Use of acoustic cardiography immediately following electrical cardioversion to predict relapse of atrial fibrillation
Bibliographic record
Abstract
Predicting atrial fibrillation (AF) recurrence after successful electrical cardioversion (ECV) is difficult. The main aim of this study was to investigate whether acoustic cardiography (AUDICOR® 200) immediately post-ECV might provide indices for AF relapse following cardioversion. Acoustic cardiography parameters included Electromechanical Activation Time (EMAT), Left Ventricular Systolic Time (LVST), QRS duration, heart rate and third heart sound intensity (S3 Strength). We analysed data from 140 patients who underwent successful cardioversion and in whom AUDICOR results and echocardiographic measurements immediately after (baseline) ECV were available. Patients were prospectively followed-up at 4-6 weeks, 3 and 12 months post-ECV, and sinus rhythm maintenance was evaluated using acoustic cardiography and Holter electrocardiography. The effect of each baseline AUDICOR parameter on the hazard of AF relapse was investigated using Cox proportional hazards (PH) models. Fifty patients (35.7%) had AF relapse. Of all the AUDICOR parameters, only S3 Strength exhibited consistent predictive value. Increasing S3 Strength increased the hazard of relapse in a univariable Cox PH model (HR=2.52, p=0.003), and in two multivariable Cox PH model constructions (Model 1 excluded heart rate and Model II excluded EMAT/RR, LVST and LVST/RR) both of which included the parameters as continuous variables (Model I: HR=1.15, p=0.042; Model II: HR=1.14, p=0.045) or the parameters dichotomized according to suggested cut-points (Model I: HR=2.5, p=0.007; Model II: HR=2.09, p=0.031). In conclusion, this study suggests that acoustic cardiography may be a simple inexpensive and quantitative bedside method to assist in prediction of AF recurrence after ECV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".