Arrhythmias in adults with congenital heart disease and heart failure
Bibliographic record
Abstract
Arrhythmias and heart failure are among the most common complications encountered by adults with congenital heart disease (CHD). In this contemporary review, we explore the interactions between arrhythmias and heart failure and discuss management strategies. Major knowledge gaps are highlighted throughout. Interactions between arrhythmias and heart failure are complex and bidirectional, with one begetting the other. Arrhythmias can provoke heart failure through various mechanisms: conduction disturbances may contribute to inefficient ventricular filling and contraction patterns; bradyarrhythmias and tachyarrhythmias can result in a reduction in cardiac output; hypoxemia may be exacerbated by right-to-left shunting; and tachycardia-induced cardiomyopathy has potentially devastating consequences if the diagnosis is delayed. In turn, heart failure promotes arrhythmogenesis through various structural (eg, fibrosis, chamber dilation, hypertrophy) and electrical remodeling effects that include changes to ion currents and channels and connexin expression, along with shortening of atrial and ventricular refractory periods with increased heterogeneity. Several shared comorbidities can contribute to, and modulate the impact of, arrhythmias and heart failure. Preemptive arrhythmia management can potentially mitigate effects on heart failure exacerbations. Similarly, optimal heart failure control could curtail its impact on arrhythmogenesis. Treatment strategies to prevent or treat heart failure in adults with CHD encompass pharmacological agents, catheter ablation, and device therapies including defibrillators, cardiac resynchronization therapy, and His bundle pacing. High-priority research avenues with major knowledge gaps include tachycardia-induced cardiomyopathy, catheter ablation of atrial fibrillation, defibrillator indications in high-risk subsets, and the role of cardiac resynchronization therapy and His bundle pacing in diverse forms of CHD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".