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: 10312661
• 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/10312661
• 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; 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 Name; 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 Assessment; Role; Safety; San Francisco; Savings; Shock; Signal Transduction; Statistical Methods; Stress; Surveillance Program; Survival Analysis; System; Techniques; Training; Translating; United States Food and Drug Administration; Universities; Veterans; adjudicate; 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 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，MD，MHS，a 旧金山弗朗西斯科的心脏病专家和加州大学的医学助理教授， 弗朗西斯科成为一个独立的退伍军人管理局卫生服务研究员与培训和经验的必要 开展创新研究，开发干预措施，提高心血管疾病退伍军人的安全性 植入式电子器械（CIED：起搏器和植入式心律转复除颤器[ICD]）。甚至 虽然超过10%的55,000退伍军人其次是退伍军人遭受CIED相关的并发症， 还没有任何系统的评价，以确定失败的CIED铅使用VA的数据系统。 意义/影响：这项研究将填补Dhruva博士在生物统计学，数据科学和生物统计学方面的知识空白。 定性方法，使他能够生成可操作的高质量证据， 电生理学家在退伍军人中植入最安全的设备。这项研究还将使他能够识别 CIED电极导线已植入退伍军人体内，但存在失效风险，从而告知 为个别退伍军人避免临床失败后遗症（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年），以“监测CIED的安全性”，HSR&D学习医疗保健系统的优先事项，并改善 退伍军人护理和安全的质量，并支持VHA的优先事项成为一个高可靠性的组织。 创新：这项研究是创新的，通过其先进的统计方法的应用，以利用 全面的，纵向数据库的退伍军人与CIEDs，弗吉尼亚州国家心脏设备监测 程序（NCDSP），包括时间密集的CIED生成的数据，以解决大规模的，复杂的 识别CIED引线故障的问题。此外，这项研究提供了有关未开发的信息。 医生选择制造商和植入器械型号的问题以及安全性数据的作用。 具体目的：目的1：比较不同心血管植入式心脏瓣膜植入物的风险调整后失败率 电子设备（CIED）在退伍军人中的领先地位。 H1：我们将检测一个或多个CIED电极导线型号，其故障率在统计学和临床上显著更高 与同类型的其他电极导线相比的比率（例如，与所有其他ICD电极导线相比的ICD电极导线）。 目的2：通过应用，开发退伍军人全因CIED电极导线失效的风险预测模型。 有监督的机器学习方法从CIED远程监测数据中重复测量。 H2：风险预测模型将以高区分度检测电极导线失效（曲线下面积[AUC] ≥0.85） 并在评估后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计的效果， 对植入退伍军人体内的特定CIED电极导线型号进行反馈干预。 H3：干预后，退伍军人将更经常地植入与最低 失败率 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NCDSP的数据集，该数据集链接到VA的企业数据仓库和医疗保险数据。目标2将 应用两种监督机器学习技术，弹性网络和随机森林，以季度病人- 从CIED生成数据以创建预测模型。目标3将包括心脏病患者的定性访谈 电生理学家关于设备选择和开发，实施和评估的一个 3个VISN中心脏电生理学家的学术详细说明、审计和反馈干预。 实施：这项研究将使Dhruva博士成为独立的VA HSR&D研究员， 进行研究，以改善与CIED退伍军人和那些谁将在未来收到一个结果。