Mortality Risk Prediction Models for People With Kidney Failure
Notice bibliographique
Résumé
Importance: People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer. Objective: To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice. Evidence Review: MEDLINE, Embase, and the Cochrane Library were searched for studies published between January 1, 2004, and September 30, 2024. Studies were included if they created or evaluated mortality prediction models for people who developed kidney failure, whether treated or not treated with kidney replacement with hemodialysis or peritoneal dialysis. Studies including exclusively kidney transplant recipients were excluded. Two reviewers independently extracted data and graded each study at low, high, or unclear risk of bias and applicability using recommended checklists and tools. Reviewers used the Prediction Model Risk of Bias Assessment Tool and followed prespecified questions about study design, prediction framework, modeling algorithm, performance evaluation, and model deployment. Analyses were completed between January and October 2024. Findings: A total of 7184 unique abstracts were screened for eligibility. Of these, 77 were selected for full-text review, and 50 studies that created all-cause mortality prediction models were included, with 2 963 157 total participants, who had a median (range) age of 64 (52-81) years. Studies had a median (range) proportion of women of 42% (2%-54%). Included studies were at high risk of bias due to inadequate selection of study population (27 studies [54%]), shortcomings in methods of measurement of predictors (15 [30%]) and outcome (12 [24%]), and flaws in the analysis strategy (50 [100%]). Concerns for applicability were also high, as study participants (31 [62%]), predictors (17 [34%]), and outcome (5 [10%]) did not fit the intended target clinical setting. One study (2%) reported decision curve analysis, and 15 (30%) included a tool to enhance model usability. Conclusions and Relevance: According to this systematic review of 50 studies, published mortality prediction models were at high risk of bias and had applicability concerns for clinical practice. New mortality prediction models are needed to inform treatment decisions in people with kidney failure.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 ».