Can machine learning complement traditional medical device surveillance? A case-study of dual-chamber implantable cardioverter–defibrillators Authors Ross JS, Bates J, Parzynski CS, Akar JG, Curtis JP, Desai NR, Freeman JV, Gamble GM, Kuntz R, Li SX, Marinac-Dabic D, Masoudi FA, Normand SLT, Ranasinghe I, Shaw RE, Krumholz HM Received 28 March 2017 Accepted for publication 25 May 2017 Published 16 August 2017 Volume 2017:10 Pages 165—188 DOI http://doi.org/10.2147/MDER.S138158 Checked for plagiarism Yes Review by Single-blind Peer reviewers approved by Dr Akshita Wason Peer reviewer comments 2 Editor who approved publication: Dr Scott Fraser
Joseph S Ross,1–4 Jonathan Bates,4 Craig S Parzynski,4 Joseph G Akar,4,5 Jeptha P Curtis,4,5 Nihar R Desai,4,5 James V Freeman,4,5 Ginger M Gamble,4 Richard Kuntz,6 Shu-Xia Li,4 Danica Marinac-Dabic,7 Frederick A Masoudi,8 Sharon-Lise T Normand,9,10 Isuru Ranasinghe,11 Richard E Shaw,12 Harlan M Krumholz 2–5 1Section of General Medicine, Department of Medicine, 2Robert Wood Johnson Foundation Clinical Scholars Program, Yale
School of Medicine, 3Department of Health Policy and Management, Yale School of Public Health, 4Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, 5Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, 6Medtronic Inc, Minneapolis, MN, 7Division of Epidemiology, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, 8Division of Cardiology, Department of Medicine, University of Colorado, Aurora, CO, 9Department of Health Care Policy, Harvard Medical School, 10Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA; 11Discipline of Medicine, University of Adelaide, Adelaide, SA, Australia; 12Department of Clinical Informatics, California Pacific Medical Center, San Francisco, CA, USA Background: Machine learning methods may complement traditional analytic methods for medical device surveillance. Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safetysignal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; nonfatal ICD-related adverse events, 19.3%–26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%–37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=–0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=–0.042). Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. Keywords: implanted cardioverter–defibrillator, methodology, surveillance
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at http://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.
Related Articles Economic impact of longer battery life of cardiac resynchronization therapy defibrillators in Sweden Fredrik Gadler et al., ClinicoEconomics and Outcomes Research Implantable cardioverter defibrillators: state of the art Jordan C Ray et al., Research Reports in Clinical Cardiology Assessment of the quality of existing patient educational tools focused on sudden cardiac arrest: a systematic evaluation by the Sudden Cardiac Arrest Thought Leadership Alliance Garrett Hazelton et al., Patient Preference and Adherence
Battle of Dual- Versus Single-Chamber ICDs John Gever, MedPage Today Undetected CIED Problems Leading to Sudden Death: More Evidence Deborah Brauser et al., Medscape EUROPACE: ICDs, Pacemakers Okay for Oldest Patients Todd Neale, MedPage Today Dual-Chamber ICD Use Varies Widely Charles Bankhead, MedPage Today Software Flags Problem ICD Leads Chris Kaiser, MedPage Today
Critical appraisal of cardiac implantable electronic devices: complications and management Luigi Padeletti et al., Medical Devices: Evidence and Research Amiodarone for the treatment and prevention of ventricular fibrillation and ventricular tachycardia Hugo Van Herendael et al., Vascular Health and Risk Management