A Primer to the Structure, Content and Linkage of the FDA’s Manufacturer and User
            Facility Device Experience (MAUDE) Files

Ensign, Lisa G.; Cohen, K. Bretonnel

doi:10.5334/egems.221

Cited by 21 publications

(20 citation statements)

References 29 publications

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“…We searched the online Manufacturer and User Facility Device Experience (MAUDE) database, which contains medical device reports of suspected device-associated deaths, serious injuries, and malfunctions received by the FDA 38 , 39 . A total of 99 unique manufacturer-reviewed reports for the EkoSonic device and nine reports for the FlowTriever device from cases of PE or unspecified indication are contained in MAUDE ( Table 4 ).…”

Section: Evolving Reperfusion Strategies To Improve Early Outcomesmentioning

confidence: 99%

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Immediate and late impact of reperfusion therapies in acute pulmonary embolism

Valerio

Klok

2019

European Heart Journal Supplements

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Haemodynamic instability and right ventricular dysfunction are the key determinants of short-term prognosis in patients with acute pulmonary embolism (PE). Residual thrombi and persistent right ventricular dysfunction may contribute to post-PE functional impairment, and influence the risk of developing chronic thromboembolic pulmonary hypertension. Patients with haemodynamic instability at presentation (high-risk PE) require immediate primary reperfusion to relieve the obstruction in the pulmonary circulation and increase the chances of survival. Surgical removal of the thrombi or catheter-directed reperfusion strategies is alternatives in patients with contraindications to systemic thrombolysis. For haemodynamically stable patients with signs of right ventricular overload or dysfunction (intermediate-risk PE), systemic standard-dose thrombolysis is currently not recommended, because the risk of major bleeding associated with the treatment outweighs its benefits. In such cases, thrombolysis should be considered only as a rescue intervention if haemodynamic decompensation develops. Catheter-directed pharmaco-logical and pharmaco-mechanical techniques ensure swift recovery of echocardiographic and haemodynamic parameters and may be characterized by better safety profile than systemic thrombolysis. For survivors of acute PE, little is known on the effects of reperfusion therapies on the risk of chronic functional and haemodynamic impairment. In intermediate-risk PE patients, available data suggest that systemic thrombolysis may have little impact on long-term symptoms and functional limitation, echocardiographic parameters, and occurrence of chronic thromboembolic pulmonary hypertension. Ongoing and future interventional studies will clarify whether ‘safer’ reperfusion strategies may improve early clinical outcomes without increasing the risk of bleeding and contribute to reducing the burden of long-term complications after intermediate-risk PE.

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Section: Evolving Reperfusion Strategies To Improve Early Outcomesmentioning

confidence: 99%

“…MAUDE reports are included up to and including August 2019 after removal of duplicates and of reports not evaluable by the manufacturer in order to account for the limitations of the surveillance data 39 , 62 …”

Section: Evolving Reperfusion Strategies To Improve Early Outcomesmentioning

confidence: 99%

Immediate and late impact of reperfusion therapies in acute pulmonary embolism

Valerio

Klok

2019

European Heart Journal Supplements

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“…MAUDE (Manufacturer and User Facility Device Experience) information from FDA (U.S. Food and Drug Administration) database is open to public providing information about medical device adverse events and product problems submitted by manufacturers, importers and device user facilities in the past 10 years. Ensign and Cohen (2017) have done a detailed study on MAUDE database website regarding its content, structure and technical consideration to manage information from the database and mention that MAUDE is a valuable database for some actionable analytics and would provide a practical insight for design guidelines. The reports in MAUDE database are categorized based on problem type, product class, event type, brand name and dates of report received as shown in Figure 3 and is helpful in finding specific adverse event.…”

Section: Maude Databasementioning

confidence: 99%

Tailor-Made Design Guidelines for Human Factors and Usability for Medical Device Application: A Proposed Methodology

Nagarajan¹,

Silva²

2019

Proc. Int. Conf. Eng. Des.

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With increasing level of advancement and complexity in medical devices, there is a need for methodology, tools and techniques in practice to integrate Human Factors and Usability (HF/U) elements in design due to its increasing diversity of users and rapidly changing interface types. This paper proposes a methodology to develop a tailor-made HF/U design guidelines for medical device development with various sources and to develop heuristics for evaluation and score the product usability throughout the development process.

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“…The United States Food & Drug Administration (FDA) passively monitors medical device performance and safety through submitted medical device reports (MDRs) in the Manufacturer and User Facility Device Experience (MAUDE) database. 1,2 The MDRs contain the type of adverse event (i.e., injury, device malfunction or death), the device, manufacturer and a free text narrative of the event. A theoretical advantage of using a centralized device database over a single-institution’s experience is a wider breadth and depth of incidents including all manufacturers of approved medical devices in all fields of medicine and surgery.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Pattern Detection in Cochlear Implant FDA Adverse Event Reports

Crowson

Hamour

Lin

et al. 2020

Preprint

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Importance: The United States Food & Drug Administration (FDA) passively monitors medical device performance and safety through submitted medical device reports (MDRs) in the Manufacturer and User Facility Device Experience (MAUDE) database. These databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. Objectives: The objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants. Design: The MDRs for the top three CI manufacturers by volume from January 1st 2009 to August 30th 2019 were retained for the analysis. Natural language processing was used to measure the importance of specific words. Four supervised machine learning algorithms were used to predict which adverse event narrative description pattern corresponded with a specific cochlear implant manufacturer and adverse event type - injury, malfunction, or death. Setting: U.S. government public database. Participants: Adult and pediatric cochlear patients. Exposure: Surgical placement of a cochlear implant. Main Outcome Measure: Machine learning model classification prediction accuracy (% correct predictions). Results: 27,511 adverse events related to cochlear implant devices were submitted to the MAUDE database during the study period. Most adverse events involved patient injury (n = 16,736), followed by device malfunction (n = 10,760), and death (n = 16). Submissions to the database were dominated by Cochlear Corporation (n = 13,897), followed by MedEL (n = 7,125), and Advanced Bionics (n = 6,489). The random forest, linear SVC, naive Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & Relevance: Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.

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A Primer to the Structure, Content and Linkage of the FDA’s Manufacturer and User Facility Device Experience (MAUDE) Files

Cited by 21 publications

References 29 publications

Immediate and late impact of reperfusion therapies in acute pulmonary embolism

Immediate and late impact of reperfusion therapies in acute pulmonary embolism

Tailor-Made Design Guidelines for Human Factors and Usability for Medical Device Application: A Proposed Methodology

Machine Learning for Pattern Detection in Cochlear Implant FDA Adverse Event Reports

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