# Dynamic Longitudinal Functional Models with Applications to the CRIC Study

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $713,998

## Abstract

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.

## Key facts

- **NIH application ID:** 10340402
- **Project number:** 1R01HL161303-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Rajat Deo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $713,998
- **Award type:** 1
- **Project period:** 2022-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10340402

## Citation

> US National Institutes of Health, RePORTER application 10340402, Dynamic Longitudinal Functional Models with Applications to the CRIC Study (1R01HL161303-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10340402. Licensed CC0.

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