Improving the patient experience of hemodialysis vascular access decision making

改善血液透析血管通路决策的患者体验

• 批准号: 10693330
• 负责人: Karen Woo
• 金额: $ 43.99万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693330
• 关键词: Age; Anatomy; Arteriovenous fistula; Attitude; Bayesian Network; Blood Vessels; Calibration; Catheters; Characteristics; Client satisfaction; Clinical; Collaborations; Data; Data Set; Data Sources; Decision Making; Development; Dialysis procedure; Disease Outcome; Disparate; Educational Materials; End stage renal failure; Face; Failure; Feedback; Fistula; Foundations; Goals; Guidelines; Health Services Accessibility; Health system; Hemodialysis; Individual; Information Systems; Institution; Interview; Kidney; Kidney Diseases; Life; Link; Logistic Regressions; Maintenance; Medicare claim; Methods; Modeling; Obesity; Outcome; Patient observation; Patients; Perception; Performance; Peripheral Vascular Diseases; Physicians; Population; Procedures; Process; Prognostic Factor; Provider; Qualitative Methods; Race; Recommendation; Registries; Research; Sensitivity and Specificity; Structure; Surgeon; Testing; United States; Update; Venous; adverse outcome; arteriovenous graft; cohort; comorbidity; data registry; design; education access; evidence base; experience; frailty; improved; innovation; large scale data; machine learning method; multiple data sources; novel; operation; outcome prediction; patient engagement; patient response; preference; prognostic model; provider communication; random forest; satisfaction; sex; sociodemographics; statistical and machine learning; trustworthiness

Project Summary/Abstract Patients with end-stage kidney disease (ESKD), who use hemodialysis as their kidney replacement method, require vascular access in the form of an arteriovenous fistula, arteriovenous graft, or central venous catheter to receive life-sustaining hemodialysis. Providers and patients face selection of a vascular access type without adequate evidence of likely outcomes. To overcome this key barrier, the goal of this R01 proposal is to optimize the patient experience of vascular access decision-making by a) developing an interactive, evidence-based guide to vascular access outcomes that incorporates a prognostic model for short and long-term outcomes of vascular access and b) identifying best practices for utilization of the guide during the clinician-patient encounter. To do so, a novel, large-scale data source that contains multi-institutional granular data regarding vascular access operations and their short and long-term outcomes will be created by linking the Vascular Quality Initiative Vascular Access Registry (VQIVAR) to the United States Renal Data Systems Registry (USRDS) and Medicare claims. Prognostic models will be developed, by using traditional statistical approaches (e.g., logistic regression, Kaplan-Meier estimates) and machine learning methods (e.g., Bayesian networks, random forests) to predict outcomes that are meaningful to patients (revision procedures, repeat vascular access operation), and compare these models using technical metrics (e.g., sensitivity/specificity). The best-performing models will be selected and tested for external validity in a local UCLA population. Simultaneously, a mixed-methods approach will be used to engage patient and provider stakeholders to collaborate in creation and implementation of the proposed guide to vascular access outcomes, assessing the: 1) preferred means of communication with the clinician during the vascular access decision-making encounter; 2) optimal methods for incorporating the guide (including the prognostic model) into the decision-making process; and 3) satisfaction with iterative versions of the guide. The Specific Aims are: Aim 1 Design, evaluate and test the externally validity of the prognostic models for hemodialysis vascular access outcomes, to be used in vascular access decision-making, generated from VQIVAR data linked to USRDS and Medicare claims using statistical and machine learning methods and validated in a UCLA cohort with model calibration. Aim 2 Identify best practices for the clinician-patient vascular access decision-making interaction by using a mixed methods approach that includes individual interviews, direct observation, and quantitative satisfaction and preference scales. Aim 3 Create and refine an interactive guide to vascular access outcomes based on the best-performing prognostic model created in Aim 1, that allows for personalization with each patient’s characteristics, by engaging patient and provider stakeholders in an iterative fashion to incorporate their feedback and arrive at a final guide.

项目总结/摘要 使用血液透析作为肾脏替代方法的终末期肾病（ESKD）患者， 需要动静脉瘘、动静脉移植物或中心静脉导管形式的血管通路， 接受维持生命的血液透析提供者和患者面临血管通路类型的选择， 有足够的证据证明可能的结果。为了克服这一关键障碍，本R 01提案的目标是优化 血管通路决策的患者经验，a）开发一个互动的、基于证据的 血管通路结局指南，包含短期和长期结局的预后模型 血管通路，以及B）识别在临床医生-患者接触期间使用指南的最佳实践。 为此，需要一种新型的大规模数据源，其中包含有关血管的多机构粒度数据， 将通过将血管质量倡议（Vascular Quality Initiative） 美国肾脏数据系统登记研究（USRDS）和医疗保险的血管通路登记研究（VQIVAR） 索赔将通过使用传统的统计方法（例如，逻辑回归， Kaplan-Meier估计）和机器学习方法（例如，贝叶斯网络，随机森林）来预测 对患者有意义的结局（翻修手术、重复血管通路手术），并比较 这些模型使用技术指标（例如，灵敏度/特异性）。将选出表现最佳的车型 并在当地加州大学洛杉矶分校的人群中进行了外部效度测试。 同时，将采用混合方法，让患者和提供者利益相关者参与， 合作创建和实施拟议的血管通路结局指南，评估： 1)在血管通路决策过程中与临床医生沟通的首选方式; 2)将指南（包括预测模型）纳入决策过程的最佳方法; 以及3）对指南的迭代版本的满意度。具体目标是： 目的1设计、评价和检验血液透析血管预后模型的外部效度 入路结局，用于血管入路决策，由VQIVAR数据生成， USRDS和Medicare索赔使用统计和机器学习方法，并在UCLA队列中得到验证 模型校准。 目的2通过以下方式确定临床医生-患者血管通路决策交互的最佳实践： 使用混合方法，包括个人访谈，直接观察和定量分析。 满意度和偏好量表。 目标3基于最佳性能创建和完善血管通路结局的交互式指南 在目标1中创建的预后模型，通过参与， 患者和提供者利益相关者以迭代的方式整合他们的反馈并达成最终指南。