Estimated prevalence of post-intensive care cognitive impairment at short-term and long-term follow-ups: a proportional meta-analysis of observational studies
Notice bibliographique
Résumé
OBJECTIVE: Evidence of the overall estimated prevalence of post-intensive care cognitive impairment among critically ill survivors discharged from intensive care units at short-term and long-term follow-ups is lacking. This study aimed to estimate the prevalence of the post-intensive care cognitive impairment at time to < 1 month, 1 to 3 month(s), 4 to 6 months, 7-12 months, and > 12 months discharged from intensive care units. METHODS: Electronic databases including PubMed, Cochrane Library, EMBASE, CINAHL Plus, Web of Science, and PsycINFO via ProQuest were searched from inception through July 2024. Studies that reported on cognitive impairment among patients discharged from intensive care units with valid measures were included. Data extraction and risk of bias assessment were performed independently for all included studies according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines. Newcastle-Ottawa Scale was used to measure risk of bias. Data on cognitive impairment prevalence were pooled using a random-effects model. The primary outcome was pooled estimated proportions of prevalence of the post-intensive care cognitive impairment. RESULTS: In total, 58 studies involving 347,940 patients were included. The pooled post-intensive care cognitive impairment prevalence rates at the follow-up timepoints < 1 month, 1-3 month(s), 4-6 months, 7-12 months, > 12 months were 49.8% [95% Prediction Interval (PI), 39.9%-59.7%, n = 19], 45.1% (95% PI, 34.8%-55.5%, n = 23), 47.9% (95% PI, 35.9%-60.0%, n = 16), 28.3% (95% PI, 19.9%-37.6%, n = 19), and 30.4% (95% PI, 18.4%-43.9%, n = 7), respectively. Subgroup analysis showed that significant differences of the prevalence rates between continents and study designs were observed. CONCLUSIONS: The prevalence rates of post-intensive care cognitive impairment differed at different follow-up timepoints. The rates were highest within the first three months of follow-up, with a pooled prevalence of 49.8% at less than one month, 45.1% at one to three months, and 47.9% at three to six months. No significant differences in prevalence rates between studies that only included coronavirus disease 2019 survivors. These fundings highlight the need for further research to develop targeted interventions to prevent or manage cognitive impairment at short-term and long-term follow-ups.
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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 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,010 | 0,006 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».