Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

OBJECTIVES: Mass spectrometry-based steroidomics combined with machine learning (ML) provides a potentially powerful approach in endocrine diagnostics, but is hampered by limitations in the conveyance of results and interpretations to clinicians. We address this shortcoming by integration of the two technologies with a laboratory information management systems (LIMS) model.

METHODS: The approach involves integration of ML algorithm-derived models with commercially available mathematical programming software and a web-based LIMS prototype. To illustrate clinical utility, the process was applied to plasma steroidomics data from 22 patients tested for primary aldosteronism (PA).

RESULTS: Once mass spectrometry data are uploaded into the system, automated processes enable generation of interpretations of steroid profiles from ML models. Generated reports include plasma concentrations of steroids in relation to age- and sex-specific reference intervals along with results of ML models and narrative interpretations that cover probabilities of PA. If PA is predicted, reports include probabilities of unilateral disease and mutations of KCNJ5 known to be associated with successful outcomes of adrenalectomy. Preliminary results, with no overlap in probabilities of disease among four patients with and 18 without PA and correct classification of all four patients with unilateral PA including three of four with KCNJ5 mutations, illustrate potential utility of the approach to guide diagnosis and subtyping of patients with PA.

CONCLUSIONS: The outlined process for integrating plasma steroidomics data and ML with LIMS may facilitate improved diagnostic-decision-making when based on higher-dimensional data otherwise difficult to interpret. The approach is relevant to other diagnostic applications involving ML.

Details

Original languageEnglish
Pages (from-to)1929-1937
Number of pages9
JournalClinical chemistry and laboratory medicine
Volume60
Issue number12
Early online date18 Jul 2022
Publication statusPublished - 25 Nov 2022
Peer-reviewedYes

External IDs

Scopus 85135148352
Mendeley 8cd80c46-84de-32b5-bd2a-854f53daba87
unpaywall 10.1515/cclm-2022-0470

Keywords

Keywords

  • artificial intelligence, laboratory information management systems, liquid chromatography with tandem mass spectrometry (LC-MS/MS), machine learning, primary aldosteronism, steroidomics, Artificial Intelligence, Humans, Male, Hyperaldosteronism/diagnosis, Mass Spectrometry, Female, Information Management, G Protein-Coupled Inwardly-Rectifying Potassium Channels/genetics, Steroids