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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要心血管疾病（CVD）是肾移植（KT）接受者死亡的主要原因有功能的同种异体移植物。 KT 患者的 CVD 发病率和死亡率比普通患者高 3 至 5 倍人口中，肾移植后三年内，这些患者中 11% 会出现心肌梗死梗塞。有证据表明，这种风险增加是由多种交叉途径导致的 CVD，包括免疫抑制药物的代谢副作用、慢性肾病史疾病和容量超负荷、当前同种异体移植功能、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 特定的 CVD 风险预测模型结合了已知的风险因素尚未制定。现有数据集缺乏全面捕获精细 CVD 事件的能力描述纵向生物标志物的贡献，或结合传统的、移植特异性的和风险评估中的社会经济因素。此外，目前的研究预测不同的复合 CVD 结果混淆了对预测风险的解释，并强调缺乏标准的 CVD 结果评估该人群的负担。最后，除了潜在的风险误算之外，现有模型在很大程度上仍然存在在临床环境中未使用，因为它们需要将数据手动输入到在线计算器中。为了解决这个问题，我们利用我们机构内独特的健康记录平台来识别一组 KT 患者，并且回顾性地捕获他们的高度精细的纵向数据来评估 CVD 风险。我们已经成功使用了该平台可为另外两个患者群体建立风险预测模型，并将临床工具嵌入到实时使用的健康记录。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 事件并定义最佳综合结果以评估有意义的风险，2) 在 KT 考虑随时间变化的疾病状态后，识别并描述与 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嵌入我们健康记录中的 CVD 风险预测工具。拟议的工作将产生全面和针对 KT 人群的可移植风险预测工具，对跨多个群体传播具有影响机构。我们的研究结果将使患者和提供者能够参与共同决策并确定干预目标最终将改善这一独特人群的结果。这项工作将是立即适用于承受过高 CVD 风险的 KT 患者及其必须优化的医生维持同种异体移植物健康和减少心血管疾病之间的平衡。