A Case of Non-Tachycardic Atrial Fibrillation Whose Left Ventricular Systolic Dysfunction Improved After Catheter Ablation
Bibliographic record
Abstract
It is difficult to identify the causes and optimal treatment of heart failure (HF) in patients with atrial fibrillation (AF) and HF with reduced ejection fraction (EF) (HFrEF). Tachyarrhythmia can cause left ventricular (LV) systolic dysfunction called tachycardia-induced cardiomyopathy (TIC). In patients with TIC, conversion to sinus rhythm may lead to improvement in LV systolic dysfunction. However, it is unclear whether we should try to convert patients with AF without tachycardia to sinus rhythm. A 46-year-old man with chronic AF and HFrEF came to our hospital. His New York Heart Association (NYHA) classification was class II. The blood test showed a brain natriuretic peptide of 105 pg/mL. Electrocardiogram (ECG) and 24-h ECG showed AF without tachycardia. Transthoracic echocardiography (TTE) showed left atrial (LA) dilatation, LV dilatation, and diffuse LV hypokinesis (EF was 40%). Although he was optimized medically, NYHA classification II persisted. Therefore, he underwent direct current cardioversion and catheter ablation. After his AF converted to a sinus rhythm of heart rate (HR) 60 - 70 beats per minute (bpm), TTE showed improvement in LV systolic dysfunction. We gradually reduced oral medications for arrhythmia and HF. We subsequently succeeded in discontinuing all medications 1 year after catheter ablation. TTE performed between 1 and 2 years after catheter ablation showed normal LV function and normal cardiac size. During the 3 years of follow-up, there was no recurrence of AF, and he was not readmitted to the hospital. This patient showed the effectiveness of converting AF to sinus rhythm in patients without tachycardia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| 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".