Risk of cardiovascular events after influenza infection-related hospitalizations in adults with congenital heart disease: A nationwide population based study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular complications due to viral infection pose a significant risk in vulnerable patients such as those with congenital heart disease (CHD). Limited data exists regarding the incidence of influenza and its impact on cardiovascular outcomes among this specific patient population. METHODS: A retrospective cohort study was designed using the Canadian Congenital Heart Disease (CanCHD) database-a pan-Canadian database of CHD patients with up to 35 years of follow-up. CHD patients aged 40 to 65 years with influenza virus-associated hospitalizations between 2010 and 2017 were identified and 1:1 matched with CHD patients with limb fracture hospitalizations on age and calendar time. Our primary endpoint was cardiovascular complications: heart failure, acute myocardial infarction, atrial arrhythmia, ventricular arrhythmia, heart block, myocarditis, and pericarditis. RESULTS: Of the 303 patients identified with incident influenza virus-associated hospitalizations, 255 were matched to 255 patients with limb fracture hospitalizations. Patients with influenza virus-related hospitalizations showed significantly higher cumulative probability of cardiovascular complications at 1 year (0.16 vs. 0.03) and 5 years (0.33 vs. 0.15) compared to patients hospitalized with bone fracture. Time-dependent hazard function modeling demonstrated a significantly higher risk of cardiovascular complications within 9 months postdischarge for influenza-related hospitalizations. This association was confirmed by Cox regression model (average hazard ratio throughout follow-up: 2.48; 95% CI: 1.59-3.84). CONCLUSIONS: This pan-Canadian cohort study of adults with CHD demonstrated an association between influenza virus-related hospitalization and risk of cardiovascular complications during the 9 months post discharge. This data is essential in planning surveillance strategies to mitigate adverse outcomes and provides insights into interpreting complication rates of other emerging pathogens, such as COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".