Predictive model of bleeding following endoscopic sphincterotomy for the treatment of choledocholithiasis in hemodialysis patients: A retrospective multicenter study. Issue 5 (17th May 2020)

• Record Type: Journal Article
• Authors: Nakaji, So; Okawa, Yoshihiro; Nakamura, Kenji; Itonaga, Masahiro; Inase, Masami; Sugiyama, Harutoshi; Suzuki, Rei; Yamauchi, Kenji; Matsui, Hiroki; Hirata, Nobuto; Saito, Junko; Ishii, Naoki; Tsuyuguchi, Toshio; Kato, Hironari; Kitano, Masayuki; Kato, Naoya; Ohira, Hiromasa; Okada, Hiroyuki; Torimura, Takuji; Maguchi, Hiroyuki
• Is Part Of: JGH open. Volume 4:Issue 5(2020)
• Page Start: 915
• Page End: 922
• Publication Date: 2020-05-17
• Subjects: choledocholithiasis -- hemodialysis -- postendoscopic sphincterotomy bleeding
• DOI: 10.1002/jgh3.12363
• Languages: English
• ISSNs: 2397-9070

Abstract

Abstract: Background and Aim: Although hemodialysis (HD) is a strong risk factor for postendoscopic sphincterotomy (ES) bleeding, additional risk factors in HD patients remain unclear. There is no model for predicting post‐ES bleeding risk in HD patients. Therefore, we conducted a retrospective multicenter study to reveal these risk factors and develop a predictive model of post‐ES bleeding in HD patients. Methods: We retrospectively reviewed the medical records of HD patients who underwent ES at eight hospitals between January 2006 and December 2016, with post‐ES bleeding as the main outcome measure. Univariate analyses were performed to extract possible risk factors for post‐ES bleeding. Factors that were clinically important and statistically significant in our univariate analyses were then included in our logistic regression analysis for the development of a multivariate predictive model of post‐ES bleeding. This predictive model was visualized using a predictive nomogram. Results: Post‐ES bleeding occurred in 20 (16.3%) of 123 HD patients. Based on clinically important factors and the results of our univariate analyses, platelet count, prothrombin time (international normalized ratio), and HD duration were included in our predictive model of post‐ES bleeding. Receiver operating characteristic analysis found that this model had an area under the curve of 0.715 (95% confidence interval, 0.609–0.822). We developed a predictive nomogram based on these results. Conclusions: We demonstrated that post‐ES bleeding is more common in HD patients than in the general population and succeeded in constructing a predictive model that can effectively identify HD patients at risk of post‐ES bleeding. Abstract : We conducted a retrospective multicenter study to reveal these risk factors and develop a predictive model of post‐ES bleeding in HD patients.