ArticleClinical chemistry and laboratory medicine2025
Robustness of steroidomics-based machine learning for diagnosis of primary aldosteronism: a laboratory medicine perspective.
Article in Clinical chemistry and laboratory medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Primary aldosteronism.Nature reviews. Disease primers · 2026Review
- Advanced machine learning-based screening for primary aldosteronism with plasma steroids, potassium, and renin.NPJ digital medicine · 2026Article
- Problems in the use of chemical and functional nomenclatures for steroids in human physiology, biology and pharmacology.RSC advances · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectivesUse 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).
methodsReproducibility 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.
resultsSupport vector machine-derived steroid-probability-scores for diagnosis of PA for seven sets of quality control plasma pools yielded an averaged CV (2.5 % CI 0.4-4.4 %) that was lower (p=0.0078) than the averaged CV for seven steroids employed in that model (12.0 % CI 7.4-16.6). Using three sets of plasma samples from 77 patients, CVs for intra-patient measurement variability of steroid-probability-scores were 7 % (CI 5-9 %) and lower (p<0.0001) than CVs for measurements of aldosterone (38 % CI 32-42 %), 18-oxocortisol (36 % CI 29-43 %), 18-hydroxycortisol (25 % CI 21-28 %) and the aldosterone:renin ratio (46 % CI 38-55 %). ML-derived probability scores for 47 duplicate plasma samples analysed at two separate laboratories displayed excellent agreement and negligible bias.
conclusionsML-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.