评估新兴骨科技术的安全性-猫眼课题宝

评估新兴骨科技术的安全性

批准号：

8293843

负责人：

Sohail K Mirza

金额：

$10.13万

依托单位：

DARTMOUTH COLLEGE

依托单位国家：

美国

项目类别：

财政年份：

资助国家：

美国

项目状态：

未结题

起止时间：

至

来源：

https://reporter.nih.gov/project-details/8293843

关键词：

Address Age Algorithms Area Under Curve Back Benchmarking Biological Products Bone Growth Bone Morphogenetic Proteins Cessation of life Clinical Clinical Research Comorbidity Complex Complication Data Data Reporting Databases Development Device Approval Device Safety Devices Diagnosis Early Diagnosis Effectiveness FDA approved Face Hospitals Implant Infection Inpatients Instruction Life Marketing Measures Medical Medical Device Methods Monitor National Institute of Arthritis and Musculoskeletal and Skin Diseases Operative Surgical Procedures Orthopedic Surgery procedures Orthopedics Patients Performance Population Procedures Publications Publishing Receiver Operator Characteristics Relative (related person)Repeat Surgery Reporting Research Risk Risk Factors Safety Sensitivity and Specificity Signal Transduction Spinal Spinal Fusion Statistical Models Surface Surgeon Surgical complication Symptoms System Techniques Technology Time United States Centers for Medicare and Medicaid Services Validation base clinical practice experience innovation instrumentation mortality multidisciplinary new technology operation population based prospective retinal rods spine bone structure theories trend

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中文摘要

翻译

项目总结(见说明)：这一开发项目旨在促进早期发现与新的整形外科设备相关的严重并发症。该项目的创新方面包括验证管理衍生的安全措施，使用FDA批准的数据作为监测安全性的基准，以及使用随时可用的基于人群的数据库来评估新兴外科设备的安全性。该项目实施了统计建模技术，利用医疗索赔数据来持续评估新设备的风险，同时获得关于重复手术、感染、其他并发症和死亡的更多数据。我们将通过研究骨形态发生蛋白的经验来演示这种方法，骨形态发生蛋白是一种生物制剂，用于脊柱融合手术。

英文摘要

PROJECT SUMMARY (See instructions): This developmental project seeks to facilitate early detection of serious complications associated with new orthopedic devices. Innovative aspects of the project include validation of administratively-derived safety measures, use of FDA-approval data as a benchmark for monitoring safety, and use of readily-available population-based databases to evaluate the safety of emerging surgical devices. The project implements statistical modeling techniques that leverage medical claims data to continually assess the risks of new devices as additional data is accured on repeat surgery, infection, other complications, and death. We will demonstrate the approach by examining the experience with bone morphogenetic protein, a biological agent used as an adjuncent to spinal fusion surgery

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