Frailty Hinders Recovery From Acute Respiratory Illness in Older Adults
Notice bibliographique
Résumé
Influenza vaccination programs aim to prevent serious outcomes. Given that frailty may impact recovery from influenza, we examined frailty as a predictor of recovery in older adults hospitalized with acute respiratory illness. Data came from the Canadian Immunization Research Network (CIRN) Serious Outcomes Surveillance (SOS) Network during the 2011/12, 2012/13, and 2013/14 influenza seasons; all patients were aged 65+. Frailty was measured using a previously validated Frailty Index (FI) of health and functional deficits; baseline frailty was categorized using published cutoffs (0-.1 non-frail, >.1-.21 pre-frail, >.21-.45 frail, >.45 most frail). Recovery was operationalized as being alive 30 days post-discharge with less than two additional health/functional deficits (<=0.06 FI increase). Logistic regression was used to examine the change in odds of recovery for every 0.1 increase in baseline FI, controlling for age, sex, season, lab-confirmed influenza status, and seasonal influenza vaccination status. Of 5125 hospitalized older adults, 15% were non-frail, 39% pre-frail, 40% frail, and 6% most frail. 11% died, and poor recovery was experienced by 520/4544=11% of survivors. Poor recovery was inversely associated with baseline frailty (11% non-frail, 17% pre-frail, 28% frail, 38% most frail; P < .001). Frailty was associated with lower odds of recovery in all three seasons [2011/12 (OR=0.71; 95% CI 0.60–0.85), 2012/13 (OR=0.72; 0.66–0.78), 2013/14 (OR=0.76; 0.70–0.82)] though results varied by season, influenza status, and vaccination status. In 2011/12, frailty was associated with poor recovery in unvaccinated (OR=0.46. 95% CI=0.32–0.67) but not vaccinated older patients (OR=0.83, 95% CI=0.68–1.02). Increasing frailty was consistently associated with lower odds of recovery in older adults admitted with influenza and other acute respiratory illnesses; depending on seasonal factors, vaccination may offer some buffering of this impact. Understanding frailty and functional status is important, both because frailty is predictive of poor recovery and because persistence of new health/functional deficits is an adverse outcome with important implications for patients, families and health systems. M. K. Andrew, GSK: Grant Investigator, Research grant; Pfizer: Grant Investigator, Research grant; Sanofi-Pasteur: Grant Investigator, Research grant; J. McElhaney, GSK Vaccines: Scientific Advisor, Speaker honorarium; M. Elsherif, Canadian Institutes of Health Research: Investigator, Research grant; Public Health Agency of Canada: Investigator, Research grant; GSK: Investigator, Research grant; S. A. Halperin, GSK: Scientific Advisor, Consulting fee; GSK: Grant Investigator, Research grant; T. Hatchette, GSK: Grant Investigator, Grant recipient; Pfizer: Grant Investigator, Grant recipient; Abbvie: Speaker for a talk on biologics and risk of TB reactivation, Speaker honorarium; J. M. Langley, GSK: Investigator, Research grant; Canadian Institutes of Health Research: Investigator, Research grant; A. Mcgeer, Hoffman La Roche: Investigator, Research grant; GSK: Investigator, Research grant; sanofi pasteur: Investigator, Research grant; J. Powis, Merck: Grant Investigator, Research grant; GSK: Grant Investigator, Research grant; Roche: Grant Investigator, Research grant; Synthetic Biologicals: Investigator, Research grant; M. Semret, GSK: Investigator, Research grant; Pfizer: Investigator, Research grant; S. Trottier, Canadian Institutes of Health Research: Investigator, Research grant; L. Valiquette, GSK: Investigator, Research grant; S. McNeil, GSK: Contract Clinical Trials and Grant Investigator, Research grant; Merck: Contract Clinical Trials and Speaker’s Bureau, Speaker honorarium; Novartis: Contract Clinical Trials, No personal renumeration; sanofi pasteur: Contract Clinical Trials, No personal renumeration
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 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 ».