The Risk of Adverse Cardiac Events after Pneumonia in Patients with Coronary Artery Disease
Bibliographic record
Abstract
Abstract Rationale Pneumonia triggers an inflammatory response that can persist even after the infection is resolved. This may further increase the risk of major adverse cardiac events (MACE) in individuals with known coronary artery disease (CAD), though this remains unclear. Objectives We aimed to assess the impact of pneumonia on MACE in individuals with existing CAD. Methods We identified patients who had coronary artery revascularization procedures in seven major hospitals in Western Australia between 2000 and 2005. Multivariable Cox regression models assessed the association between time-dependent pneumonia and MACE (composite of all-cause death + myocardial infarction + unstable angina + ischemic stroke + heart failure) and component outcomes separately, over 30 days, 1 year, and full follow-up. Results There were 14,425 patients in the study cohort (mean age, 64.4 yr; 23.6% female). Over a maximum of 13 years of follow-up, 988 patients experienced one or more pneumonia hospitalization. The risk of MACE increased over time, with adjusted hazard ratios (aHRs) of 4.91 (95% confidence interval [CI], 1.21–20.00) and 4.91 (95% CI, 2.62–9.19) over 30-day and 1-year intervals, respectively, and an aHR of 11.41 (95% CI, 9.22–14.11) over the entire follow-up. Myocardial infarction risk was highest during the first 30 days (aHR, 11.34) and reduced over the 1-year interval and the remainder of follow-up (aHR, 2.27 and 2.63, respectively). Risk of heart failure and cardiovascular death were also high over the entire follow-up period (aHR, 10.39 and 12.25, respectively). Conclusions Pneumonia hospitalization is associated with a significantly increased risk of MACE in patients with CAD. Underlying mechanisms should be better understood to develop targeted interventions to reduce MACE in this already high-risk population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".