title: Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy.
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.
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan; Predictive Medicine Institute, Minato-ku, Tokyo, Japan; Tokai University School of Medicine, Isehara-shi, Kanagawa, Japan; Wata Clinic, Katsushika-ku, Tokyo, Japan.
authors.forename: Tetsuro
authors.initials: T
authors.lastname: Takayama
authors.affiliation: Department of General Medicine, Kyorin University School of Medicine, Mitaka-shi, Tokyo, Japan.
authors.forename: Susumu
authors.initials: S
authors.lastname: Okamoto
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan.
authors.forename: Tadakazu
authors.initials: T
authors.lastname: Hisamatsu
authors.affiliation: Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Naganuma
authors.affiliation: Department of Advanced Therapeutics for Gastrointestinal Diseases, Graduate School, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
authors.forename: Katsuyoshi
authors.initials: K
authors.lastname: Matsuoka
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan; Department of Internal Medicine, Saiseikai Central Hospital, Minato-ku, Tokyo, Japan.
authors.forename: Shinta
authors.initials: S
authors.lastname: Mizuno
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan.
authors.forename: Rieko
authors.initials: R
authors.lastname: Bessho
authors.affiliation: Center for Advanced IBD Research and Treatment, Kitasato Institute Hospital, Kitasato University,Minato-ku, Tokyo, Japan.
authors.forename: Toshifumi
authors.initials: T
authors.lastname: Hibi
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan.
authors.forename: Takanori
authors.initials: T
authors.lastname: Kanai
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003093:Colitis, Ulcerative / Q000175:diagnosis* / Q000628:therapy*; D016238:Cytapheresis; D003658:Decision Making, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D055815:Young Adult