Validation and Refinement of Prediction Models to Estimate Exercise Capacity in Cancer Survivors Using the Steep Ramp Test

摘要

Abstract Objective To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (V o 2peak ) and peak power output (W peak ). Design Cross-sectional study. Setting Multicenter. Participants Cancer survivors (N=283) in 2 randomized controlled exercise trials. Interventions Not applicable. Main Outcome Measures Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions. Results ICCs of SRT-predicted V o 2peak and W peak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for V o 2peak and ±59W with a bias of 5W for W peak . Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of V o 2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of W peak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for W peak . Conclusions Predictions of V o 2peak and W peak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for W peak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription. Highlights ? Peak oxygen consumption and peak power output can be estimated from the steep ramp test in cancer survivors. ? A multivariable nomogram yields better predictions than does a univariable nomogram. ? The validity of the estimations is likely to vary across different populations. ? The estimations can be used cautiously to aid exercise prescription. ? The estimations should not be used for clinical decision making for individuals. ]]>