Abstract 12155: Trends in Catheter Ablation of Ventricular Tachycardia in Patients With Ischemic and Nonischemic Cardiomyopathy: A National Inpatient Sample Trend in the Years 2016-2019
Bibliographic record
Abstract
Introduction: Catheter ablation of ventricular tachycardia (CAVT) has emerged as a viable option in structural heart disease. Recent studies report differences in outcomes of CATV in ischemic (ICM) and non-ischemic (NICM) cardiomyopathy. We aimed to examine temporal trends and in-hospital outcomes of CATV in ICM and NICM. Methods: National Inpatient Sample 2016-2019 was queried using ICD-10 codes to identify adults with ICM or NICM who underwent CAVT. Temporal trends were assessed using logistic regression. Results: 193895 CAVT were performed between 2016 and 2019. CAVT increased in numbers for both ICM [16415 in 2016 to 23030 in 2019 (P<0.001)] and NICM [22915 in 2016 to 31355 in 2019 (P<0.001)] during this period. Overall, rate of most common in-hospital complications increased during study period (7.4% in 2016 to 9.2% in 2019, p<0.0001) particularly of cardiogenic shock (3% in 2016 to 4.2% in 2019, p<0.001) and pericardial effusion (2.9% in 2016 and 3.7% in 2019, p<0.001). In concordance with higher rates of complications over time, there was also an increase in the rate of comorbid conditions as indicated by the Charlson’s Comorbidity Index Score >3 (35% in 2016 and 40% in 2019, p<.0001). Differences existed in the rate of most common in-hospital complications between ICM and NICM patients including cardiogenic shock (5%-vs-2.6%), pericarditis (1.3%-vs-2.1%), pericardial effusion 2.6%-vs-3.7%), and cardiac tamponade 1%-vs-1.5%) [all p<0.001). In-patient mortality and length of stay were higher in those with ICM (1.6%-vs-0.8%; and 6.3-vs-4.9 days; both p<0.001). Conclusions: The number of VT ablations for structural heart disease has increased in recent years. These promising procedures are performed with reasonable complication rates in progressively more complex settings. Differences exist in rates of complications between patients with ICM and NICM including in-hospital mortality.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".