Heart Failure after Laboratory Confirmed Influenza Infection (FLU-HF)
Bibliographic record
Abstract
Background: Influenza has been shown to exacerbate heart failure (HF). Importantly, no study to date has examined the relationship between HF hospitalizations (HFH) with laboratory confirmed influenza infections. This study evaluated the association between laboratory confirmed influenza infection and HFH in the two largest hospitals in Saskatchewan, Canada. Methods: We used a retrospective self-controlled case series design to evaluate the association between laboratory-confirmed influenza infection and HFH. We compared the incidence ratio for HFH during the influenza risk interval with the control interval. We defined the influenza risk interval as the seven days after a laboratory confirmed influenza result and the control interval as one year before and after the risk interval. Results: We identified 114 HFH that occurred within one year before and after a positive test result for influenza between April 1, 2010, and April 30, 2018. Of these, 28 (28 admissions per week) occurred during the risk interval and 86 (0.853 admissions per week) occurred during the control interval. The incidence ratio of a HFH during the risk interval as compared with the control interval was 33.53 (95% confidence interval [CI], 21.89 to 51.36). A decline in incidence was observed after day seven; between days 8 to 14 and 14 to 28 incidence ratios was 0.91 (95% CI, 0.13 to 6.52) and 0.91 (95% CI, 0.22 to 3.68) respectively. Conclusion: We have observed a significant association between acute influenza infection and HFH. However, further research with a larger sample size and involving a multicenter setting is warranted. Highlights: Influenza may contribute and exacerbate heart failure events especially during annual influenza season.Early identification of influenza among patients with heart failure, could lead to earlier treatment with antiviral medication, reduce unnecessary antibiotic use, and tail off the morbidity and mortality.In this study, despite our efficient study design, our sample size was limited to only the two largest hospitals in the province, possibly excluding a significant population in remote areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".