Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

• Published in: Scientific reports Vol. 11; no. 1; p. 16440
• Main Authors: Miyoshi, Jun, Maeda, Tsubasa, Matsuoka, Katsuyoshi, Saito, Daisuke, Miyoshi, Sawako, Matsuura, Minoru, Okamoto, Susumu, Tamura, Satoshi, Hisamatsu, Tadakazu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 12.08.2021; Nature Publishing Group UK; Nature Portfolio
• Subjects: Body weight; Concept learning; Inflammatory bowel disease; Learning algorithms; Lymphocytes; Machine learning; Monoclonal antibodies; Patients; Phenotypes; Prediction models; Remission; Ulcerative colitis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-96019-x

Abstract Predicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.