Frailty Hinders Recovery From Acute Respiratory Illness in Older Adults
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".