Analysis of Transesophageal Echocardiography Appropriateness for Diagnosing Infective Endocarditis: Insights From Two Tertiary-Care Hospitals
Bibliographic record
Abstract
Background Echocardiography plays a key role in the diagnosis of infective endocarditis (IE), and recommendations have been published regarding the appropriate use of transesophageal echocardiography (TEE). The objective of this study is to evaluate the utilization of TEE in Regina, Saskatchewan, in the diagnosis of IE. Methods A retrospective chart review was performed on patients aged ≥ 18 years who received a TEE test for the diagnosis of IE from January 1 to December 31, 2019. The primary outcome included the proportion of TEE uses that complied with the American College of Cardiology Foundation and American Society of Echocardiography (ACCF and ASE) recommendations and the European Society of Cardiology (ESC) recommendations. Results A total of 204 admissions involving 188 patients who had TEE performed for the diagnosis of IE occurred within the study period. The mean age was 53.1 ± 17.1 years. Of the 204 TEE uses, 152 (74.5%) were considered appropriate by the ACCF and ASE recommendations. Having at least one predisposing condition (adjusted odds ratio [aOR] 4.30 [95% confidence interval [CI] 2.11-9.04), P < 0.001]) was more likely to be associated with appropriate TEE use, per the ACCF and ASE criteria. Of the 204 TEE uses, only 80 (39.2%) were considered appropriate by the ESC recommendations. Having a history of intravenous drug use (aOR 3.08 [95% CI 1.08-9.27], P = 0.04) and having blood cultures positive for IE-related organisms (aOR 2.31 [95% CI 1.16-4.80], P = 0.02)) were more likely to be associated with appropriate TEE use, per ESC recommendations. Conclusions The current study suggests that the use of TEE in the diagnosis of IE demonstrated variable levels of adherence to recommendations published by the ACCF and ASE and by the ESC, with significant discrepancy between the two.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".