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Med Devices (Auckl). 2017 Aug 16;10:165-188. doi: 10.2147/MDER.S138158. eCollection 2017.

Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators.

Author information

1
Section of General Medicine, Department of Medicine.
2
Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine.
3
Department of Health Policy and Management, Yale School of Public Health.
4
Center for Outcomes Research and Evaluation, Yale-New Haven Hospital.
5
Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT.
6
Medtronic Inc, Minneapolis, MN.
7
Division of Epidemiology, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD.
8
Division of Cardiology, Department of Medicine, University of Colorado, Aurora, CO.
9
Department of Health Care Policy, Harvard Medical School.
10
Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
11
Discipline of Medicine, University of Adelaide, Adelaide, SA, Australia.
12
Department of Clinical Informatics, California Pacific Medical Center, San Francisco, CA, USA.

Abstract

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 safety-signal 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

Conflict of interest statement

Disclosure JSR receives support from the US FDA as part of the Centers for Excellence in Regulatory Science and Innovation program and from the Laura and John Arnold Foundation to support the Collaboration on Research Integrity and Transparency at Yale. JSR, NRD, HMK, and GMG receive research support through Yale University from Johnson and Johnson to develop methods of clinical trial data sharing. JSR and GMG receive research support from the Blue Cross Blue Shield Association to better understand medical technology evidence generation. JSR, JPC, NRD, SXL, SLTM, IR, HMK, and CSP work under contract to the Centers for Medicare and Medicaid Services to develop and maintain performance measures that are used for public reporting. JVF receives salary support from the American College of Cardiology NCDR, and modest consulting fees from Janssen Pharmaceuticals. RK is an employee of Medtronic Inc. DMD is an employee of the FDA. HMK chairs a cardiac scientific advisory board for United Health, is a participant/participant representative of the IBM Watson Health Life Sciences Board, is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna, and is the founder of Hugo, a personal health-information platform. The authors report no other conflicts of interest in this work.

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