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Sau A, Ahmed A, Chen JY, Pastika L, Wright I, Li X, Handa B, Qureshi N, Koa-Wing M, Keene D, Malcolme-Lawes L, Varnava A, Linton NWF, Lim PB, Lefroy D, Kanagaratnam P, Peters NS, Whinnett Z, Ng FS. Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients. Eur Heart J Digit Health 2024; 5:50-59. [PMID: 38264702 PMCID: PMC10802825 DOI: 10.1093/ehjdh/ztad064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 01/25/2024]
Abstract
Aims Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT. Methods and results In this single-centre retrospective study, we analysed data from 69 VT episodes stored on ICDs from 27 patients (36 spontaneously terminating VT, 33 sustained VT). Several VT CL parameters including heart rate variability metrics were calculated. Additionally, a first order auto-regression model was fitted using the first 10 CLs. Using features derived from the first 10 CLs, a random forest classifier was used to predict VT termination. Sustained VT episodes had more stable CLs. Using data from the first 10 CLs only, there was greater CL variability in the spontaneously terminating episodes (mean of standard deviation of first 10 CLs: 20.1 ± 8.9 vs. 11.5 ± 7.8 ms, P < 0.0001). The auto-regression coefficient was significantly greater in spontaneously terminating episodes (mean auto-regression coefficient 0.39 ± 0.32 vs. 0.14 ± 0.39, P < 0.005). A random forest classifier with six features yielded an accuracy of 0.77 (95% confidence interval 0.67 to 0.87) for prediction of VT termination. Conclusion Ventricular tachycardia CL variability and instability are associated with spontaneously terminating VT and can be used to predict spontaneous VT termination. Given the harmful effects of unnecessary ICD shocks, this machine learning model could be incorporated into ICD algorithms to defer therapies for episodes of VT that are likely to self-terminate.
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Affiliation(s)
- Arunashis Sau
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Amar Ahmed
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
| | - Jun Yu Chen
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
| | - Libor Pastika
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
| | - Ian Wright
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Xinyang Li
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
| | - Balvinder Handa
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Norman Qureshi
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Michael Koa-Wing
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Daniel Keene
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Louisa Malcolme-Lawes
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Amanda Varnava
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Nicholas W F Linton
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Phang Boon Lim
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - David Lefroy
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Prapa Kanagaratnam
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Nicholas S Peters
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Zachary Whinnett
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
| | - Fu Siong Ng
- National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, 72 Du Cane Road, W12 0HS, London, UK
- Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, 369 Fulham Road, SW10 9NH, London, UK
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