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：这项提议旨在支持Sanket Dhruva，MD，MHS，a 旧金山退伍军人管理局心脏病专家，加州大学医学助理教授， 旧金山成为独立的退伍军人健康服务研究员，具备必要的培训和经验 进行创新研究并开发干预措施，以提高退伍军人心血管疾病的安全性 植入式电子设备(CIED：起搏器和植入型心律转复除颤器[ICD])。连 尽管55,000名退伍军人中有超过10%的人患有与CIED相关的并发症， 尚未使用退伍军人管理局的数据系统进行任何系统评估以确定故障的CIED引线。 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 定性方法，使他能够产生可操作的、高质量的证据来告知退伍军人管理局心脏 电生理学家为退伍军人植入最安全的装置。这项研究还将使他能够确定 已经植入退伍军人体内但有失败风险的CIED导线，从而通知 对个别退伍军人避免失败(如不适当的休克和死亡)的临床后遗症的策略。 该提案直接与VHA指令1189(于1月发布)中规定的业务优先顺序保持一致 2020)“监测CIEDs的安全性”，HSR&发展学习型医疗体系的优先事项和改进 退伍军人护理质量和安全，并支持VHA成为高可靠性组织的优先事项。 创新：这项研究是创新的，通过应用先进的统计方法来利用 退伍军人综合纵向数据库，退伍军人国家心脏装置监测 计划(NCDSP)，包括时间密集的CIED生成的数据，以解决大规模、复杂的 识别CIED引线故障的问题。此外，这项研究还提供了关于未被探索的 医生选择制造商和植入装置的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 退伍军人中的电子设备(CIED)铅模型。 H1：我们将检测到一个或多个CIED Lead模型在统计和临床上具有显著更高的故障 与同类型的其他导线相比时的比率(例如，与所有其他ICD导线相比时的ICD导线)。 目的2：建立退伍军人全因CIED导线失效风险预测模型 从CIED远程监测数据到重复测量的有监督机器学习方法。 H2：风险预测模型将以高分辨率检测引线故障(曲线下面积[AuC]≥0.85) 并在评估后3个月和12个月进行适当的校准。 目标3：进行一项试点研究，以确定学术细节和审计的效果 退伍军人体内植入特定CIED导联模型的反馈干预。 H3：干预后，退伍军人将更多地被植入与最低层相关的铅模型 失败率。 方法：目标1将使用序贯倾向分数调整的模拟前瞻性生存分析 应用于链接到退伍军人管理局的公司数据仓库和医疗保险数据的NCDSP的数据集。目标2将 将两种有监督的机器学习技术-弹性网络和随机森林-应用于季度患者- 从CIED生成数据以创建预测模型。目标3将包括心脏的定性访谈 电生理学家关于设备的选择以及AN的开发、实施和评估 对3个VISN中的心脏电生理学家进行学术详述和审计及反馈干预。 实施：这项研究将使Dhruva博士成为一名独立的退伍军人事务部HSR&D研究员 开展研究，以改善患有CIEDs的退伍军人和那些将在未来接受CIEDs的退伍军人的结果。