Comparing Frailty Assessment Methods and Their Ability to Predict Adverse Outcomes in Patients with Advanced CKD
Notice bibliographique
Résumé
Key Points Most measures of frailty, regardless of definition used, were associated with higher risk of mortality, hospitalizations, and emergency room visits. Claims-based definitions had poor agreement when compared with objective and subjective measures of frailty in the advanced CKD population. Administrative definitions require further development to accurately identify frail patients in the advanced CKD population. Background Frailty is common in patients with CKD, and those affected by both are at increased risk of adverse outcomes including disability, hospitalization, and death. Collecting data on frailty as part of clinical care could enhance care by identifying patients at risk of adverse events. However, clinical assessment of frailty requires time and resources. Frailty definitions based on administrative data might provide an efficient alternative. The primary objective was to compare agreement between administrative claims-based definitions versus objectively measured frailty in adults with advanced, nondialysis CKD and to examine their associations with adverse outcomes. Methods The cohort consisted of Manitoba participants from the Canadian Frailty Observation and Interventions Trial. This multicenter cohort study followed adults with advanced CKD longitudinally. Every visit, assessments were conducted to determine frailty status using the Fried Frailty Phenotype, Short Physical Performance Battery, and health care providers' impression. The Canadian Frailty Observation and Interventions Trial database was linked to administrative databases at the Manitoba Centre for Health Policy to calculate two claims-based frailty indicators, the Segal and modified preoperative frailty indices, which have been validated in the non-CKD literature. Results Of the 442 participants included, the mean age was 66±14 years and 58% were male; 88% had hypertension, 61% dyslipidemia, and 58% diabetes. The prevalence of frailty varied from 19% to 70% depending on definition. Agreement between frailty definitions was poor ( κ 0.09–0.33); however, individuals considered frail using both administrative or measured definitions had a higher risk of all-cause mortality and hospitalization, except for those identified by the Segal Frailty Indicator. Conclusions This study suggests that those identified as frail by nearly all measures were at higher risk of adverse outcomes. Thus, most frailty models in this study can be used to identify high risk advanced nondialysis CKD populations, allowing us to target individuals for interventions that aim to improve outcomes.
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 | 0,010 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».