Emergency Department Visits Following Transcatheter Cardiac Interventions: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
While hospital readmission is the most common health system performance metric after transcatheter cardiac interventions, emergency department (ED) visits are rarely evaluated. We aimed to synthesize the incidence, causes, predictors, costs, and outcomes of ED visits following transcatheter procedures in patients undergoing percutaneous coronary interventions (PCI), atrial septal defect (ASD) closures, patent foramen ovale (PFO) closures, and transcatheter aortic valve interventions (TAVI). Embase and Medline databases were searched from January 01, 2004, to July 25, 2024. We descriptively summarized ED visit outcomes for all studies and, when relevant, performed a meta-analysis to obtain a pooled mean cumulative incidence for 30-day ED visits following intervention. Nineteen studies met the eligibility criteria, nine of which were subsequently meta-analyzed. The incidence of 30-day ED visits in individual studies ranged from 2.8% to 22% for PCI patients, 4% to 15.3% for TAVI patients, and 16.9% to 19.5% for ASD/PFO patients. The pooled mean cumulative incidence of 30-day ED visits was 9% (95% CI: 3%-18%) for PCI patients and 12% (95% CI: 9%-15%) for TAVI patients. A high degree of between-study heterogeneity was found in the meta-analysis of these outcomes. Based on limited reporting, risk factors associated with ED visits varied by procedure and included factors such as urgency of procedure, length of stay, sex, age, and dementia. The incidence and predictors of ED visits post-transcatheter cardiac interventions were noteworthy yet highly variable and inconsistently reported. Further research into the risk factors that contribute to ED visits is needed to capture the burden of this outcome.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".