Tools
Tools
Mehta, Vishal S., Ma, YingLiang 
ORCID: https://orcid.org/0000-0001-5770-5843, Wijesuriya, Nadeev, DeVere, Felicity, Howell, Sandra, Elliott, Mark K., Mannkakara, Nilanka N., Hamakarim, Tatiana, Wong, Tom, O’Brien, Hugh, Niederer, Steven, Razavi, Reza and Rinaldi, Christopher A.
(2024)
Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models.
Heart Rhythm.
ISSN 1547-5271
Abstract
Background: Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). Objective: The purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). Methods: We hypothesized certain features—(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC—detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features. Results: A total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74–0.87), sensitivity (68%–83%), specificity (72%–91%), and area under the curve (AUC) (0.767–0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76–0.86), sensitivity (75%–85%), specificity (63%–87%), and AUC (0.684–0.913). Conclusion: Risk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence,complications,computer vision,machine learning,risk prediction,transvenous lead extraction,cardiology and cardiovascular medicine,physiology (medical) ,/dk/atira/pure/subjectarea/asjc/2700/2705 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Mar 2024 18:32 |
| Last Modified: | 09 Apr 2024 09:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94493 |
| DOI: | 10.1016/j.hrthm.2024.02.015 |
Actions (login required)
 |
View Item |