Leveraging a novel health records platform to predict the development of cardiovascular disease following kidney transplantation

利用新型健康记录平台预测肾移植后心血管疾病的发展

• 批准号: 10679322
• 负责人: Mary Grace Bowring
• 金额: $ 5.52万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10679322
• 关键词: Accounting; Acute; Address; Allografting; Area; Biological Markers; Calibration; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Cause of Death; Cessation of life; Chronic; Chronic Kidney Failure; Clinical; Complex; Computer software; Confusion; Consensus; Data; Data Set; Development; Discrimination; Disease; Disease Outcome; Disparate; Electronic Health Record; Engineering; Equilibrium; Evaluation; Event; Exclusion; Face; Frequencies; General Population; Goals; Health; Hospitals; Housing; Immunosuppression; Incidence; Income; Inflammation; Institution; Intervention; Interview; Kidney Diseases; Kidney Transplantation; Knowledge; Manuals; Mentors; Metabolic; Modeling; Modification; Morbidity - disease rate; Myocardial Infarction; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Physicians; Population; Provider; ROC Curve; Recording of previous events; Reduce health disparities; Research; Risk; Risk Estimate; Risk Factors; Risk Management; Severities; Socioeconomic Factors; Structure; System; Testing; Time; Transplant Recipients; Transplantation; Validation; Visualization software; Work; burden of illness; cardiovascular disorder risk; cardiovascular risk factor; clinical biomarkers; clinical decision-making; clinical implementation; cohort; data visualization; evidence based guidelines; experience; hazard; health record; high risk; improved; improved outcome; model building; mortality; novel; patient oriented; patient population; pilot test; predictive modeling; predictive tools; prevent; primary endpoint; risk prediction; risk prediction model; shared decision making; side effect; standard of care; time use; tool; transplant registry; usability

PROJECT SUMMARY Cardiovascular disease (CVD) is the leading cause of death among kidney transplant (KT) recipients with a functioning allograft. KT patients face a 3- to 5-fold higher risk of CVD morbidity and mortality than the general population, and within three years of kidney transplantation, 11% of these patients will have had a myocardial infarction. Evidence suggests that this increased risk is driven by multiple intersecting pathways contributing to CVD, including the metabolic side-effects of immunosuppression medications, a history of chronic kidney disease and volume overload, current allograft function, chronic and acute inflammation, and socioeconomic factors such as housing and income. Despite this, a KT-specific CVD-risk prediction model incorporating known risk factors has not been developed. Existing datasets lack the ability to capture granular CVD events, fully characterize contributions of longitudinal biomarkers, or incorporate traditional, transplant-specific, and socioeconomic factors in their risk estimation. Furthermore, current studies predict disparate composite CVD outcomes confusing the interpretation of predicted risk and highlighting the lack of a standard CVD outcome to assess burden in this population. Finally, beyond potential risk miscalculation, existing models remain largely unused in the clinical setting as they require manual input of data into an online calculator. To address this, we have leveraged a unique health records platform within our institution to identify a cohort of KT patients and retrospectively capture their highly granular longitudinal data to assess CVD risk. We have successfully used this platform to build risk prediction models for two other patient populations and embedded clinical tools into the health record for use in real time. Thus, my proposed research strategy is to 1) quantify the cumulative incidence of CVD events in our KT population and define the optimal compositive outcome to assess meaningful risk, 2) identify and characterize risk factors associated with CVD after KT accounting for time-varying disease states, longitudinal biomarker trajectories, and socioeconomic factors, and 3) implement and pilot-test an individualized CVD-risk prediction tool embedded in our health record. The proposed work will generate a comprehensive and transportable risk-prediction tool specific to the KT population with implications for dissemination across multiple institutions. Our findings will allow patients and providers to engage in shared decision-making and identify targets of intervention that will ultimately improve outcomes in this unique population. This work will be immediately applicable to KT patients burdened with excessive CVD risk and their physicians who must optimize the balance between maintaining allograft health and minimizing cardiovascular disease.

项目摘要 心血管疾病（CVD）是肾脏移植（KT）接受者的主要原因 功能同种异体移植。 KT患者面临的CVD发病率和死亡率高3至5倍 人口，以及肾脏移植的三年之内，这些患者中有11％患有心肌 梗塞。证据表明，这种增加的风险是由多个相交途径驱动的 CVD，包括免疫抑制药物的代谢副作用，慢性肾脏史 疾病和体积超负荷，当前同种异体功能，慢性和急性炎症以及社会经济 住房和收入等因素。尽管如此，KT特异性的CVD风险预测模型包括已知 尚未开发风险因素。现有数据集缺乏捕获颗粒状CVD事件的能力，完全 表征纵向生物标志物的贡献，或纳入传统，特定于移植的贡献 其风险估计中的社会经济因素。此外，当前的研究预测了不同的复合CVD 结果使人们对预测风险的解释感到困惑，并强调缺乏标准的CVD结果 评估该人群的负担。最后，超出潜在的风险错误估计，现有模型仍然在很大程度上仍然 在临床环境中未使用，因为它们需要将数据输入到在线计算器中。为了解决这个问题，我们 是否利用我们机构内的独特的健康记录平台来确定一群KT患者和 追溯捕获其高度颗粒状的纵向数据以评估CVD风险。我们已经成功使用了 这个平台为其他两个患者人群建立风险预测模型，并嵌入了临床工具 健康记录可实时使用。因此，我提出的研究策略是1）量化累积发生率 我们的KT人群中的CVD事件，并定义了评估有意义风险的最佳组合结果，2） 识别和表征与随时间变化疾病状态的KT后与CVD相关的危险因素， 纵向生物标志物轨迹和社会经济因素，以及3）实施和试点测试个性化 CVD风险预测工具嵌入了我们的健康记录中。拟议的工作将产生全面和 可运输风险预测工具针对KT人群，对跨多个的传播产生了影响 机构。我们的发现将使患者和提供者能够进行共同的决策并确定 干预目标最终将改善这种独特的人群的结果。这项工作将是 立即适用于负担过多CVD风险的KT患者及其必须优化的医生 维持同种异体健康与最小化心血管疾病之间的平衡。