Impact of Frailty on Influenza Vaccine Effectiveness and Clinical Outcomes: Experience From the Canadian Immunization Research Network (CIRN) Serious Outcomes Surveillance (SOS) Network 2011/12 Season
Notice bibliographique
Résumé
Background. Health impact of influenza is traditionally considered only in acute terms. There is increasing evidence that influenza may have lasting health implications, particularly for frail older adults. We studied vaccine effectiveness (VE) and outcomes of influenza-related hospitalization in relation to frailty & functional status. Methods. The SOS Network conducted active surveillance for influenza in Canadian hospitals for the 2011/12 influenza season. VE for prevention of influenza-hospitalization was assessed using a matched test-negative case-control analysis. Special attention was paid to frailty and functional status of patients ≥65 years at baseline (2 weeks prior to onset of symptoms) and follow up (30 days post-discharge). Admission swabs were tested by PCR to identify influenza cases (positive) and controls (negative). VE was calculated as 1 minus the odds ratio of vaccination in cases versus controls × 100. VE estimates were adjusted using conditional multivariate logistic regression with age, antiviral use, frailty and a stepwise backward selection of covariates with p < 0.1 by univariate analysis. Frailty was assessed using a validated 39-item frailty index (FI) and function was assessed using the Barthel Index (BI). Results. SOS enrolled 320 cases and 564 controls. Unadjusted VE for patients ≥65 years against influenza-hospitalization due to any strain was 45.0% (95% CI: 25.7–59.3); adjusted VE was 58.0% (95% CI: 34.2–73.2). Adjusting for frailty on top of fixed covariates alone very closely approximated the final fully adjusted model. On average, all older adults experienced functional loss during hospitalization. A total of 15.1% experienced persistent catastrophic disability (≥20 point decline on the BI between baseline and follow up); older patients with influenza were more likely to experience this decline than controls in the same age segment (p = 0.047). Conclusion. VE was moderate for prevention of influenza-related hospitalization in elderly people. Not accounting for frailty may underestimate VE due to a frailty bias; frailty is the most important confounder to take into account in adults 65+. Persistent functional decline is an important adverse outcome of influenza-related hospitalization and reducing this burden represents an important public health goal. Disclosures. M. K. Andrew, GSK: Investigator, Research support; G. Boivin, Biocryst: Investigator, Research grant. Merck: Investigator, Research grant; W. Bowie, GSK: Investigator, Research grant; A. Chit, Sanofi Pasteur: Employee, Salary; G. Dos Santos, Business and Decision Life Sciences: Consultant, Salary; T. Hatchette, GSK: Investigator, Research grant; F. Haguinet, GSK Vaccines: Employee, Salary; S. A. Halperin, GSK: Consultant, Grant Investigator and Research Contractor, Consulting fee and Grant recipient; B. Ibarguchi, GSK: Employee, Salary; P. Lagace-Wiens, Merck: Scientific Advisor, Consulting fee and Speaker honorarium; J. M. Langley, Sanofi Pasteur: Investigator, Research grant. GSK: Investigator, Research grant. PREVENT: Investigator, Research grant; A. E. Mccarthy, GSK: Investigator, Research support; J. E. Mcelhaney, GSK: Scientific Advisor, Research support and Speaker honorarium. Sanofi Pasteur: Scientific Advisor, Speaker honorarium; A. Poirier, Actelion: Investigator, Research grant. Genetech: Investigator, Research grant. Sanofi Pasteur: Investigator, Research grant. Vertex: Investigator, Research grant; J. Powis, GSK: Investigator, Research support; V. Shinde, GSK: Employee, Salary; S. A. Mcneil, GSK: Grant Investigator, Research grant and Research support. Pfizer: Grant Investigator, Consulting fee, Research grant, Research support and Speaker honorarium. Merck: Consultant and Investigator, Consulting fee, Research support and Speaker honorarium
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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 | 0,014 | 0,009 |
| 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 | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| 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 ».