Computer-aided prediction of long-term prognosis of patients with ulcerative colitis after cytoapheresis therapy

Tetsuro Takayama, Susumu Okamoto, Tadakazu Hisamatsu, Makoto Naganuma, Katsuyoshi Matsuoka, Shinta Mizuno, Rieko Bessho, Toshifumi Hibi, Takanori Kanai

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Abstract

Cytoapheresis (CAP) therapy is widely used in ulcerative colitis (UC) patients with moderate to severe activity in Japan. The aim of this study is to predict the need of operation after CAP therapy of UC patients on an individual level using an artificial neural network system (ANN). Ninety UC patients with moderate to severe activity were treated with CAP. Data on the patients' demographics, medication, clinical activity index (CAI) and efficacy of CAP were collected. Clinical data were divided into training data group and validation data group and analyzed using ANN to predict individual outcomes. The sensitivity and specificity of predictive expression by ANN were 0.96 and 0.97, respectively. Events of admission, operation, and use of immunomodulator, and efficacy of CAP were significantly correlated to the outcome. Requirement of operation after CAP therapy was successfully predicted by using ANN. This newly established ANN strategy would be used as powerful support of physicians in the clinical practice.

Original languageEnglish
Article numbere0131197
JournalPLoS One
Volume10
Issue number6
DOIs
Publication statusPublished - 2015 Jun 25

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ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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