Impact of routine transoesophageal echocardiography on safety, outcomes, and cost of pulmonary vein ablation: inferences drawn from a decision analysis model
Bibliographic record
Abstract
AIMS: The practice of routine vs. selective transoesophageal echocardiography (TEE) surveillance for left atrial appendage or intracavitary thrombus prior to pulmonary vein isolation (PVI) varies widely as evidence to guide this decision in terms of important clinical outcomes is lacking. METHODS AND RESULTS: We constructed a decision analysis model to compare the cost-effectiveness of routine TEE for detection of left atrial thrombus vs. no TEE. The model incorporated health outcomes and costs. Markov methodology was used to follow patients as they transition through varying health states. We examined a hypothetical cohort of patients with symptomatic atrial fibrillation suitable for PVI, and expected outcomes were modelled over a period of 2 years. Simulated patients (SPs) undergoing a strategy of a routine TEE experienced significantly fewer transient ischemic attacks (TIAs) [OR 0.28 (0.22-0.37)], and debilitating strokes [OR 0.23 (0.15-0.33)]. Routine TEE led to an absolute risk reduction for stroke of 1.2% [number needed to treat (NNT) 84 (79-100)] and 1.9% for TIA [NNT 53 (48-59)]. The incremental cost-effectiveness ratio (ICER) for TEE was $226,608 per quality-adjusted life year (QALY). The ICER for TEE among high-risk SPs, with pre-existing clot in the left atrium, was $2232 per QALY. CONCLUSION: Decision analysis and microsimulation suggest that routine use of TEE in an unselected population prior to PVI lowers the incidence of cerebral thrombo-embolic events but with considerable cost per QALY.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".