Development, validation, and transportability of several machine-learned, non-exercise-based VO_2max prediction models...

There exist few maximal oxygen uptake (VO_2max) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO_2max is infeasible in large epidemiologic cohort studies, we sought to develop, validate, compare, and assess the transportability of several ML VO_2max prediction algorithms.

The Baltimore Longitudinal Study of Aging (BLSA) participants with valid VO_2max tests were included (n = 1080). Least absolute shrinkage and selection operator, linear- and tree-boosted extreme gradient boosting, random forest, and support vector machine (SVM) algorithms were trained to predict VO_2max values. We developed these algorithms for: (a) the overall BLSA, (b) by sex, (c) using all BLSA variables, and (d) variables common in aging cohorts. Finally, we quantified the associations between measured and predicted VO_2max and mortality.

The age was 69.0 ± 10.4 years (mean ± SD) and the measured VO_2max was 21.6 ± 5.9 mL/kg/min. Least absolute shrinkage and selection operator, linear- and tree-boosted extreme gradient boosting, random forest, and support vector machine yielded root mean squared errors of 3.4 mL/kg/min, 3.6 mL/kg/min, 3.4 mL/kg/min, 3.6 mL/kg/min, and 3.5 mL/kg/min, respectively. Incremental quartiles of measured VO_2max showed an inverse gradient in mortality risk. Predicted VO_2max variables yielded similar effect estimates but were not robust to adjustment.

Measured VO_2max is a strong predictor of mortality. Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment. Future studies should seek to reproduce these results so that VO_2max, an important vital sign, can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.