Analgesic Use in Patients With Advanced Chronic Kidney Disease: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
BACKGROUND: Pain is common in patients with chronic kidney disease (CKD). Analgesics may be appropriate for some CKD patients. OBJECTIVES: To determine the prevalence of overall analgesic use and the use of different types of analgesics including acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), adjuvants, and opioids in patients with CKD. DESIGN: Systematic review and meta-analysis. SETTING: Interventional and observational studies presenting data from 2000 or later. Exclusion criteria included acute kidney injury or studies that limited the study population to a specific cause, symptom, and/or comorbidity. PATIENTS: Adults with stage 3-5 CKD including dialysis patients and those managed conservatively without dialysis. MEASUREMENTS: Data extracted included title, first author, design, country, year of data collection, publication year, mean age, stage of CKD, prevalence of analgesic use, and the types of analgesics prescribed. METHODS: statistic was computed to measure heterogeneity. Random-effects models were used to account for variations in study design and sample populations, and a double arcsine transformation of the prevalence variables was used to accommodate potential overweighting of studies with very large or very small prevalence measurements. Sensitivity analyses were performed to determine the magnitude of publication bias and assess possible sources of heterogeneity. RESULTS: Forty studies were included in the analysis. The prevalence of overall analgesic use in the random-effects model was 50.8%. The prevalence of acetaminophen, NSAIDs, and adjuvant use was 27.5%, 17.2%, and 23.4%, respectively, while the prevalence of opioid use was 23.8%. Due to the possibility of publication bias, the actual prevalence of acetaminophen use in patients with advanced CKD may be substantially lower than this meta-analysis indicates. A trim-and-fill analysis decreased the pooled prevalence estimate of acetaminophen use to 5.4%. The prevalence rate for opioid use was highly influenced by 2 large US studies. When these were removed, the estimated prevalence decreased to 17.3%. LIMITATIONS: There was a lack of detailed information regarding the analgesic regimen (such as specific analgesics used within each class and inconsistent accounting for patients on multiple drugs and the use of over-the-counter analgesics such as acetaminophen and NSAIDs), patient characteristics, type of pain being treated, and the outcomes of treatment. Data on adjuvant use were very limited. These results, therefore, must be interpreted with caution. CONCLUSIONS: There was tremendous variability in the prescribing patterns of both nonopioid and opioid analgesics within and between countries suggesting widespread uncertainty about the optimal pharmacological approach to treating pain. Further research that incorporates robust reporting of analgesic regimens and links prescribing patterns to clinical outcomes is needed to guide optimal clinical practice.
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 | 0,012 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,036 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».