Improving Safety of Cardiovascular Implantable Electronic Devices in Veterans

提高退伍军人心血管植入电子设备的安全性

• 批准号: 10552536
• 负责人: Sanket S Dhruva
• 金额: --
• 依托单位: VETERANS AFFAIRS MED CTR SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552536
• 关键词: Academic Detailing; Address; Adverse event; Anxiety; Area Under Curve; Arrhythmia; Assessment tool; Biometry; Calibration; California; Cardiac; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cessation of life; Clinical; Complex; Data; Data Analytics; Data Science; Data Set; Data Sources; Development; Device Safety; Devices; Discrimination; Elasticity; Electronic Health Record; Electronics; Evaluation; Event; Failure; Feedback; Future; Goals; Health; Health Services; Healthcare Systems; Implant; Implantable Defibrillators; Individual; Information Systems; Intervention; Interview; Knowledge; Lead; Learning; Life; Link; Malignant - descriptor; Mandatory Programs; Manufacturer; Measures; Mechanics; Medicare; Medicine; Mentors; Methodology; Modeling; Monitor; Pacemakers; Pain; Patient risk; Patients; Persons; Physicians; Pilot Projects; Population; Probability; Publications; Publishing; Qualitative Methods; Quality of Care; Quality of life; Registries; Repeat Surgery; Reporting; Research; Research Personnel; Resources; Risk; Risk Adjustment; Risk Assessment; Role; Safety; San Francisco; Shock; Signal Transduction; Statistical Methods; Stress; Surveillance Program; Survival Analysis; System; Techniques; Training; Translating; United States Food and Drug Administration; Universities; Veterans; adjudication; adverse outcome; cardiac device; cardiac implant; career development; comparative; data warehouse; experience; follow-up; implantation; implementation science; implementation strategy; improved; improved outcome; individual patient; innovation; longitudinal database; machine learning method; novel; post intervention; predictive modeling; prevent; professor; programs; prospective; random forest; remote monitoring; risk mitigation; risk prediction model; skills; statistics; supervised learning; transmission process

Background: This proposal is intended to support the career development of Sanket Dhruva, MD, MHS, a Staff Cardiologist at the San Francisco VA and Assistant Professor of Medicine at the University of California, San Francisco into an independent VA health services researcher with the training and experience necessary to conduct innovative research and develop interventions that improve safety of Veterans with cardiovascular implantable electronic devices (CIEDs: pacemakers and implantable cardioverter defibrillators [ICDs]). Even though more than 10% of the 55,000 Veterans followed by VA have suffered CIED-related complications, there has not been any systematic evaluation to identify failed CIED leads using VA’s data systems. Significance/Impact: This research will close Dr. Dhruva’s knowledge gaps in biostatistics, data science, and qualitative methods, enabling him to generate actionable, high-quality evidence to inform VA cardiac electrophysiologists to implant the safest devices in Veterans. This research will also enable him to identify CIED leads that have already been implanted in Veterans but are at risk for failure, thereby informing strategies to avoid clinical sequelae of failure (such as inappropriate shocks and death) for individual Veterans. This proposal is directly aligned with operational priorities set forth in VHA Directive 1189 (published in January 2020) to “monitor the safety of CIEDs,” HSR&D Priorities of a Learning Healthcare System and improving Veteran Quality of Care and Safety, and supports VHA’s priority of becoming a High-Reliability Organization. Innovation: This research is innovative through its application of advanced statistical methods to leverage a comprehensive, longitudinal database of Veterans with CIEDs, the VA National Cardiac Device Surveillance Program (NCDSP), including temporally dense CIED-generated data, to address the large-scale, complex problem of identifying CIED lead failure. Additionally, this research provides information about the unexplored question of physician selection of manufacturer and model of device to implant and the role of safety data. Specific Aims: Aim 1: To compare risk-adjusted failure rates of different cardiovascular implantable electronic device (CIED) lead models among Veterans. H1: We will detect one or more CIED lead models with statistically and clinically significantly higher failure rates when compared to other leads of the same type (e.g. ICD lead when compared to all other ICD leads). Aim 2: To develop risk prediction models of all-cause CIED lead failure among Veterans by applying supervised machine learning methods to repeated measures from CIED remote monitoring data. H2: Risk prediction models will detect lead failure with high discrimination (area under the curve [AUC] ≥0.85) and adequate calibration at 3 months and 12 months post-assessment. Aim 3: To conduct a pilot study to determine the effect of an academic detailing and audit and feedback intervention on the specific CIED lead models implanted in Veterans. H3: Post-intervention, Veterans will more often be implanted with lead models associated with the lowest failure rates. Methodology: Aim 1 will use sequential propensity score-adjusted simulated prospective survival analyses applied to a dataset of the NCDSP linked to VA’s Corporate Data Warehouse and Medicare data. Aim 2 will apply two supervised machine learning techniques, elastic net and random forests, to quarterly patient- generated data from CIEDs to create prediction models. Aim 3 will include qualitative interviews of cardiac electrophysiologists about device selection and the development, implementation, and evaluation of an academic detailing and audit and feedback intervention for cardiac electrophysiologists in 3 VISNs. Implementation: This research will enable Dr. Dhruva to become an independent VA HSR&D investigator who conducts research to improve outcomes for Veterans with CIEDs and those who will receive one in the future.

背景：本提案旨在支持 Sanket Dhruva（医学博士、MHS）的职业发展， 旧金山退伍军人管理局的心脏病专家和加利福尼亚大学的医学助理教授， 旧金山成为一名独立的退伍军人事务部卫生服务研究员，并具备必要的培训和经验 进行创新研究并开发干预措施，以提高患有心血管疾病的退伍军人的安全 植入式电子设备（CIED：起搏器和植入式心脏复律除颤器 [ICD]）。 尽管 VA 跟踪的 55,000 名退伍军人中超过 10% 患有 CIED 相关并发症，但 尚未使用 VA 的数据系统进行任何系统评估来识别失败的 CIED 线索。 意义/影响：这项研究将弥补 Dhruva 博士在生物统计学、数据科学和生物统计学方面的知识差距。 定性方法，使他能够生成可操作的高质量证据来告知 VA 心脏 电生理学家将最安全的设备植入退伍军人体内，这项研究也将使他能够识别。 CIED 导线已植入退伍军人体内，但有失败的风险，从而告知 避免个别退伍军人因失败而产生临床后遗症（例如不适当的电击和死亡）的策略。 该提案与 VHA 指令 1189（1 月发布）中规定的运营优先事项直接一致 2020）以“监控 CIED 的安全”、学习医疗保健系统的 HSR&D 优先事项并改进 经验丰富的护理和安全质量，并支持 VHA 成为高可靠性组织的优先事项。 创新：这项研究通过应用先进的统计方法来利用 拥有 CIED（退伍军人管理局国家心脏装置监测）的退伍军人的综合纵向数据库 程序 (NCDSP)，包括 CIED 生成的时间密集数据，以解决大规模、复杂的问题 此外，这项研究还提供了有关未探索的信息。 医生选择植入设备的制造商和型号以及安全数据的作用的问题。 具体目标： 目标 1：比较不同心血管植入物的风险调整失败率 电子设备 (CIED) 在退伍军人中处于领先地位。 H1：我们将检测一个或多个 CIED 主要模型，其在统计和临床上的失败率显着较高 与同类型的其他导联相比（例如，ICD 导联与所有其他 ICD 导联相比）。 目标 2：通过应用开发退伍军人全因 CIED 导联失败的风险预测模型 使用监督机器学习方法对 CIED 远程监控数据进行重复测量。 H2：风险预测模型将以高辨别力检测引线故障（曲线下面积 [AUC] ≥0.85） 评估后 3 个月和 12 个月进行充分校准。 目标 3：进行试点研究，以确定学术细节和审核的效果 对植入退伍军人体内的特定 CIED 引线模型进行反馈干预。 H3：干预后，退伍军人将更频繁地植入与最低水平相关的引线模型 故障率。 方法：目标 1 将使用序贯倾向评分调整的模拟前瞻性生存分析 应用于链接到 VA 企业数据仓库和 Medicare Aim 2 数据的 NCDSP 数据集。 将弹性网络和随机森林这两种监督机器学习技术应用于季度患者 目标 3 将包括对心脏的定性访谈。 电生理学家了解设备选择以及设备的开发、实施和评估 3 个 VISN 中心脏电生理学家的学术细节以及审核和反馈干预。 实施：这项研究将使 Dhruva 博士成为一名独立的 VA HSR&D 调查员， 进行研究以改善接受 CIED 的退伍军人和未来将接受 CIED 的退伍军人的结果。