Association Between the Frailty Index Based on Laboratory Tests and All-Cause Mortality in Hospitalized Older Adults: Retrospective Cohort Study
Notice bibliographique
Résumé
Background: Frailty is a common issue among hospitalized older adult patients and is associated with numerous adverse health outcomes. Assessing frailty facilitates better decision-making for treatment plans, patient placement, and discharge planning. Approximately a decade ago, the frailty index based on laboratory tests (FI-Lab) metric was introduced. Although this index has been shown in numerous studies to predict adverse medical outcomes, including mortality, it has not been extensively evaluated among patients hospitalized in internal medicine departments for diverse indications. Objective: The aim of the study was to investigate the relationship between FI-Lab at admission and all-cause mortality during hospitalization and after discharge in patients aged 65 years and older admitted for diverse clinical indications to internal medicine departments. Methods: This retrospective cohort study included patients aged 65 years and older hospitalized in the internal medicine departments of a large tertiary hospital. Data included demographic variables, comorbidity, and all-cause mortality. The FI-Lab was calculated based on 16 available blood tests, as well as blood pressure and heart rate measurements. We used Cox proportional hazards regression models to evaluate associations with mortality. Model performance was assessed using the C-index and time-dependent receiver operating characteristic (ROC) curves. Hospitalization data were collected from December 25, 2016, to January 7, 2023. Results: During the study period, 31,443 patients were hospitalized in internal medicine departments, and FI-Lab was calculable for 31,398 of them. The mean age of the patients was 77.6 (SD 8.2) years, and 52.1% (16,346/31,443) were women. The mean FI-Lab score was 0.38 (SD 0.15). Based on FI-Lab scores, patients were categorized into 4 groups: robust, mildly prefrail, moderately prefrail, and frail. After adjusting for age, sex, and comorbidities, frail and prefrail patients exhibited higher mortality rates than robust patients. For each 0.01 increase in the FI-Lab score (as a continuous variable), adjusted analyses revealed a 5.5% increase in in-hospital mortality, a 2.9% increase in mortality within the first year after hospitalization, and a 1.9% increase in mortality beyond the first year. Conclusions: The FI-Lab is a readily available and informative metric of frailty in older hospitalized patients. Calculating this index can assist physicians with identifying patients at high risk of mortality and provide meaningful information to support clinical decision-making.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».