Is estimated cardiorespiratory fitness an effective predictor for cardiovascular and all-cause mortality? A meta-analysis.
Level 3 - non-randomized controlled study
Systematic review and meta-analysis of prospective cohort studies
PubMed 34225102 · doi:10.1016/j.atherosclerosis.2021.06.904
What was done
Authors conducted a systematic review and dose-response meta-analysis of prospective cohort studies evaluating the association between algorithm-derived estimated cardiorespiratory fitness (eCRF) and cardiovascular or all-cause mortality. Study-specific multivariate-adjusted hazard ratios (HRs) per 1-metabolic equivalent (MET) increase in eCRF were pooled using random-effects models, and discriminative performance was compared against individual components and exercise testing-measured CRF.
What was found
The meta-analysis analyzed 25 datasets from 8 cohort studies enrolling over 170,000 participants. Each 1-MET increase in eCRF was associated with a summary HR of 0.83 (95% CI 0.80 to 0.86) for cardiovascular mortality (11 datasets) and 0.83 (95% CI 0.78 to 0.88) for all-cause mortality (14 datasets). The associations showed no sex differences and were linear (all p nonlinearity ≥ 0.27). Discriminative performance of eCRF was higher than individual components (physical activity, resting heart rate, BMI; all p < 0.05), but slightly lower than exercise testing-measured CRF.
Why it matters
Algorithm-derived fitness estimates offer a practical, scalable alternative for mortality risk stratification when formal clinical exercise testing is not feasible.
Limits
eCRF has lower discriminative accuracy than direct exercise testing. The findings reflect observational cohort data prone to residual confounding, and the abstract does not describe algorithm formulas, follow-up durations, or heterogeneity across datasets.
Cited by
- supports Higher cardiorespiratory fitness is associated with a reduction in all-cause mortality, cardiovascular disease, and type 2 diabetes.