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

<div><p>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.</p></div

The impact of visceral adipose tissue as best predictor for difficult colonoscopy and the clinical utility of a long small-caliber scope as rescue

<div><p>Background</p><p>There have been many reports about a variety of factors associated with incomplete colonoscopy or difficult colonoscopy with long cecal intubation time (CIT). The aim of this retrospective study was to analyze the factors related to difficult colonoscopy under conscious sedation and demonstrate the clinical utility of a small-caliber scope as rescue by using the data from a large number of subjects who underwent health check-ups.</p><p>Methods</p><p>Consecutive 1036 cases over a 12-month period (April 2015 to March 2016) were enrolled and 619 subjects were divided into two groups: Easy colonoscopy (CS) Group (CIT ≤ 10 min); Difficult CS Group (CIT > 10 min or incomplete colonoscopy by a standard scope). The two groups were compared by subjects and colonoscopy characteristics with univariate analysis followed by multivariate logistic regression analysis. Reasons for incomplete colonoscopy were also assessed.</p><p>Results</p><p>Cecal intubation rate increased from 97.9% to 99.9% (1007/1008) by the rescue scope. Main reasons for incomplete colonoscopy were tortuosity in the left hemicolon (38%), redundancy in the right hemicolon (29%), pain (19%) and fixation (14%). Moreover, 95% (20/21) of rescue colonoscopies were completed without additional sedation. Higher BMI (21 kg/m² ≤ BMI) and intermediate visceral adipose tissue (VAT) (75 cm² ≤ VAT < 150 cm²) were significantly associated with easy CS (80.7% vs 19.3%, <i>P</i> = 0.004; 56.3% vs 43.7%, <i>P</i> = 0.001) by univariate analysis. Age, gender, and VAT, not BMI, were independently associated with difficult colonoscopy by multivariate analysis (OR (95% CI), <i>P</i>: 0.964 (0.942, 0.985), 0.001; 1.845 (1.101, 3.091), 0.020; 2.347 (1.395, 3.951), 0.001). Subgroup analysis by gender also showed VAT as the best predictor for both genders.</p><p>Conclusion</p><p>Difficult colonoscopy was significantly associated with advancing age, female gender and, lower (< 75 cm²) or higher (150 cm² ≤) VAT. These subjects may benefit from having complete and more comfortable colonoscopy examinations by using the small-caliber scope rather than the standard scope.</p></div

The sensitivity and specificity provided by ANN.

<p>The sensitivity and specificity provided by ANN.</p

The sensitivity and specificity provided by ANN.

<p>The sensitivity and specificity provided by ANN.</p

Characteristics of patients.

<p>Continuous data are expressed as mean with range or number in parentheses. CAI: Clinical activity index, CAP: Cytoapheresis, PSL: Prednisolone</p><p>Characteristics of patients.</p

Relative weights of input factors for ANNs.

<p>Data are expressed as the mean±SD for member networks.</p

Factors and outcome used to predict individual patient outcome.

<p>Factors and outcome used to predict individual patient outcome.</p

Multivariate analysis of the predictors for difficult colonoscopy in females.

<p>Multivariate analysis of the predictors for difficult colonoscopy in females.</p