Investigating Risks Associated with Influenza Infection in Patients with or at Risk of Cardiovascular Disease Using Epidemiological Techniques
Bibliographic record
Abstract
Cardiovascular disease (CVD) and influenza were the 2nd and 6th leading causes of death in Canada in the pre-pandemic era. Influenza contributes to significant cardiopulmonary morbidity and mortality, particularly in patients with CVD, which may be prevented with influenza vaccines. Yet, many high-risk patients with CVD and other underlying medical conditions demonstrate a reduced response to vaccines, which decreases effectiveness for preventing adverse events. High-dose influenza vaccines, typically reserved for older adults with weaker immune systems, may augment the immune response in patients with CVD and further reduce morbidity and death. The overall objective of this thesis is to leverage real-world routinely collected and pragmatic trial data to study the preventative benefit of influenza vaccination and the determinants and burden of illness post-viral respiratory infection in patients with underlying medical conditions, focusing on CVD. Aim 1 (Chapter 2) is a systematic review and meta-analysis of all randomized controlled trials evaluating standard seasonal influenza vaccine against placebo or standard care in patients with varying phenotypes of CVD and CV risk, to evaluate the possible cardioprotective benefits of influenza vaccination. Aim 2 (Chapter 3) is a recurrent events analysis of the INVESTED trial to identify predictors of first and recurrent cardiopulmonary hospitalizations and death in a contemporary cohort of high-risk CV patients, as well as whether the high-dose versus the standard-dose influenza vaccine is associated with fewer recurrent major adverse CV events. Finally, Aim 3 (Chapter 4) is a population-based, test-positive retrospective cohort study to estimate and compare the burden of adverse outcomes, including CV hospitalization, in the acute and post-acute phases of influenza infection by care setting, over five pre-pandemic influenza seasons. In summary, these three aims – all novel contributions to the literature - will provide added insights into viral respiratory infections like influenza by exploring the burden experienced by patients with highly prevalent underlying medical conditions like CVD. These patients represent an important target population for the rapidly developing field of viral vaccines, and learnings from them are applicable to many other underlying medical conditions that are susceptible to the same underlying pathophysiologic immune-modulation and frailty mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".