gauche récent avec aphasie ont été évalués grâce aux trois batteries. Nous avons estimé le pourcentage de patients chez qui la passation d’au moins un item de ces échelles était impossible et comparé leur temps de passation.
Résultats
Sur quarante-quatre patients inclus (64 ± 15 ans, 26 hommes), 8 (18 %) CASP étaient non réalisables, contre 16 (36 %) MMSE (p = 0,05) et 13 (30 %) MoCA (p = 0,21, NS). Tous les patients avec troubles isolés de l’expression ont pu bénéficier du CASP, aucun des autres batteries. Les temps de passation étaient était plus longs pour le CASP (13 ± 4 min) que pour le MMSE (8 ± 3 min, p < 10−6) et la MoCA (11 ± 5 min, p = 0,23, NS).
Conclusion
En termes de faisabilité, le CASP est mieux adapté aux patients aphasiques que le MMSE et la MoCA. |
Permalink : |
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in Annals of physical and rehabilitation medicine > Vol. 57, n°6-7 (August/September 2014) . - p.422 - p.435
[article] Le « Cognitive Assessment scale for Stroke Patients » (CASP) vs le MMSE et la MoCA : comparaison de la faisabilité chez des patients cérébro-lésés gauches vasculaires aphasiques [texte imprimé] / José Luis Barnay, Auteur . - 2014 . - p.422 - p.435. Langues : Français ( fre) in Annals of physical and rehabilitation medicine > Vol. 57, n°6-7 (August/September 2014) . - p.422 - p.435
Mots-clés : |
Comparaison Faisabilité CASP MMSE MoCA Aphasie |
Résumé : |
Introduction
L’aphasie post-AVC rend difficile l’évaluation des autres fonctions cognitives. Nous avons élaboré le CASP qui peut être passé sans utiliser le langage. Nous avons comparé les faisabilités du CASP, du Mini Mental State Examination (MMSE) et de la Montreal Cognitive Assessment (MoCA) chez des patients cérébro-lésés gauches vasculaires aphasiques.
Patients et méthodes
Tous les patients hospitalisés sur une période de quatre mois dans sept services de rééducation français pour premier AVC hémisphérique gauche récent avec aphasie ont été évalués grâce aux trois batteries. Nous avons estimé le pourcentage de patients chez qui la passation d’au moins un item de ces échelles était impossible et comparé leur temps de passation.
Résultats
Sur quarante-quatre patients inclus (64 ± 15 ans, 26 hommes), 8 (18 %) CASP étaient non réalisables, contre 16 (36 %) MMSE (p = 0,05) et 13 (30 %) MoCA (p = 0,21, NS). Tous les patients avec troubles isolés de l’expression ont pu bénéficier du CASP, aucun des autres batteries. Les temps de passation étaient était plus longs pour le CASP (13 ± 4 min) que pour le MMSE (8 ± 3 min, p < 10−6) et la MoCA (11 ± 5 min, p = 0,23, NS).
Conclusion
En termes de faisabilité, le CASP est mieux adapté aux patients aphasiques que le MMSE et la MoCA. |
Permalink : |
./index.php?lvl=notice_display&id=33392 |
| ![Le « Cognitive Assessment scale for Stroke Patients » (CASP) vs le MMSE et la MoCA : comparaison de la faisabilité chez des patients cérébro-lésés gauches vasculaires aphasiques 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) 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