PMID- 35851438
OWN - NLM
STAT- MEDLINE
DCOM- 20221104
LR  - 20221104
IS  - 1437-4331 (Electronic)
IS  - 1434-6621 (Linking)
VI  - 60
IP  - 12
DP  - 2022 Nov 25
TI  - Integration of artificial intelligence and plasma steroidomics with laboratory 
      information management systems: application to primary aldosteronism.
PG  - 1929-1937
LID - 10.1515/cclm-2022-0470 [doi]
AB  - 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.
CI  - (c) 2022 Walter de Gruyter GmbH, Berlin/Boston.
FAU - Constantinescu, Georgiana
AU  - Constantinescu G
AUID- ORCID: 0000-0002-7304-2557
AD  - Department of Internal Medicine III, University Hospital "Carl Gustav Carus", 
      Technische Universitat Dresden, Dresden, Germany.
AD  - Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania.
FAU - Schulze, Manuel
AU  - Schulze M
AD  - Department of Distributed and Data Intensive Computing, Center for Information 
      Services and High Performance Computing (ZIH), Technische Universitat Dresden, 
      Dresden, Germany.
FAU - Peitzsch, Mirko
AU  - Peitzsch M
AD  - Institute of Clinical Chemistry and Laboratory Medicine, University Hospital 
      "Carl Gustav Carus", Technische Universitat Dresden, Dresden, Germany.
FAU - Hofmockel, Thomas
AU  - Hofmockel T
AD  - Department of Radiology, University Hospital "Carl Gustav Carus", Technische 
      Universitat Dresden, Dresden, Germany.
FAU - Scholl, Ute I
AU  - Scholl UI
AUID- ORCID: 0000-0002-0309-8045
AD  - Berlin Institute of Health at Charite - Universitatsmedizin Berlin, Center of 
      Functional Genomics, Berlin, Germany.
FAU - Williams, Tracy Ann
AU  - Williams TA
AUID- ORCID: 0000-0002-2388-6444
AD  - Medizinische Klinik und Poliklinik IV, Klinikum der Universitat, 
      Ludwig-Maximilians-Universitat Munchen, Munich, Germany.
AD  - Department of Medical Sciences, Division of Internal Medicine and Hypertension, 
      University of Turin, Turin, Italy.
FAU - Lenders, Jacques W M
AU  - Lenders JWM
AD  - Department of Internal Medicine III, University Hospital "Carl Gustav Carus", 
      Technische Universitat Dresden, Dresden, Germany.
AD  - Department of Internal Medicine, Radboud University Medical Centre, Nijmegen, The 
      Netherlands.
FAU - Eisenhofer, Graeme
AU  - Eisenhofer G
AD  - Department of Internal Medicine III, University Hospital "Carl Gustav Carus", 
      Technische Universitat Dresden, Dresden, Germany.
AD  - Institute of Clinical Chemistry and Laboratory Medicine, University Hospital 
      "Carl Gustav Carus", Technische Universitat Dresden, Dresden, Germany.
LA  - eng
PT  - Journal Article
DEP - 20220718
PL  - Germany
TA  - Clin Chem Lab Med
JT  - Clinical chemistry and laboratory medicine
JID - 9806306
RN  - 0 (Steroids)
RN  - 0 (KCNJ5 protein, human)
RN  - 0 (G Protein-Coupled Inwardly-Rectifying Potassium Channels)
SB  - IM
MH  - Male
MH  - Female
MH  - Humans
MH  - *Hyperaldosteronism/diagnosis
MH  - Artificial Intelligence
MH  - Steroids
MH  - Mass Spectrometry
MH  - Information Management
MH  - G Protein-Coupled Inwardly-Rectifying Potassium Channels/genetics
OTO - NOTNLM
OT  - artificial intelligence
OT  - laboratory information management systems
OT  - liquid chromatography with tandem mass spectrometry (LC-MS/MS)
OT  - machine learning
OT  - primary aldosteronism
OT  - steroidomics
EDAT- 2022/07/20 06:00
MHDA- 2022/11/05 06:00
CRDT- 2022/07/19 08:22
PHST- 2022/05/16 00:00 [received]
PHST- 2022/06/28 00:00 [accepted]
PHST- 2022/07/20 06:00 [pubmed]
PHST- 2022/11/05 06:00 [medline]
PHST- 2022/07/19 08:22 [entrez]
AID - cclm-2022-0470 [pii]
AID - 10.1515/cclm-2022-0470 [doi]
PST - epublish
SO  - Clin Chem Lab Med. 2022 Jul 18;60(12):1929-1937. doi: 10.1515/cclm-2022-0470. 
      Print 2022 Nov 25.