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

项目总结 心血管疾病(CVD)是肾移植(KT)受者死亡的主要原因。 功能正常的同种异体移植。KT患者的心血管疾病发病率和死亡率是普通患者的3至5倍。 人群中，在肾移植的三年内，11%的患者将有心肌梗死 脑梗塞。有证据表明，这种增加的风险是由多条交叉途径推动的，这些途径有助于 心血管疾病，包括免疫抑制药物的代谢副作用，慢性肾脏病史 疾病和容量超载、当前的同种异体移植功能、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特有的心血管疾病风险预测模型结合了已知的 风险因素尚未形成。现有数据集缺乏完全捕获细粒度CVD事件的能力 表征纵向生物标记物的作用，或结合传统的、移植特有的和 风险评估中的社会经济因素。此外，目前的研究预测了不同的复合型心血管疾病 结果混淆了对预测风险的解释，并突显了缺乏标准的心血管疾病结果 评估这一人群的负担。最后，除了潜在的风险误判，现有的模型在很大程度上仍然 在临床环境中未使用，因为它们需要将数据手动输入在线计算器。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群KT患者和 回溯捕获其高度细粒度的纵向数据，以评估心血管疾病风险。我们已经成功地使用了 该平台为另外两个患者群体构建风险预测模型，并将临床工具嵌入 实时使用的健康记录。因此，我提出的研究策略是：1)量化累积发病率 评估我们KT人群中的心血管疾病事件，并定义评估有意义的风险的最佳综合结果，2) 在KT后识别和表征与心血管疾病相关的风险因素， 纵向生物标记物轨迹和社会经济因素，以及3)实施和试行个性化的 心血管疾病-嵌入我们健康记录中的风险预测工具。拟议的工作将产生一个全面和 专用于KT人群的可移动式风险预测工具，其影响将在多个 机构。我们的发现将允许患者和提供者参与共同的决策并确定 最终将改善这一独特人群的结果的干预目标。这项工作将是 立即适用于心血管风险过高的KT患者及其医生必须优化 保持同种异体移植物健康和最大限度减少心血管疾病之间的平衡。

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.