Development of a Classification Scheme for Examining Adverse Events Associated with Medical Devices, Specifically the DaVinci Surgical System as Reported in the FDA MAUDE Database

Gupta, Priyanka; Schomburg, John; Krishna, Suprita; Adejoro, Oluwakayode; Wang, Qi; Marsh, Benjamin M.; Nguyen, Andrew; Genere, Juan Reyes; Self, Patrick; Lund, Elizabeth M.; Konety, Badrinath R.

doi:10.1089/end.2016.0396

Cited by 27 publications

(22 citation statements)

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“…The introduction of devices following clearance is unstructured and variable; more often than not, their use is reported in non-comparative trials without institutional board review. This process carries an obvious risk to patient safety, and a number concerns have been raised regarding the lack of centralised adverse event reporting [ 30 ]. To address this shortfall, the Balliol Collaboration has proposed the IDEAL model for safe innovation [ 3 – 5 ].…”

Section: Discussionmentioning

confidence: 99%

Robot-assisted stereotactic brain biopsy: systematic review and bibliometric analysis

et al. 2018

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IntroductionStereotactic brain biopsy represents one of the earliest applications of surgical robotics. The aim of the present systematic review and bibliometric analysis was to evaluate the literature supporting robot-assisted brain biopsy and the extent to which the scientific community has accepted the technique.MethodsThe Cochrane and PubMed databases were searched over a 30-year period between 1st of January 1988 and 31st of December 2017. Titles and abstracts were screened to identify publications that met the following criteria: (1) featured patients with brain pathology, (2) undergoing stereotactic brain biopsy, (3) reporting robot-assisted surgery, and (4) outcome data were provided. The reference lists of selected studies were also sought, and expert opinion sought to identify further eligible publications. Selected manuscripts were then reviewed, and data extracted on effectiveness and safety. The status of scientific community acceptance was determined using a progressive scholarly acceptance analysis.ResultsAll identified studies were non-randomised, including 1 retrospective cohort study and 14 case series or reports. The diagnostic biopsy rate varied from 75 to 100%, and the average target accuracy varied from 0.9 to 4.5 mm. Use of the robot was aborted in two operations owing to geometric inaccessibility and an error in image registration but no associated adverse events were reported. A compounding progressive scholarly acceptance analysis suggested a trend towards acceptance of the technique by the scientific community.ConclusionsIn conclusion, robot-assisted stereotactic brain biopsy is an increasingly mainstream tool in the neurosurgical armamentarium. Further evaluation should proceed along the IDEAL framework with research databases and comparative trials.

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Section: Discussionmentioning

confidence: 99%

Robot-assisted stereotactic brain biopsy: systematic review and bibliometric analysis

et al. 2018

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“…In 2017, Connor applied Chi‐Square, Kolmogorov‐Smirnov, and regression in the review of device‐class specific reports and recalls in MAUDE. Also in 2017, Gupta et al applied Chi‐Square along with manual classification to monitor MAUDE AEs …”

Section: Discussionmentioning

confidence: 99%

“…Registries as defined for this review included hospital and provider case series and medical‐record based data. MAUDE represented the second most common data source with eight studies from 1998 to 2017 representing 8% of articles reviewed …”

Section: Summary Of Reviewed Articlesmentioning

confidence: 99%

“…PV methods groups include Disproportionality , Propensity Score , Risk Adjustment , Bayesian , Sequential Probability Ratio , and Advanced Data Mining . Among the 14 reviewed Device articles, methods groups ordered by number of articles were Disproportionality , Risk Adjustment , Sequential Probability Ratio , Survival Modeling , Propensity Score , Bayesian, and Advanced Data Mining . Changepoint analysis from one Device article by Xu and other methods that did not fall into any method group were labeled as Other .…”

Section: Summary Of Reviewed Articlesmentioning

confidence: 99%

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Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

Chung

Etter

Yoo

2020

Pharmacoepidemiology and Drug

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Purpose: The collection and analysis of real-world data for the active monitoring of medical device performance and safety has become increasingly important. Spontaneous reports, such as those in the Food & Drug Administration's (FDA's) Manufacturer and User Facility Device Experience (MAUDE), provide early warning of potential issues with marketed devices. This review synthesizes the current literature on medical device surveillance signal detection and provides a framework for application of methods to active surveillance of spontaneous reports.Methods: Ovid MEDLINE, Ovid Embase, Scopus, and PubMed databases were systematically searched up to January 2019. Additionally, five methods articles from pharmacovigilance were added that had potential applications to medical devices.Results: Among 105 articles included, the most common source of data (84%) was registries; median time between data collection and publication was 8 years. Surgical procedure outcome signal detection articles comprised 83% while 14% were on device outcome signal detection. The most common family of methods cited (70%) was Sequential Probability Ratio.Conclusion: Application of any signal detection algorithm requires careful consideration of influential factors, data limitations, and algorithmic assumptions.We recommend approaches using disproportionality, statistical process control, and sequential probability tests and provide R packages to further development efforts. The small number of published examples suggest that further development of statistical methods and technological solutions to analyze large amounts of data for device safety and performance is needed. Fundamental differences in products, data infrastructure, and the regulatory landscape suggest that medical device vigilance requires its own body of research distinct from pharmacovigilance.

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“…Agcaoglu and colleagues, reporting on malfunction and failure of robotic systems, described a 4·5 per cent incidence of these events, related to instruments, optical system, arms and the console. Recently Gupta and co‐workers found that 77 per cent of adverse events during RAS were classified as being definitely related to the device, 15 per cent as possibly related and 8 per cent as not related. Using the results from a specialized centre as part of informed consent can misrepresent these risks and outcomes for patients.…”

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confidence: 99%

Bioethical approach to robot-assisted surgery

Paolo

Boggi

Turillazzi

2019

British Journal of Surgery

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Development of a Classification Scheme for Examining Adverse Events Associated with Medical Devices, Specifically the DaVinci Surgical System as Reported in the FDA MAUDE Database

Abstract: Our classification system is a valid tool with moderate inter-rater agreement that can be used to better understand device-related adverse events. The majority of robotic related events were mild but associated with the device.

Cited by 27 publications

References 10 publications

Robot-assisted stereotactic brain biopsy: systematic review and bibliometric analysis

Robot-assisted stereotactic brain biopsy: systematic review and bibliometric analysis

Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

Bioethical approach to robot-assisted surgery

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