Temporal Trends, Characteristics, and Outcomes of Infective Endocarditis After Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: Procedural improvements combined with the contemporary clinical profile of patients undergoing transcatheter aortic valve replacement (TAVR) may have influenced the incidence and outcomes of infective endocarditis (IE) following TAVR. We aimed to determine the temporal trends, characteristics, and outcomes of IE post-TAVR. METHODS: Observational study including 552 patients presenting definite IE post-TAVR. Patients were divided in 2 groups according to the timing of TAVR (historical cohort [HC]: before 2014; contemporary cohort [CC]: after 2014). RESULTS: Overall incidence rates of IE were similar in both cohorts (CC vs HC: 5.45 vs 6.52 per 1000 person-years; P = .12), but the rate of early IE was lower in the CC (2.29‰ vs 4.89‰, P < .001). Enterococci were the most frequent microorganism. Most patients presented complicated IE ( CC: 67.7%; HC: 69.6%; P = .66), but the rate of surgical treatment remained low (CC: 20.7%; HC: 17.3%; P = .32). The CC exhibited lower rates of in-hospital acute kidney injury (35.1% vs 44.6%; P = .036) and in-hospital (26.6% vs 36.4%; P = .016) and 1-year (37.8% vs 53.5%; P < .001) mortality. Higher logistic EuroScore, Staphylococcus aureus etiology, and complications (stroke, heart failure, and acute renal failure) were associated with in-hospital mortality in multivariable analyses (P < .05 for all). CONCLUSIONS: Although overall IE incidence has remained stable, the incidence of early IE has declined in recent years. The microorganism, high rate of complications, and very low rate of surgical treatment remained similar. In-hospital and 1-year mortality rates were high but progressively decreased over time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".