The Accuracy of Screening for Post-stroke Cognitive Impairment Assessment Tools: a Meta-analysis
Notice bibliographique
Résumé
Background Post-stroke cognitive impairment (PSCI) brings a heavy burden to patients and their families. An early recognition and intervention can help delay the occurrence and development of PSCI. Therefore, the use of accurate neuropsychological assessment tools to screen for PSCI is essential for the management and treatment of PSCI. Objective To analyze the screening accuracy of assessment tools for PSCI by meta-analysis, thus providing references for an accurate screening of PSCI. Methods Diagnostic trials on screening tools of PSCI published from the establishment of the database to December 2022 were searched in CNKI, VIP, Wanfang Data, SinoMed, PubMed, Embase, Web of Science, Cochrane Library. Two researchers respectively screened literatures, extracted data, and assessed the risk of bias. Stata 17.0 software was used to analyze the data. Results A total of 57 articles were included, involving 7 assessment tools [the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network 5-Minute Battery (NINDS-CSN 5-Minutes), the Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), the Addenbrooke's Cognitive Examination-Revised (ACE-R), the Telephone Interview for Cognitive Status Modified (TICS-m) and the Montreal Cognitive Assessment 5-minute protocol (MoCA-5 min) ] to screen 12 113 patients. Meta-analysis results showed that the combined sensitivity and specificity of MoCA in screening PSCI were 0.84 (95%CI=0.80-0.87) and 0.74 (95%CI=0.67-0.80), respectively, with a combined area under the curve (AUC) of 0.87 (95%CI=0.84-0.90). The combined sensitivity and specificity of MMSE in screening PSCI were 0.73 (95%CI=0.67-0.79) and 0.76 (95%CI=0.69-0.82), respectively, with a combined AUC of 0.81 (95%CI=0.77-0.84). The combined sensitivity and specificity of IQCODE in screening PSCI were 0.73 (95%CI=0.48-0.89) and 0.95 (95%CI=0.75-0.99), respectively, with a combined AUC of 0.91 (95%CI=0.88-0.93). The combined sensitivity and specificity of the NINDS-CSN 5-min in screening PSCI were 0.83 (95%CI=0.78-0.87) and 0.69 (95%CI=0.60-0.76), respectively, with a combined AUC of 0.85 (95%CI=0.81-0.88). The combined sensitivity and specificity of the ACE-R in screening PSCI were 0.90 (95%CI=0.80-0.95) and 0.61 (95%CI=0.19-0.91), respectively, with a combined AUC of 0.90 (95%CI=0.87-0.92). The combined sensitivity and specificity of TICS-m in screening PSCI were 0.84 (95%CI=0.75-0.91) and 0.67 (95%CI=0.61-0.74), respectively, with a combined AUC of 0.66 (95%CI=0.60-0.71) . Conclusion The combined AUC of IQCODE and ACE-R is larger, and the former as a higher combined specificity and the latter has a higher combined sensitivity. Therefore, IQCODE and ACE-R are optimal assessment tools to accurately screen PSCI. Due to the limited number of literatures reporting the IQCODE and ACE-R in screening PSCI, our conclusions still need to be validated by multicenter and large-sample studies.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».