P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach
Bibliographic record
Abstract
Introduction: Mitral stenosis is associated with an atrial cardiomyopathic process, leading to abnormal atrial electrophysiology, manifesting as prolonged P-wave duration (PWD), larger P-wave area, increased P-wave dispersion (PWDmax – PWDmin), and/or higher P-wave terminal force on lead V1 (PTFV1) on the electrocardiogram. Methods: This was a single-centre retrospective study on Chinese patients, diagnosed with mitral stenosis in sinus rhythm at baseline, from a single center between November 2009 and October 2016. Automated ECG measurements from raw data were determined. The primary outcome was incident atrial fibrillation (AF). Results: A total 59 mitral stenosis patients were included (age 59 [54-65] years, 13 (22%) males). New onset AF was observed in 27 patients. Age (odds ratio [OR]: 1.08 [1.01-1.16], P=0.017), systolic blood pressure (OR: 1.03 [1.00-1.07]; P=0.046), mean P-wave area in V3 (odds ratio: 3.97 [1.32-11.96], P=0.014) were significant predictors of incident AF. On multivariate analysis, age (OR: 1.08 [1.00-1.16], P=0.037) and P-wave area in V3 (OR: 3.64 [1.10-12.00], P=0.034) remained significant predictors of AF. Receiver-operating characteristic (ROC) analysis showed that the optimum cut-off for P-wave area in V3 was 1.45 Ashman units (area under the curve: 0.65) for classification of new onset AF. A decision tree learning model with individual and nonlinear interaction variables with age achieved the best performance for outcome prediction (accuracy=0.84, precision=0.84, recall=0.83, F-measure=0.84). Conclusion: Atrial electrophysiological alterations in mitral stenosis can detected on the electrocardiogram. Age, systolic blood pressure and P-wave area in V3 predicted new onset AF. A decision tree learning model significantly improved outcome prediction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".