High precision machine learning-enabled ECG algorithm for predicting sites of idiopathic ventricular arrhythmia origin

Introduction

Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA). The accurate prediction of the origins of IVA can significantly increase the procedure success rate, reduce operation duration and decrease the risk of complications. The present work proposes an ECG analysis algorithm to estimate 21 possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy, which include left coronary cusp (LCC), right coronary cusp (RCC), aortomitral continuity (AMC), summit, LCC-RCC commissure, left His bundle, mitral valve (MV), left septal including left anterior fascicle (LAF), left posterior fascicle (LPF), left anterior papillary muscle (LAPM), left posterior papillary muscle (LPPM), anterior cusp (AC), left cusp (LC), right cusp (RC), RVOT septal, free wall, right His bundle, tricuspid valve (TV), and right anterior papillary muscle (RAPM).

Method

A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. The first scheme will help the operators to figure out the origin from epicardium of left ventricular summit, right, and left ventricle. The second one can separate origins from left/right outflow tract and left/right non-out flow tract, respectively. The third one is able to predict 18 anatomical locations, and the fourth scheme can distinguish 21 possible sites. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the validation cohorts.

Results

In the first classification scheme used to predict right ventricular endocardium, left ventricular endocardium, and epicardium of left ventricular summit, the model achieved an accuracy of 99.79 (99.41–99.89) and a F1-score of 99.84 (99.6–99.96). For scheme 2, the proposed method reached an accuracy of 99.62 (99.09–99.78) and a F1-score of 99.42 (98.79–99.75). For scheme 3, the model achieved an accuracy of 97.78 (96.76–98.41), a F1-score of 97.74 (94.15–99.73), and an adjusted accuracy of 98.53 (98.33–99.15). For scheme 4 that can distinguish 21 origin sites, the proposed model attained an accuracy of 98.24 (97.36–98.71), a F1-score of 98.56 (97.88–99.12) and an adjusted accuracy of 98.75 (98.35–99.38).

Conclusion

The proposed machine learning model can be immediately and effortlessly deployed to electrophysiology labs allowing cardiologists to predict the exact origins of arrhythmia and provide an optimum treatment plan both before and during the CA procedure. This approach will significantly reduce the CA procedure duration and the risk of complications.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): 2020 Natural Science Foundation of Zhengjiang Province Confusion matrix for classification schemes

Panniculectomy with simultaneous gynecologic oncology surgery

OBJECTIVE

The objective was to report the utility and morbidity of panniculectomy in obese gynecologic oncology patients undergoing exploratory laparotomy.

METHODS

A retrospective chart review of 41 consecutive women who had a panniculectomy as part of an abdominal gynecologic oncology procedure between July 1996 and May 2000 was performed. Obese patients possessing a large pannus, the majority with a BMI > or = 30 kg/m(2), were included. Demographic, preoperative, operative, and postoperative data were obtained. Statistical analyses were performed using Statistical Analysis System (SAS) Version 6.13.

RESULTS

Panniculectomy was performed on 41 patients with a mean age of 55, weight of 126 kg, and BMI of 48 kg/m(2). The most common comorbidities in this population were hypertension, diabetes, and osteoarthritis. Wound infection occurred in 4 (9.8%) patients; 88% of the patients received a hysterectomy. The average EBL was 358 cc. Operative time and length of hospital stay were on average 203 min and 5.5 days, respectively. A prior history of diabetes increased the risk of early complications (P = 0.03). Late complications were more likely to occur in older women (P = 0.05). Wound complications were increased in patients with larger BMI's (P = 0.05).

CONCLUSIONS

This study supports the safety of the panniculectomy procedure in this high-risk group of morbidly obese patients for whom a technical advantage may be achieved by improved operative exposure.