Predisposing factors of infective endocarditis and their relation to microbiological findings
Bibliographic record
Abstract
Abstract Introduction Understanding the predisposing factors for infective endocarditis (IE) is essential for implementing prevention strategies, prophylaxis, and providing recommendations related to hygiene and other habits. The objective of our study was to investigate possible predisposing factors for IE and compare them with the microbiological findings obtained. Methods A registry of patients diagnosed with IE between 2016 and 2022 at a reference hospital for cardiovascular surgery was utilized. These patients were grouped according to the type of microbiological isolation. These groups were analyzed based on their baseline characteristics, cardiac and extracardiac predisposing factors. Lastly, risk factors present or occurring in proximity to the IE event (endoscopic procedures, dental work, venous infections, etc.) were considered. Results A total of 189 patients were included, with Enterococcus being the microorganisms most frequently isolated (N=36), followed by S. epidermidis (N=33) and S. aureus (N=35). The median age was 69 years, with differences among groups, showing that patients with infections by Enterococcus, S. aureus, S. epidermidis, and S. bovis presented with IE at an older age (p<0.01). It was found that 61% of patients with IE had some type of pre-existing heart disease, with the presence of valvular heart disease (at least moderate) being the most common (42%). Among these, there was a high frequency of patients with bicuspid valve disease (7%, representing 20% of IE cases in the aortic position), and these patients had a younger age (mean 52 years). The precursor risk factors are described in the image. Conclusions The Enterococcus family was the most frequently isolated in this study, being associated with recent diagnoses of urothelial tumors and urinary tract instrumental procedures. S. bovis was related to procedures in the gastrointestinal tract and digestive tumors. S. aureus was associated with episodes of phlebitis, and S. viridans with dental procedures, although without reaching significance. The most common predisposing heart disease was valvular heart disease. Bicuspid valve disease was frequent and was associated with infections in younger patients and with microorganisms typical of oral, skin, and urinary tract flora (S. epidermidis, Enterococcus, and S. viridans).Image 1Image 2
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".