A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases

• Published in: Autoimmunity reviews Vol. 21; no. 6; p. 103105
• Main Authors: Danieli, Maria Giovanna, Tonacci, Alessandro, Paladini, Alberto, Longhi, Eleonora, Moroncini, Gianluca, Allegra, Alessandro, Sansone, Francesco, Gangemi, Sebastiano
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2022
• Subjects: Allergy and Immunology; Dermatomyositis; Intravenous immunoglobulin; Machine learning; Multi-omics; Polymyositis; Subcutaneous immunoglobulin; Subcutaneous immunoglobulin; Polymyositis; Dermatomyositis; Machine learning; Intravenous immunoglobulin; DL; SCIg; IVIg; DM; PM; Multi-omics; ML; Deep Learning
• ISSN: 1568-9972; 1873-0183
• DOI: 10.1016/j.autrev.2022.103105

To evaluate the response to treatment with intravenous (IVIg) and subcutaneous (20%SCIg) immunoglobulin in our series of patients with Inflammatory idiopathic myopathies (IIM) by the means of artificial intelligence. IIM are rare diseases mainly involving the skeletal muscle with particular clinical, laboratory and radiological characteristics. Artificial intelligence (AI) represents computer processes which allows to perform complex calculations and data analyses, with the least human intervention. Recently, the use an AI in medicine significantly expanded, especially through machine learning (ML) which analyses huge amounts of information and accordingly makes decisions, and deep learning (DL) which uses artificial neural networks to analyse data and automatically learn. In this study, we employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in our series of patients with IIM. The diagnoses were determined on the established EULAR/ACR criteria. The treatment response was evaluated employing the following: serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score) and disability (HAQ-DI score). We evaluated all the above parameters, applying, with R, different supervised ML algorithms, including Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net, Classification and Regression Trees and Random Forest to estimate the most important predictors for a good response to IVIg and 20%SCIg treatment. By the means of AI we have been able to identify the scores that best predict a good response to IVIg and 20%SCIg treatment. The muscle strength as evaluated by MMT8 score at the follow-up is predicted by the presence of dysphagia and of skin disorders, and the myositis activity index (MITAX) at the beginning of the treatment. The relationship between muscle strength and MITAX indicates a better action of IVIg therapy in patients with more active systemic disease. Considering our results, Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX at most) in myositis. •Artificial intelligence allows to perform data analyses, with the least human intervention.•We employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in myositis.•The muscle strength at the follow-up is predicteddysphagia and of skin disorders at the beginning of the treatment.•The relationship between muscle strength and disease activity indicates a  benefit  of IVIg in patients with more active systemic disease.•Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX) in myositis.