Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence
| العنوان: | Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence |
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| المؤلفون: | Uday C. Ghoshal, Ankur Gupta, Sushmita Rai, Akshay Kulkarni |
| المصدر: | JGH Open, Vol 4, Iss 5, Pp 889-897 (2020) JGH Open: An Open Access Journal of Gastroenterology and Hepatology |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | medicine.medical_specialty, corticosteroid, medicine.drug_class, medicine.medical_treatment, Serum albumin, RC799-869, Inflammatory bowel disease, Gastroenterology, 03 medical and health sciences, 0302 clinical medicine, immunosuppressants, inflammatory bowel disease, Internal medicine, medicine, medical treatment, Hydrocortisone, Colectomy, Univariate analysis, Hepatology, biology, business.industry, outcome predictors, Albumin, deep learning, Original Articles, Diseases of the digestive system. Gastroenterology, medicine.disease, Ulcerative colitis, 030220 oncology & carcinogenesis, biology.protein, Corticosteroid, Original Article, 030211 gastroenterology & hepatology, business, artificial neural network, medicine.drug |
| الوصف: | Background and Aim About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options. Methods Data of 263 patients (mean age 37.0 ± 14.0‐years, 176, 77% male) with acute severe UC admitted during a 12‐year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN). Results Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C‐reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower. Conclusion It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors. An early prediction of response of medical treatment, which occurs in 15% of patients with acute severe ulcerative colitis (UC) may help the treating team and the patients and their families to prepare for alternative treatment options. In this study on 263 patients with acute severe UC, we found that it is possible to predict the response to medical treatment in patients with UC using linear and non‐linear (artificial neural network) modeling techniques. Serum albumin and Hb are strong predictive factors. |
| اللغة: | English |
| تدمد: | 2397-9070 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5083bbd3009aa47ec3b003fe46ebe04cTest https://doaj.org/article/f8b65240416248b7a6c8fae29ca755a2Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....5083bbd3009aa47ec3b003fe46ebe04c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23979070 |
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