Long-Term Mortality Risk According to Cardiorespiratory Fitness in Patients Undergoing Coronary Artery Bypass Graft Surgery

Abstract

The aim of this study was to evaluate the association between cardiorespiratory fitness (CRF) and long-term survival in United States (US) Veterans undergoing CABG. We identified 14,550 US Veterans who underwent CABG at least six months after completing a symptom-limited exercise treadmill test (ETT) with no evidence of cardiovascular disease. During a mean follow-up period of 10.0 ± 5.4 years, 6502 (43.0%) died. To assess the association between CRF and risk of mortality, we formed the following five fitness categories based on peak workload achieved (metabolic equivalents or METs) prior to CABG: Least-Fit (4.3 ± 1.0 METs (n = 4722)), Low-Fit (6.8 ± 0.9 METs (n = 3788)), Moderate-Fit (8.3 ± 1.1 METs (n = 2608)), Fit (10.2 ± 0.8 METs (n = 2613)), and High-Fit (13.0 ± 1.5 METs (n = 819)). Cox proportional hazard models were used to calculate risk across CRF categories. The models were adjusted for age, body mass index, race, cardiovascular disease, percutaneous coronary intervention prior to ETT, cardiovascular medications, and cardiovascular disease risk factors. P-values < 0.05 using two-sided tests were considered statistically significant. The association between cardiorespiratory fitness and mortality was inverse and graded. For every 1-MET increase in exercise capacity, the mortality risk was 11% lower (HR = 0.89; CI: 0.88–0.90; p < 0.001). When compared to the Least-Fit category (referent), mortality risk was 22% lower in Low-Fit individuals (HR = 0.78; CI: 0.73–0.82; p < 0.001), 31% lower in Moderate-Fit individuals (HR = 0.69; CI: 0.64–0.74; p < 0.001), 52% lower in Fit individuals (HR = 0.48; CI: 0.44–0.52; p < 0.001), and 66% lower in High-Fit individuals (HR = 0.34; CI: 0.29–0.40; p < 0.001). Cardiorespiratory fitness is inversely and independently associated with long-term mortality after CABG in Veterans referred for exercise testing.

A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise

“…Given the natural variation in physiological systems over time due to biological degradation and various influencing factors, such as habits, events, and diseases [34][35][36][37][38][39][40], the proposed strategy leverages the longitudinal outcomes of the static fit to reveal the temporal changes in the physiological system's characteristics. By modeling these timerelated trends, future parameter values can be predicted, providing valuable insights into the system parameter dynamics.…”

“…There is no specific structure defined for this modeling. Still, applying autoregressive techniques is recommended, considering their simplicity and the cumulative effect regarding time and other factors on physiological systems [34][35][36][37][38][39][40]. Therefore, the design of a MISO (Multiple-Input Single-Output) model is proposed for each parameter.…”

“…Quantifiable information related to factors other than age reported significantly influencing the cardiorespiratory system was also recorded. These data accounted for habits such as diet [35,36], physical activity [38,39], and quality of sleep [37,44], related to measures of body mass index, average hours of weekly physical activity, and average hours of daily sleep, respectively. Other relevant factors, such as accidents or illnesses, habits such as smoking, or family history of diseases, were not reported, considering that they were regarded as exclusion criteria.…”