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Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy.

Takayama T, Okamoto S, Hisamatsu T, Naganuma M, Matsuoka K, Mizuno S, Bessho R, Hibi T, Kanai T - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Related in: MedlinePlus

Relative weights of input factors for ANNs.Data are expressed as the mean±SD for member networks.
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pone.0131197.g001: Relative weights of input factors for ANNs.Data are expressed as the mean±SD for member networks.

Mentions: We next attempted to identify factors that critically correlate to the outcome in ANN by using the relative weights of input factors analysis (Fig 1 and S1 Table). This analysis involves determining how the result changes when the test factor (Xtest) is excluded. An Xtest value greater than 1 indicates that it improves the expression, and a value less than 1 indicates that it does not improve the expression. We analysed all expressions and determined the corresponding means and standard deviations. As shown in Fig 1, X13 (history of operation) and X11 (history of admission) were defined as significant predictive factors in every trial.


Computer-Aided Prediction of Long-Term Prognosis of Patients with Ulcerative Colitis after Cytoapheresis Therapy.

Takayama T, Okamoto S, Hisamatsu T, Naganuma M, Matsuoka K, Mizuno S, Bessho R, Hibi T, Kanai T - PLoS ONE (2015)

Relative weights of input factors for ANNs.Data are expressed as the mean±SD for member networks.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481415&req=5

pone.0131197.g001: Relative weights of input factors for ANNs.Data are expressed as the mean±SD for member networks.
Mentions: We next attempted to identify factors that critically correlate to the outcome in ANN by using the relative weights of input factors analysis (Fig 1 and S1 Table). This analysis involves determining how the result changes when the test factor (Xtest) is excluded. An Xtest value greater than 1 indicates that it improves the expression, and a value less than 1 indicates that it does not improve the expression. We analysed all expressions and determined the corresponding means and standard deviations. As shown in Fig 1, X13 (history of operation) and X11 (history of admission) were defined as significant predictive factors in every trial.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Related in: MedlinePlus