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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, 21.
pp. 919-928.
ISSN 1547-5271
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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 |
|---|---|
| Additional Information: | Funding Information: NW receives fellowship funding from the British Heart Foundation (FS/CRTF/22/24362). VM has received fellowship funding from Siemens and Abbott. NNM is in receipt of fellowship funding from Heart Research UK (grant no. RG2701) and Abbott. YM receives research funding from UK Engineering and Physical Sciences Research Council (EP/X023826/1). SAN acknowledges support from the UK Engineering and Physical Sciences Research Council (EP/M012492/1, NS/A000049/1, and EP/P01268X/1), the British Heart Foundation (PG/15/91/31812, PG/13/37/30280, SP/18/6/33805), US National Institutes of Health (NIH R01-HL152256), European Research Council (ERC PREDICT-HF 864055) and Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. CAR receives research funding and/or consultation fees from Abbott, Medtronic, Boston Scientific, Spectranetics and MicroPort outside of the submitted work. |
| 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: | 21 May 2024 08:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94493 |
| DOI: | 10.1016/j.hrthm.2024.02.015 |
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