AI-Techniques Loss-Based Algorithm for Severity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing

OBJECTIVE: Heightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET). MATERIALS AND METHODS: After establishing sex- and age-adjusted reference centiles (0-10 Borg scale), we developed a novel algorithm (AI-Techniques Loss-Based Algorithm for Severity Classification [ATLAS]) based on reciprocal exponential loss for CPET data from patients with chronic obstructive lung disease of varied severity. RESULTS: Categories of dyspnea intensity by ATLAS-but not dyspnea at peak exercise-correctly discriminated patients in progressively higher resting and exercise impairment (P < .05). DISCUSSION: This new AI-techniques approach will be translated to the care of disabled patients to uncover the seeds and consequences of their activity-related symptoms. CONCLUSIONS: We used innovative informatics research to change paradigms in displaying, quantifying, and analyzing effort-related symptoms in patient populations.