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

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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.