Table 1_Predicting low muscle strength from routine fitness-monitoring data in hypertensive older adults living at high altitude: development and internal validation of interpretable models.docx

Post hoc analyses included simple benchmark models, a sex-excluded model, sex-stratified models, threshold operating characteristics, and leave-one-site/altitude-band-out internal–external validation.</p>Results<p>Low muscle strength was present in 171/433 participants (39.5%). Random forest had the highest AUC point estimate (0.788, 95% CI 0.742–0.831), but its interval overlapped with elastic-net logistic regression (0.781, 0.732–0.825) and histogram gradient boosting (0.774, 0.729–0.819).

Elastic-net was designated as the candidate model because calibration was closest to ideal (slope 0.99, 95% CI 0.78–1.24; intercept −0.00, −0.22 to 0.24) and it provides a transportable equation. Its PR-AUC was 0.725 versus a no-skill baseline of 0.395. Incremental discrimination over sex plus age plus site/altitude was small (ΔAUC 0.016, 95% CI − 0.009 to 0.041).

AUC was 0.705 without sex, 0.653 under a sex-specific tertile outcome, and 0.626–0.759 when each site was held out.</p>Conclusion<p>Routine fitness-monitoring variables contain information about concurrent low muscle strength, but much of the apparent performance is explained by the sex-specific outcome definition and site/altitude structure. Direct grip-strength measurement remains preferable whenever feasible. The elastic-net model should be regarded only as a candidate for opportunistic triage when a valid grip measurement is unavailable or unusable; independent external validation, recalibration, and prospective impact evaluation are required before implementation.</p>