Medical Management of Infants With Supraventricular Tachycardia: Results From a Registry and Review of the Literature
Bibliographic record
Abstract
Background Several medication choices are available for acute and prophylactic treatment of refractory supraventricular tachycardia (SVT) in infants. There are almost no controlled trials, and medication choices are not necessarily evidence based. Our objective was to report the effectiveness of management strategies for infant SVT. Methods A registry of infants admitted to hospital with re-entrant SVT and no haemodynamically significant heart disease were prospectively followed at 11 international tertiary care centres. In addition, a systematic review of studies on infant re-entrant SVT in MEDLINE and EMBASE was conducted. Data on demographics, symptoms, acute and maintenance treatments, and outcomes were collected. Results A total of 2534 infants were included: n=108 from the registry (median age, 9 days [0-324 days], 70.8% male) and n=2426 from the literature review (median age, 14 days; 62.3% male). Propranolol was the most prevalent acute (61.4%) and maintenance treatment (53.8%) in the Registry, whereas digoxin was used sparingly (4.0% and 3.8%, respectively). Propranolol and digoxin were used frequently in the literature acutely (31% and 33.2%) and for maintenance (17.8% and 10.1%) ( P < 0.001). No differences in acute or prophylactic effectiveness between medications were observed. Recurrence was higher in the Registry (25.0%) vs literature (13.4%) ( P < 0.001), and 22 (0.9%) deaths were reported in the literature vs none in the Registry. Conclusion This was the largest cohort of infants with SVT analysed to date. Digoxin monotherapy use was rare amongst contemporary paediatric cardiologists. There was limited evidence to support one medication over another. Overall, recurrence and mortality rates on antiarrhythmic treatment were low.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".