A new resampling-based statistical framework called Exploratory Stability Selection (ESS) was applied to the PRIME-KNEE study, which tracked physical resilience—defined as the ability to maintain or regain function after a health stressor—in older adults undergoing elective total knee arthroplasty. By integrating multiple resampling strategies and sparsity levels across clinical-only, plasma biomarker-only, and combined predictor sets, ESS distinguished highly stable predictors of resilient pain-interference recovery trajectories from context-dependent signals that fluctuate with analytic configuration.
Physical resilience after surgical stress is a critically underexplored dimension of healthy aging, distinct from baseline frailty or fitness. Most variable-selection methods in high-dimensional biomarker research are notoriously unstable—small data perturbations can flip which predictors appear significant, a reproducibility hazard that plagues geriatric biomarker discovery. ESS addresses this by making stability itself a measurable output rather than an assumption. The approach is well-positioned to accelerate precision-medicine targeting of at-risk older surgical patients before functional decline becomes irreversible.
That said, important caveats apply. ESS is explicitly hypothesis-generating, not confirmatory; the framework surfaces candidate predictors for future validation rather than establishing causal or clinical utility. The PRIME-KNEE sample is limited to a specific elective surgery population, restricting generalizability to broader aging contexts. Crucially, this is a preprint posted on medRxiv and has not yet undergone peer review—methodological claims and effect estimates may be revised. Overall, the contribution is incremental but methodologically useful: a transparency-focused tool filling a genuine analytic gap in resilience research.
