Incorporating Learning Effects into Medical Device Active Safety Surveillance Methods

将学习效果纳入医疗器械主动安全监测方法

基本信息

批准号：
10352373
负责人：
MICHAEL E. MATHENY
金额：
$ 75.45万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10352373
关键词：
Achievement Acute Address Adverse event Algorithm Design Algorithms Blinded Blood Vessels Cardiovascular system Caring Characteristics Chronic Clinical Clinical Data Complex Data Data Aggregation Data Analytics Data Set Detection Development Device Designs Device Safety Devices Early Diagnosis Elements Environment Etiology Evaluation Event Feedback Generations Implant Injections Injury Institution Investigation Knowledge Lead Learning Literature Manufacturer Name Medical Device Medical Device Designs Medical Device Safety Methodology Methods Outcome Patient-Focused Outcomes Patients Performance Physicians Process Provider Public Health Publishing Registries Reporting Risk Safety Signal Transduction Specific qualifier value Statistical Models Structure Surveillance Methods Time Training United States Validation Variant adverse outcome algorithm development cardiovascular risk factor clinical heterogeneity design expectation experience high risk implantable device improved machine learning model novel open source patient population post-market prospective safety outcomes simulation surveillance strategy surveillance study systems research tool

Achievement Acute Address Adverse event Algorithm Design Algorithms Blinded Blood Vessels Cardiovascular system Caring Characteristics Chronic Clinical Clinical Data Complex Data Data Aggregation Data Analytics Data Set Detection Development Device Designs Device Safety Devices Early Diagnosis Elements Environment Etiology Evaluation Event Feedback Generations Implant Injections Injury Institution Investigation Knowledge Lead Learning Literature Manufacturer Name Medical Device Medical Device Designs Medical Device Safety Methodology Methods Outcome Patient-Focused Outcomes Patients Performance Physicians Process Provider Public Health Publishing Registries Reporting Risk Safety Signal Transduction Specific qualifier value Statistical Models Structure Surveillance Methods Time Training United States Validation Variant adverse outcome algorithm development cardiovascular risk factor clinical heterogeneity design expectation experience high risk implantable device improved machine learning model novel open source patient population post-market prospective safety outcomes simulation surveillance strategy surveillance study systems research tool

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项目摘要

Implantable medical devices have revolutionized contemporary cardiovascular care, and are used in a wide spectrum of acute and chronic cardiovascular conditions. However, medical device design fault or incorrect use may lead to significant risk of patient injury and represents an important preventable public health risk in the United States. To help identify device-related safety issues, a strategy of active, prospective, post-market safety surveillance has been recommended by the FDA, and evaluated methodologically. This type of surveillance offers significant advantages over traditional adverse event reporting strategies. However, all such approaches are challenged by the need to incorporate learning effects into expectations regarding safety. These learning impacts been repeatedly shown to have dramatic impacts on outcomes during early device experience. Quantifying learning effects on the outcomes associated with high-risk cardiovascular devices will improve our understanding of intrinsic device performance, thereby identifying patient populations best treated with such devices while simultaneously providing necessary feedback to device manufacturers to support iterative improvement in device design. Separately, understanding the impacts of learning may identify opportunities for targeted training as well as help to tease apart institutional and operator characteristics that may accelerate the achievement of optimal outcomes in the use of the specific cardiovascular device. This proposal seeks to extend the previously validated, open-source, active, prospective device safety surveillance tool, by developing and validating robust learning curve (LC) detection and quantification algorithms, designed to simultaneously account for the effects at the operator and institutional levels. We propose a “blinded” development strategy, in which one team will generate robust synthetic clinical data simulator with LC impacts, and the other team develops and applies LC detection and quantification algorithms, without knowledge of the underlying relationships, determine performance and accuracy through sequential refinement and validation steps. We propose to formally validate the optimized LC tools in real-world data through re-analysis of previously published LC effects on transcatheter valves and vascular closure devices using national cardiovascular registries. In addition, the LC tools will be incorporated into two active, prospective device safety surveillance studies of novel implantable cardiovascular devices using large clinical registries.

可植入的医疗设备彻底改变了当代心血管护理，并且用于广泛的急性和慢性心血管疾病。但是，医疗设备设计故障或不正确的使用可能会导致患者受伤的重大风险，并表示在美国，重要的可预防的公共卫生风险。帮助识别与设备相关的安全问题，一种积极，潜在的，市场后的安全监视的策略由FDA推荐并进行方法论评估。这种监视提供与传统不利事件报告策略相比，具有显着优势。但是，所有这些将学习效果纳入期望的需要挑战关于安全。这些学习影响反复证明对在早期设备体验中的结果。量化学习对结果的影响与高风险心血管设备相关设备性能，从而确定最好用此类设备治疗的患者人群同样，向设备制造商提供必要的反馈以支持迭代设备设计的改进。单独了解学习的影响可能会识别有针对性培训的机会，并帮助教授机构和运营商分开可能会加速使用最佳结果的特征特定的心血管装置。该建议旨在扩展先前验证的，开源，活跃，潜在的设备安全监视工具，通过开发和验证鲁棒学习曲线（LC）检测和定量算法，旨在简单地说明效果操作员和机构层面。我们提出了一种“盲人”的发展策略，其中一个团队将生成具有LC撞击的强大合成临床数据模拟器，另一个团队开发并应用LC检测和量化算法，不了解潜在的关系，通过顺序完善确定性能和准确性和验证步骤。我们建议在实际数据中正式验证优化的LC工具通过重新分析先前发表的LC对经导管和血管的影响使用国家心血管注册机构关闭设备。此外，LC工具将是纳入了两项活跃的前瞻性设备安全监视研究，可用于新型植入使用大型临床注册机构的心血管设备。