# Association of Treadmill Exercise Testing Parameters with PREVENT-Estimated Cardiovascular Risk: A Cross-Sectional Analysis

## TL;DR

This study shows that treadmill exercise test results are linked to cardiovascular risk estimates but mostly reflect the same information as resting clinical data.

## Contribution

The study reveals that treadmill parameters add minimal independent value to cardiovascular risk prediction when clinical data is already included.

## Key findings

- Treadmill parameters like METs and heart rate recovery were significantly associated with cardiovascular risk in a demographic base model.
- When clinical data was included, treadmill parameters added negligible incremental value to risk prediction.
- Exercise testing shifted risk estimates for 14% of participants, especially those with intermediate or higher risk.

## Abstract

Background/Objectives: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death worldwide. The 2023 American Heart Association PREVENT equations represent a contemporary approach to cardiovascular risk estimation, yet they rely on resting clinical and biochemical parameters. This study aimed to evaluate the association between PREVENT-estimated 10-year cardiovascular risk and treadmill exercise testing (TET)-derived physiological variables. Methods: We conducted a single-center observational study of 391 participants (mean age 42.9 ± 9.0 years, 56.8% male) who underwent symptom-limited treadmill testing. Ten-year cardiovascular risk was estimated using PREVENT for total cardiovascular disease (CVD), ASCVD, and heart failure (HF). Hierarchical multivariable regression was performed using log-transformed PREVENT risk estimates to quantify the incremental association of exercise capacity (METs), hemodynamic markers (double product), autonomic recovery (heart rate recovery), and the ST/HR index beyond demographic (age, sex, BMI) and extended clinical base models incorporating available PREVENT input covariates. Results: Beyond the demographic base model, treadmill parameters were significantly associated with log-transformed PREVENT-CVD risk (ΔR2 = 0.026, p < 0.001; Cohen’s f2 = 0.154). Double product (standardized β = 0.116), HRR at 1 min (standardized β = −0.081), and maximum METs (standardized β = −0.079) were independently associated with risk estimates. However, when the full set of available PREVENT input covariates was included in the base model, the incremental association was negligible (ΔR2 = 0.0004, p = 0.386), indicating substantial overlap between exercise-derived physiology and PREVENT-embedded clinical information. The incremental association was greatest in participants with intermediate (1–5%) and higher (≥5%) estimated risk (ΔR2 = 0.052 and 0.246, respectively). Approximately 14% of participants shifted to a different quartile of estimated risk after inclusion of treadmill data. Conclusions: Treadmill-derived physiological parameters are significantly associated with PREVENT-estimated cardiovascular risk, but this association largely reflects shared pathophysiology with PREVENT input variables rather than statistically independent incremental information. Exercise testing may serve as a physiological complement to static risk estimation, particularly in intermediate-risk populations, by providing a dynamic physiological assessment that complements resting clinical measurements. Prospective studies with adjudicated cardiovascular outcomes are needed before clinical implementation.