Enhancing Transvenous Lead Extraction Risk Prediction: Integrating Imaging Biomarkers into Machine Learning Models:Using imaging to predict risk following TLE

Background:Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). Objective:We tested if integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAE: procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). Methods:We hypothesised certain features: i) lead angulation ii) coil percentage inside the superior vena cava (SVC), and iii) number of overlapping leads in the SVC, detected from a pre-TLE plain anterior-posterior (AP) chest x-ray (CXR) would improve prediction of MAE and long procedure times. A deep-learning convolutional neural network was developed to automatically detect these CXR features.Results:1050 cases were included, with 24 (2.3%) MAEs. The neural network was able to detect: i) heart border with 100% accuracy, ii) coils: 98% accuracy, iii) acute angle in the right ventricle and SVC: 91% and 70% accuracy respectively. The following features significantly improved MAE prediction: i) ≥50% coil within the SVC, ii) ≥2 overlapping leads in the SVC, and iii) acute lead angulation. Balanced accuracy (0.74 to 0.87), sensitivity (68% to 83%), specificity (72% to 91%), and area under the curve (AUC) (0.767 to 0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76 to 0.86), sensitivity (75% to 85%), specificity (63% to 87%), and AUC (0.684 to 0.913).Conclusion:Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedure time related to TLE.<br/