Nomogram-based prediction of rebleeding in small bowel bleeding patients: the \u2018PRSBB\u2019 score

Genta Uchida,Osamu Watanabe,Hiroki Kawashima,Kazuhiro Furukawa,Yoshiki Hirooka,Takuya Ishikawa,Takeshi Yamamura,Hidemi Goto,Kohei Funasaka,Hiroki Suhara,Eizaburo Ohno,Masanao Nakamura,Masanobu Matsushita,Ryoji Miyahara

doi:10.1038/s41598-018-24868-0

Genta Uchida, Osamu Watanabe + Show 12 more

Open Access

https://doi.org/10.1038/s41598-018-24868-0

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Journal: Scientific Reports	Publication Date: Apr 23, 2018
Citations: 8	License type: open-access

Affiliation: Nagoya University, Nagoya University Hospital

Abstract

Small bowel capsule endoscopy (SBCE) and balloon-assisted endoscopy (BAE) have revolutionized the diagnosis and treatment of small bowel bleeding (SBB), allowing access to the small bowel and identification of specific bleeding lesions. However, some patients experience rebleeding after small bowel investigation, and there are no definitive algorithms for determining the most appropriate follow-up strategy in SBB patients. We developed and validated a nomogram that can predict rebleeding risk and be used to develop a risk-stratified follow-up strategy in SBB patients. A retrospective study was performed using data from 401 SBB patients who underwent SBCE at Nagoya University Hospital. We developed and internally validated a predictive model for rebleeding in the form of a nomogram using Cox regression models and a bootstrap resampling procedure. Optimal risk factors were selected according to the least absolute shrinkage and selection operator (LASSO). The LASSO method identified 8 independent predictors of rebleeding that could be assessed to obtain a ‘predicting rebleeding in SBB’, or ‘PRSBB’ score: age, sex, SBB type, transfusion requirement, cardiovascular disease, liver cirrhosis, SBCE findings, and treatment. The c-statistic for the predictive model was 0.681. In conclusion, our PRSBB score can help clinicians devise appropriate follow-up plans.

Highlights

Small bowel bleeding (SBB) accounts for approximately 5% of gastrointestinal bleeding and is frequently caused by a lesion in the small bowel[1]
The strengths of this study were as follows: (1) optimal predictors could be selected using the least absolute shrinkage and selection operator (LASSO) method, preventing data overfitting that can occur when the Cox regression model is used; (2) the development of the small bowel bleeding (SBB) score can allow clinicians to inform patients of their rebleeding risk and to develop individualized follow-up strategies based on that risk; and (3) risk stratification according to PRSBB scores could be useful in forthcoming prospective clinical trials
Several risk factors for rebleeding after SBB have been reported in previous studies, they were identified with multivariate analysis using a stepwise selection procedure that can suffer from overfitting

Summary

Introduction

Small bowel bleeding (SBB) accounts for approximately 5% of gastrointestinal bleeding and is frequently caused by a lesion in the small bowel[1]. SBCE allows for noninvasive evaluation of the entire small bowel in 79–90% of patients with suspected small bowel bleeding[2], and is useful in selecting patients who are likely to benefit from BAE due to its high negative predictive value[3]. SBCE and BAE can allow access to the small bowel and enable effective treatment of SBB by identifying specific bleeding lesions, rebleeding has still been reported to occur in 13–20% of cases after small bowel investigation[9,10]. The purpose of this study was to: (1) develop and internally validate a model that predicts rebleeding risk in SBB patients after small bowel investigation, and (2) incorporate the findings into a nomogram that can be used in clinical practice to offer individualized information to patients and develop an appropriate follow-up plan

Objectives

Results

Conclusion