Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognost...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2024-09, Vol.96 (36), p.14560-14570
Main Authors: Cao, Jie, An, Guo-shuai, Li, Rong-qi, Hou, Ze-jin, Li, Jian, Jin, Qian-qian, Du, Qiu-xiang, Sun, Jun-hong
Format: Article
Language:English
Subjects:
analytical chemistry
blood serum
Chromatography
Chromatography, Liquid
Diagnosis
Female
Gas chromatography
Gas Chromatography-Mass Spectrometry
Humans
Learning algorithms
Liquid chromatography
Machine Learning
Male
Mass spectrometry
Mass spectroscopy
Metabolites
Metabolomics
Metabolomics - methods
Middle Aged
Morbidity
mortality
Scientific imaging
Thromboembolism
Thrombosis
user interface
Veins
Venous Thrombosis - blood
Venous Thrombosis - diagnosis
Venous Thrombosis - metabolism
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Summary:Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.
ISSN:0003-2700
1520-6882
1520-6882
DOI:10.1021/acs.analchem.4c02973