Enterococcus faecalis bacteraemia and infective endocarditis - what are we missing?
Bibliographic record
Abstract
Enterococcus faecalis is an increasingly common cause of infective endocarditis, with a recent study by Dahl et al demonstrating a prevalence of 26% of IE when transoesophageal echo was routinely undertaken. Another study undertaken by Østergaard et al found that 16.7% of patient with E. faecalis bacteraemia developed endocarditis. Based on these findings we examined the rates of IE diagnosed in our own health board to determine if with our current practice is potentially missing cases of IE and if we could improve our management of these bacteraemias. All blood cultures in patients over 18 which were positive for E. faecalis from October 2017 to March 2022 were reviewed. We analysed the patient characteristics, clinical outcomes and included a follow up period of 6 months to assess for recrudescence and treatment failure. The rate of patients with E. faecalis bacteraemia diagnosed with IE was 7.1%. If polymicrobial blood cultures were excluded this rose to 13.0%. Community acquisition, patient cardiac or immune risk factors, monomicrobial culture and multiple positive blood cultures all were associated with IE. 62.1% of patients with E. faecalis bacteraemia did not have an echocardiogram during their admission, due to a variety of reasons. The lower reported rate of IE in our cohort may be explained by higher proportion of CVC related infections. However, given the low rates of echocardiography and poor correlation of echocardiography use with IE risk factors, it is likely that cases of IE are being missed, particularly in those with multiple risk factors. Despite this, there was no difference in one-year survival between those diagnosed with IE vs without IE. We have delivered education sessions and introduced a multidisciplinary team meeting to discuss infective endocarditis cases to address these issues.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".