Post-stroke cognitive impairment: screening with MMSE and MoCA and predictors of their persistence after treatment at the Stroke Center
Notice bibliographique
Résumé
Objective — to analyze the results of screening for post‑stroke cognitive impairment (PCI) in patients with cerebral stroke (CS) admitted to the Stroke Center (SC) in different disease phases, and to determine independent predictors of the PCI persistence at discharge.
 Methods and subjects. 399 patients were enrolled, including 242 (60.7 %) men and 157 (39.3 %) women with the median age was 66.2 years (IQR 58.5 — 76.3). IS was diagnosed in 331 (82.9 %), and ICH in 68 (17.1 %) patients. Among patients with IS, 137 (41.4 %) had an atherothrombotic subtype, 152 (46.0 %) had a cardioembolic subtype, 21 (6.3 %) had a lacunar subtype, another 21 (6.3 %) had another or unknown cause of stroke. Patients were screened for PCI using the Mini‑Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) on admission and at discharge. Participants with MMSE score of 0 — 24 or a MoCA score of 0 — 25 were considered having PCI. Upon admission, all patients were assessed using the National Institutes of Health Stroke Scale (NIHSS), Bartel Index, and Modified Rankine Scale (mRS). The method of constructing and analyzing logistic regression models was used to determine independent predictors of the preservation of PCI at discharge. The analysis was carried out using the MedCalc v. 19.1.
 Results. The baseline NIHSS score ranged from 0 to 39 (median 11, IQR 6 — 18). The majority (64.2 %) of the subjects were hospitalized within the first 30 days from the CS onset. The MMSE score on admission ranged from 0 to 30 (median 20, IQR 2 — 27), and in 179 (44.9 %) of the patients the initial score was 0 to 17 (severe PCI), whereas in 61 (15 3 %) of the participants it was 18 to 24 (moderately severe PCI) and only 159 (39.8 %) persons scored 25 to 30 (no PCI). The baseline MoCA score ranged from 0 to 30 (median 15, IQR 1 — 24), and 356 (89.2 %) patients were shown to have PCI (score 0 to 25). According to screening with MMSE at discharge, 125 (31.4 %) patients had severe PCI, and 67 (16.8 %) had moderately severe PCI. The MoCA assessment before discharge indicated PCI in 324 (81.2 %) patients. According to both MMSE and MoCA, the rate of PCI on admission was significantly higher than at discharge (p < 0.001). Among the 240 patients who had PCI according to MMSE score, 239 (99.6 %) had PCI according to the MoCA score. However, among 159 patients who screened negative for PCI with MMSE at admission, 117 (73.6 %) screened positive with MoCA. Screening results using both MMSE and MoCA were not significantly associated with affected hemisphere. ICH was associated with lower (p < 0.0001) MMSE and MoCA scores compared with IS. Predictors of PCI according to MMSE score at discharge were a longer time interval from CS onset to SC admission, and a lower baseline MMSE score. However, with MoCA, the predictors were AT subtype IS, lesions in the distribution of the right or both middle cerebral arteries, older patient age, and a lower baseline MoCA score.
 Conclusions. In patients with MI, a high rate of PCI was documented on admission, but was significantly lower at discharge. In patients with established PCI, according to MMSE score, the use of MoCA for screening seems useless, however, screening with MoCA identified PCI in 3/4 in patients with a normal MMSE score. The independent predictors of scores on these two scales, indicating PCI, were significantly different, so they should not be considered interchangeable.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».