Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective

Objectives: Use of machine learning (ML) in diagnostics offers promise to optimise interpretation of laboratory data and guide clinical decision-making. For this, ML-based outputs should provide robustly reproducible results at least as good as the underlying laboratory data. The objective of this study was to assess robustness of ML-based steroid-probability-scores for diagnosis of primary aldosteronism (PA). Methods: Reproducibility of ML-based steroid-probability-scores was assessed from coefficients of variation (CVs) for pools of quality control plasma from selected groups of patients with and without PA. Intra-patient measurement variability was assessed from CVs of three consecutive plasma specimens obtained on different days from 77 patients. Inter-laboratory reproducibility was assessed from 47 duplicate plasma specimens analysed in two different laboratories. Conclusions: ML-based steroid-probability-scores for diagnosis of PA display remarkably high robustness according to reproducibility of measurements within and between laboratories as well as within patients.