Prediction model and treatment of high‑output ileostomy in colorectal cancer surgery

Fujino,Shiki; Miyoshi,Norikatsu; Ohue,Masayuki; Takahashi,Yuske; Yasui,Masayoshi; Sugimura,Keijiro; Akita,Hirohumi; Takahashi,Hidenori; Kobayashi,Shogo; Yano,Masahiko; Sakon,Masato

doi:10.3892/mco.2017.1336

Prediction model and treatment of high‑output ileostomy in colorectal cancer surgery

Authors:
- Shiki Fujino
- Norikatsu Miyoshi
- Masayuki Ohue
- Yuske Takahashi
- Masayoshi Yasui
- Keijiro Sugimura
- Hirohumi Akita
- Hidenori Takahashi
- Shogo Kobayashi
- Masahiko Yano
- Masato Sakon
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Affiliations: Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita, Osaka 565‑0871, Japan, Department of Surgery, Osaka International Cancer Institute, Osaka 541‑8567, Japan
Published online on: July 19, 2017 https://doi.org/10.3892/mco.2017.1336
Pages: 468-472

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Abstract

The aim of the present study was to examine the risk factors of high‑output ileostomy (HOI), which is associated with electrolyte abnormalities and/or stoma complications, and to create a prediction model. The medical records of 68 patients who underwent colorectal cancer surgery with ileostomy between 2011 and 2016 were retrospectively investigated. All the patients underwent surgical resection for colorectal cancer at the Osaka Medical Center for Cancer and Cardiovascular Diseases (Osaka, Japan). A total of 7 patients with inadequate data on ileostomy output were excluded. Using a group of 50 patients who underwent surgery between 2011 and 2013, the risk of HOI was classified by a decision tree model using a partition platform. The HOI prediction model was validated in an additional group of 11 patients who underwent surgery between 2014 and 2016. Univariate analysis of clinical factors demonstrated that young age (P=0.003) and high white blood cell (WBC) count (P<0.001) after surgery were significantly correlated with HOI. Operative factors, such as surgical procedure, approach, operative time and blood loss, were not significantly correlated with HOI. Using these clinical factors, the risk of HOI was classified by statistical partition. In this model, three factors (gender, age and WBC on postoperative day 1) were generated for the prediction of HOI. The patients were classified into five groups, and HOI was observed in 0‑88% of patients in each group. The area under the curve (AUC) was 0.838. The model was validated by an external dataset in an independent patient group, for which the AUC was 0.792. In conclusion, HOI patients were classified and an HOI prediction model was developed that may help clinicians in postoperative care.

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