P-Wave Area Predicts New Onset Atrial Fibrillation in Mitral Stenosis: A Machine Learning Approach - Tse, Gary; Lakhani, Ishan; Zhou, Jiandong; Li, Ka Hou Christien; Lee, Sharen; Liu, Yingzhi; Leung, Keith Sai Kit; Liu, Tong; Baranchuk, Adrian; Zhang, Qingpeng (Frontiers in Bioengineering and Biotechnology, 2020)
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.