P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach

Gary Tse, Ishan Lakhani, Jiandong Zhou, Ka Hou Christien Li, Sharen Lee, Yingzhi Liu, Keith Sai Kit Leung, Tong Liu, Adrian Baranchuk, Qingpeng Zhang

Research output: Contribution to journalArticlepeer-review


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-center retrospective study of Chinese patients, diagnosed with mitral stenosis in sinus rhythm at baseline, 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 non-linear 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.
Original languageEnglish
Article number479
JournalFrontiers In Bioengineering and Biotechnology
Publication statusPublished - 15 May 2020

Bibliographical note

© 2020 Tse, Lakhani, Zhou, Li, Lee, Liu, Leung, Liu, Baranchuk and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


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