Margot Vermeiren

Background and Objectives: The first ventilatory threshold (VT1) is a key indicator of submaximal aerobic fitness. Its determination typically relies on visual assessment, which is time-consuming and subject to interobserver variability. Automated solutions may offer a more objective, reproducible and time-efficient alternative. This study evaluated the accuracy of a revised V-slope method. Methods: This retrospective study was conducted at University Hospitals Leuven, Belgium, using cardiopulmonary exercise test (CPET) data of 271 healthy adults collected between 2010 and 2020. Automated VT1 detection using a revised V-slope algorithm was compared with manual determination via the visual V-slope method in 262 participants with a determinable VT1. Agreement was evaluated using Bland–Altman analysis, intraclass correlation coefficients (ICC), Deming regression and equivalence testing (TOST). Interobserver agreement was evaluated using ICC. Results: Median oxygen uptake at VT1 was higher with automated compared to manual assessment (1151 vs. 1122 mL/min; p < 0.001). Mean difference was 29 mL/min (95% CI [19, 40]. Relative mean bias was 2.2% ± 7.1% (LoA [− 11.7 to 16.0%], with excellent agreement (ICC 0.97 [95% CI 0.96–0.98] and a Deming regression slope of 0.95 [95% CI 0.92–0.99] and intercept of 26.1 mL/min [95% CI −11.9 to 64.7]. TOST with equivalence bounds [− 100, 100] was significant ( p  < 0.001; 90% CI [20, 38]). Conclusion: The revised V-slope algorithm demonstrated strong group-level agreement with visual assessment for VT1 determination, supporting the reliability and practical applicability for large-scale research applications in healthy adults. However, given the observed individual-level variability, caution is warranted when applying automated VT1 values to guide exercise prescription for individual patients without expert review.

Aims: Chronotropic incompetence (CI), defined as the inability of the heart to appropriately increase its rate in response to exercise, is associated with exercise intolerance and adverse events. However, the diagnostic criteria for CI vary widely, limiting their clinical applicability and comparability across studies. Here we aim to systematically map the current definitions of CI and the prediction models used to estimate age-predicted maximal heart rate (APMHR). We illustrate how this diagnostic variability affects the identification of CI in patients enrolled in a cardiac rehabilitation program. Methods and results: A scoping review systematically identified all published definitions of CI and prediction models for APMHR. A retrospective study evaluated the agreement between definitions in patients participating in cardiac rehabilitation. A total of 86 studies were included. Seventeen distinct methods to define CI were identified, ranging from absolute or relative peak heart rate parameters to approaches incorporating workload. In many cases, the applied cut-off values lacked a rationale. In parallel, numerous APMHR formulas were identified, differing by sex, age category, fitness level, clinical status, and the use of beta-blocker therapy, showing limited accuracy. In the clinical setting agreement between four major definitions was poor and pairwise comparisons between them showed significant differences in classification. Conclusion: Current methods to define CI and predict APMHR are inconsistent and problematic. This review provides a practical framework for selecting a context-appropriate definition and formula. It identifies current methodological gaps and highlights the need for future research to establish a consensus definition of CI.