Getting to the Heart of the Matter: Increased Risk of Developing Infective Endocarditis Among Patients on Hemodialysis
Bibliographic record
Abstract
Background: There is limited data characterizing the rates of IE following episodes of bacteremia among HD patients across a wide range of microorganisms. We sought to assess the proportion of infective endocarditis (IE)) following bacteremia in an unselected cohort of HD patients, as compared to the non-dialysis population. Methods: We did a retrospective cohort study of all adults in Calgary, Alberta with ≥ 1 positive blood isolates between January 1, 2006, and October 15, 2022 for the most prevalent microorganisms in the population (excluding coagulase negative staphylococcus). Physician claims data was used to identify patients receiving maintenance HD ≤ 3 weeks prior to the index blood culture isolate (defined as the first isolate of each species per patient in a 30-day period). IE was defined using International Classification of Disease codes as cases occurring ≤ 90 days after index isolate. We assessed the proportion of IE associated with bacteremia episodes and stratified by microorganism. Results: There were 1498 and 22,683 episodes of bacteremia among participants receiving and not receiving maintenance HD. Staphylococcus aureus was the most common organism in the HD cohort (n= 659, 44%) whereas Escherichia coli was the most common among non-dialysis participants (n= 9,294, 41%). Across all bloodstream infections, participants on HD were more likely to develop IE vs non-dialysis participants (relative risk [RR] 3.96, 95% confidence interval [CI] 3.48-4.51) as shown in Figure 1. This increased risk was observed for all organisms except Enterococcus faecalis (RR 1.0, 95% CI 0.6-1.68), including those that are less commonly associated with IE such as Beta-hemolytic streptococcus (RR 4.09, 95% CI 2.64-6.34). Conclusion: Patients receiving maintenance HD are at markedly increased risk of IE following bacteremia due to most pathogens. These findings have implications for risk-stratifying episodes of bacteremia in the HD population. Funding: Other U.S. Government Support
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".