# Dynamic and personalized prediction of complex cardiovascular events.

> **NIH NIH R21** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $121,500

## Abstract

PROJECT SUMMARY
Statistical learning methods such as random forests have proven useful in medical research. With the availability
of massive biomedical and event history data collected during the course of diseases, dynamic and personalized
risk prediction of future clinical events can provide valuable information to identify high-risk individuals and initiate
timely treatments or interventions. Our application is motivated by the NHLBI Pooled Cohorts Study, where risk
factors were measured intermittently at follow-up visits, and multiple cardiovascular disease (CVD) events could
occur during the follow-up period. Existing statistical learning methods usually focus on time to the first event with
baseline predictors; methods that can handle the second and subsequent clinical events or repeatedly measured
time-dependent risk factors are lacking. We develop flexible random forest methods for multiple event data, where
the complex event history information is fully utilized without pre-specifying the dependence structure of different
events. The proposed methods can deal with the case where events are of different degrees of clinical importance
and competing risks exist. The methodology will be applied to the pooled cohorts to build accurate risk prediction
tools and to identify important risk factors for both CVD incidence and recurrence. We will conduct validation
analysis to test whether novel statistical learning methods can outperform existing methods such as Cox-type
models; we will also use forest models to provide guidance in building Cox-type models for CVD recurrence. The
proposed research has the potential to advance dynamic and personalized risk prediction and to facilitate more
effective prevention and treatment strategies for CVD recurrence.

## Key facts

- **NIH application ID:** 10360163
- **Project number:** 1R21HL156228-01A1
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Yifei Sun
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $121,500
- **Award type:** 1
- **Project period:** 2022-01-03 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10360163

## Citation

> US National Institutes of Health, RePORTER application 10360163, Dynamic and personalized prediction of complex cardiovascular events. (1R21HL156228-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10360163. Licensed CC0.

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