Association of Surrogate Decision-making Interventions for Critically Ill Adults With Patient, Family, and Resource Use Outcomes
Notice bibliographique
Résumé
Importance: Physicians often rely on surrogate decision-makers (SDMs) to make important decisions on behalf of critically ill patients during times of incapacity. It is uncertain whether targeted interventions to improve surrogate decision-making in the intensive care unit (ICU) reduce nonbeneficial treatment and improve SDM comprehension, satisfaction, and psychological morbidity. Objective: To perform a systematic review and meta-analysis of randomized clinical trials (RCTs) to determine the association of such interventions with patient- and family-centered outcomes and resource use. Data Sources: A search was conducted of MEDLINE, Embase, and other relevant databases for potentially relevant studies from inception through May 30, 2018. Study Selection: Randomized clinical trials studying interventions that were targeted at SDMs or family members of critically ill adults in the ICU were included. Key search terms included surrogate or substitute decision-maker, critically ill, randomized controlled trials, and their respective related terms. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Two independent, blinded reviewers independently screened citations and extracted data. Random effects models with inverse variance weighting were used to pool outcomes data when possible and otherwise present findings qualitatively. Main Outcomes and Measures: Outcomes of interest were divided into 3 categories: (1) patient-related clinical outcomes (mortality, length of stay [LOS], duration of life-sustaining therapies), (2) SDM and family-related outcomes (comprehension, major change in goals of care, incident psychological comorbidities [posttraumatic stress disorder, anxiety, depression], and satisfaction with care), and (3) use of resources (cost of care and health care resource use). Results: Of 3735 studies screened, 13 RCTs were included, comprising a total of 10 453 patients. Interventions were categorized as health care professional led (n = 6), ethics consultation (n = 3), palliative care consultation (n = 2), and media (n = 1 pamphlet and 1 video). No association with mortality was observed (risk ratio, 1.03; 95% CI, 0.98-1.08; P = .22). Intensive care unit LOS was significantly shorter among patients who died (mean difference, -2.11 days; 95% CI, -4.16 to -0.07; P = .04), but not in the overall population (mean difference, -0.79 days; 95% CI, -2.33 to 0.76 days; P = .32). There was no consistent difference in SDM-related outcomes, including satisfaction with care or perceived quality of care (n = 6 studies) and incident psychological comorbidities (depression: ratio of means, -0.11; 95% CI, -0.29 to 0.08; P = .26; anxiety: ratio of means, -0.08; 95% CI, -0.25 to 0.08; P = .31; or posttraumatic stress disorder: ratio of means: -0.04; 95% CI, -0.21 to 0.13; P = .65). Among 6 trials reporting effects on health care resource use, only 1 nurse-led intervention observed a significant reduction in costs ($75 850 control vs $51 060 intervention; P = .04). Conclusions and Relevance: Systematic interventions aimed at improving surrogate decision-making for critically ill adults may reduce ICU LOS among patients who die in the ICU, without influencing overall mortality. Better understanding of the complex processes related to surrogate decision-making is needed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,046 | 0,197 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,013 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 source (Gemma direct ou Codex distillé), 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 ».