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

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, promi...

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Autores principales:	Jun Miyoshi, Tsubasa Maeda, Katsuyoshi Matsuoka, Daisuke Saito, Sawako Miyoshi, Minoru Matsuura, Susumu Okamoto, Satoshi Tamura, Tadakazu Hisamatsu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/67f3558ce61d4ab2abbd337be27645ca
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spelling	oai:doaj.org-article:67f3558ce61d4ab2abbd337be27645ca2021-12-02T15:08:11ZMachine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis10.1038/s41598-021-96019-x2045-2322https://doaj.org/article/67f3558ce61d4ab2abbd337be27645ca2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96019-xhttps://doaj.org/toc/2045-2322Abstract 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.Jun MiyoshiTsubasa MaedaKatsuyoshi MatsuokaDaisuke SaitoSawako MiyoshiMinoru MatsuuraSusumu OkamotoSatoshi TamuraTadakazu HisamatsuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jun Miyoshi Tsubasa Maeda Katsuyoshi Matsuoka Daisuke Saito Sawako Miyoshi Minoru Matsuura Susumu Okamoto Satoshi Tamura Tadakazu Hisamatsu Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
description	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.
format	article
author	Jun Miyoshi Tsubasa Maeda Katsuyoshi Matsuoka Daisuke Saito Sawako Miyoshi Minoru Matsuura Susumu Okamoto Satoshi Tamura Tadakazu Hisamatsu
author_facet	Jun Miyoshi Tsubasa Maeda Katsuyoshi Matsuoka Daisuke Saito Sawako Miyoshi Minoru Matsuura Susumu Okamoto Satoshi Tamura Tadakazu Hisamatsu
author_sort	Jun Miyoshi
title	Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
title_short	Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
title_full	Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
title_fullStr	Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
title_full_unstemmed	Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
title_sort	machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/67f3558ce61d4ab2abbd337be27645ca
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Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

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