Using Machine Learning-Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are we there yet?

Abstract

ABSTRACT Introduction Well-trained staff is needed to interpret cardiopulmonary exercise tests (CPET). We aimed to examine the accuracy of machine learning-based algorithms to classify exercise limitations and their severity in clinical practice compared to expert consensus using patients presenting at a pulmonary clinic. Methods This study included 200 historical CPET datasets (48.5% female) of patients aged >40 years referred for CPET due to unexplained dyspnoea, preoperative examination, and evaluation of therapy progress. Datasets were independently rated by experts according to the severity of pulmonary-vascular, mechanical-ventilatory, cardio-circulatory, and muscular limitations using a visual analogue scale. Decision trees and random forests analyses were calculated. Results Mean deviations between experts in the respective limitation categories ranged from 1.0 to 1.1 points (SD = 1.2) before consensus. Random forests identified parameters of particular importance for detecting specific constraints. Central parameters were nadir ventilatory efficiency for CO2, ventilatory efficiency slope for CO2 (pulmonary-vascular limitations), breathing reserve, forced expiratory volume in one second, and forced vital capacity (mechanical-ventilatory limitations), and peak oxygen uptake, O2 uptake/work rate slope, and % change of the latter (cardio-circulatory limitations). Thresholds differentiating between different limitation severities were reported. The accuracy of the most accurate decision tree of each category was comparable to expert ratings. Finally, a combined decision tree was created quantifying combined system limitations within one patient. Conclusions Machine-learning-based algorithms may be a viable option to facilitate the interpretation of CPET and identify exercise limitations. Our findings may further support clinical decision-making and aid the development of standardised rating instruments.