A new maximal bicycle test using a prediction algorithm developed from four large COPD studies

Background: Maximum exercise workload (W_MAX) is today assessed as the first part of Cardiopulmonary Exercise testing. The W_MAX test exposes patients with COPD, often having cardiovascular comorbidity, to risks. Our research project was initiated with the final aim to eliminate the W_MAX test and replace this test with a predicted value of W_MAX, based on a prediction algorithm of W_MAX derived from multicentre studies. Methods: Baseline data (W_MAX, demography, lung function parameters) from 850 COPD patients from four multicentre studies were collected and standardized. A prediction algorithm was prepared using Random Forest modelling. Predicted values of W_MAX were used in a new W_MAX test, which used a linear increase in order to reach the predicted W_MAX within 8 min. The new W_MAX test was compared with the standard stepwise W_MAX test in a pilot study including 15 patients with mild/moderate COPD. Results: The best prediction algorithm of W_MAX included age, sex, height, weight, and six lung function parameters. FEV₁ and DLCO were the most important predictors. The new W_MAX test had a better correlation (R² = 0.84) between predicted and measured W_MAX than the standard W_MAX test (R² = 0.66), with slopes of 0.50 and 0.46, respectively. The results from the new W_MAX test and the standard W_MAX test correlated well. Conclusion: A prediction algorithm based on data from four large multicentre studies was used in a new W_MAX test. The prediction algorithm provided reliable values of predicted W_MAX. In comparison with the standard W_MAX test, the new W_MAX test provided similar overall results.