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：该建议旨在支持Sanket Dhruva，医学博士，MHS的职业发展 弗吉尼亚州旧金山的员工心脏病专家和加利福尼亚大学医学助理教授 旧金山进入独立的VA卫生服务研究人员，并进行了必要的培训和经验 进行创新的研发干预措施，以提高患有心血管的退伍军人的安全 可植入的电子设备（CIEDS：起搏器和植入式心脏扭曲器除颤器[ICDS]）。甚至 尽管在55,000名退伍军人中，有超过10％的弗吉尼亚州遭受了与CIED相关的并发症 使用VA的数据系统识别出任何系统评估来识别失败的CIED导线。 意义/影响：这项研究将缩小Dhruva博士在生物统计学，数据科学和 定性方法，使他能够生成可行的高质量证据，以告知VA心脏 电生理学家将最安全的设备植入退伍军人中。这项研究还将使他能够确定 已经植入退伍军人但有失败风险的CIED线索，从而告知 避免对单个退伍军人的失败后遗症（例如不适当的冲击和死亡）的策略。 该提案直接与VHA指令1189中规定的运营优先级保持一致（1月份出版 2020年）“监控CIEDS的安全”，HSR＆D的优先级并改善 退伍军人的护理和安全质量，并支持VHA成为一个高可靠性组织的优先事项。 创新：这项研究通过应用高级统计方法来利用A具有创新性 VA国家心脏设备监视的退伍军人的全面，纵向数据库 程序（NCDSP），包括暂时致密的CIED生成数据，以解决大规模的复杂 识别CIED铅失败的问题。此外，这项研究还提供了有关意外的信息 制造商的物理选择和植入设备模型的问题以及安全数据的作用。 具体目的：目标1：比较不同心血管植入的风险调整的失败率 退伍军人中的电子设备（CIED）铅模型。 H1：我们将检测一个或多个CIED铅模型，其统计学和临床​​上的失败明显更高 与相同类型的其他导线相比（例如，与所有其他ICD引线相比，ICD铅）的速率。 目的2：通过申请，建立退伍军人中全因CIED铅失败的风险预测模型 监督的机器学习方法是从CIED远程监视数据中重复措施。 H2：风险预测模型将通过高歧视检测铅失败（曲线下的面积≥0.85） 并在评估后3个月零12个月进行足够的校准。 目标3：进行试点研究以确定学术细节和审核的效果 对退伍军人植入的特定CIED铅模型的反馈干预。 H3：干预后，退伍军人经常会植入与最低相关的铅模型 故障率。 方法论：AIM 1将使用顺序的承诺得分调整的模拟前瞻性生存分析 应用于链接到VA公司数据仓库和Medicare数据的NCDSP数据集。 AIM 2意志 将两种监督的机器学习技术，弹性网和随机森林应用于季度患者 - 从CIEDS生成数据以创建预测模型。 AIM 3将包括心脏的定性访谈 关于设备选择以及开发，实施和评估的电生理学家 在3个VISN中为心脏电生理学家提供的学术细节，审核和反馈干预。 实施：这项研究将使Dhruva博士成为独立的VA HSR＆D调查员 进行研究，以改善CIEDS的退伍军人和将来会收到一个人的结果。