Dynamic Longitudinal Functional Models with Applications to the CRIC Study

Dynamic Longitudinal Functional Models with Applications to the CRIC Study The goals of this project are to develop novel dynamic longitudinal functional models and to apply them to the electrocardiographic (ECG) data that are measured repeatedly over time in the Chronic Renal Insufficiency Cohort (CRIC) study of individuals with chronic kidney disease (CKD). We will also develop real-time risk prediction algorithms to identify individuals at high-risk of cardiovascular diseases (CVD). The CRIC study is an ongoing study of individuals with chronic kidney diseases (CKD), funded by the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) since 2001. The CRIC study has recorded standard twelve-lead electrocardiograms (ECG) annually in all participants recruited from seven clinical centers. Our primary objective is to evaluate whether longitudinal ECG patterns are precursors to CVD and thus can be used to identity high-risk individuals. We propose new statistical methods to extract novel features from the raw ECG tracing both at baseline and in terms of longitudinal changes that are predictive of complications from CVD such as hospitalizations for heart failure (HF), myocardial infarction (MI), stroke, atrial fibrillation (AFib), and cardiovascular death. The information will be incorporated in the proposed real-time, computationally efficient risk prediction algorithms. We will also validate our discovery using an external cohort of CKD patients collected from the University of Pennsylvania Health System (UPHS).The proposed methods are not restricted to ECG data analysis and have a wide range of applications. We will develop user- friendly software packages for the new statistical models and risk prediction algorithms and share the validation data to promote their use in both statistical and clinical communities.