Surgical outcomes and optimal approach to treatment of aortic valve endocarditis with aortic root abscess -- systematic review and meta-analysis
Bibliographic record
Abstract
Background Data on the postoperative outcomes for patients with infective endocarditis complicated by an aortic root abscess is sparse due to the condition’s low incidence and high mortality rates. This systematic review and meta-analysis aims to evaluate existing data on the impact of aortic root abscesses (ARA) on the postoperative outcomes of surgically managed infective endocarditis (IE) and to inform optimal surgical approach. Methods The online databases MEDLINE, EMBASE and Cochrane library were searched from 1990 to 2022 for studies comparing ARA with NARA (no ARA) in infective endocarditis. Data was extracted by two independent investigators and aggregated in a random-effects model (Review Manager version 5.3). Risk of bias was assessed using an adapted version of the Newcastle-Ottawa scale. Results Six clinical studies were included in the meta-analysis (n = 1982). The ARA group was associated with an increased risk of in-hospital mortality (OR = 1.74 96% CI 1.18-2.56) and late mortality (HR = 1.27 95% CI 1.03-1.58). The reoperation meta-analysis was complicated by high rates of heterogeneity (I 2 = 59%) and found no significant differences in reoperation between group ARA and NARA (no ARA) (HR = 1.48; 95% CI 0.92-2.40). Post-hoc scatter graph showed a strong linear relationship (r=0.998), suggesting hospitals with higher rates of aortic root replacement (ARR) achieve lower rates of reoperation for ARA patients compared with PR. Conclusions The presence of an ARA in aortic valve endocarditis is associated with elevated early and late mortality despite modern standards of care. Additionally, ARR should be considered to have a favourable postoperative profile for use in this context.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".