Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.

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

Methods: The Baltimore Longitudinal Study of Aging (BLSA) participants with valid VO 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 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 and mortality.

Results: The age was 69.0 ± 10.4 years (mean ± SD) and the measured VO 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 showed an inverse gradient in mortality risk. Predicted VO variables yielded similar effect estimates but were not robust to adjustment.

Conclusion: Measured VO 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, an important vital sign, can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.