ArticleCancer chemotherapy and pharmacology2024
Machine learning application identifies plasma markers for proteinuria in metastatic colorectal cancer patients treated with Bevacizumab.
Article in Cancer chemotherapy and pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors at 2 institutions in 1 country.
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Abstract
BACKGROUND AND
objectivesProteinuria is a common complication after the application of bevacizumab therapy in patients with metastatic colorectal cancer, and severe proteinuria can lead to discontinuation of the drug. There is a lack of sophisticated means to predict bevacizumab-induced proteinuria, so the present study aims to predict bevacizumab-induced proteinuria using peripheral venous blood samples.
methodsA total of 122 subjects were enrolled and underwent pre-treatment plasma markers, and we followed them for six months with proteinuria as the endpoint event. We then analyzed the clinical features and plasma markers for grade ≥ 2 proteinuria occurrence using machine learning to construct a model with predictive utility.
resultsOne hundred sixteen subjects were included in the statistical analysis. We found that high baseline systolic blood pressure, low baseline HGF, high baseline ET1, high baseline MMP2, and high baseline ACE1 were risk factors for the development of grade ≥ 2 proteinuria in patients with metastatic colorectal cancer who received bevacizumab. Then, we constructed a support vector machine model with a sensitivity of 0.889, a specificity of 0.918, a precision of 0.615, and an F1 score of 0.727.
conclusionWe constructed a machine learning model for predicting grade ≥ 2 bevacizumab-induced proteinuria, which may provide proteinuria risk assessment for applying bevacizumab in patients with metastatic colorectal cancer.
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