Abstract

IntroductionIn patients with left-sided obstructive colon cancer (LSOCC), a stoma is often constructed as part of primary treatment, but with a considerable risk of becoming a permanent stoma (PS). The aim of this retrospective multicentre cohort is to identify risk factors for a PS in LSOCC and to develop a pre- and postoperative prediction model for PS. Materials and methodsData was retrospectively obtained from 75 hospitals in the Netherlands. Patients who had curative resection of LSOCC between January 1, 2009 to December 31, 2016 were included with a minimum follow-up of 6 months after resection. The interventions analysed were emergency resection, decompressing stoma or stent as bridge-to-elective resection. Main outcome measure was presence of PS at the end of follow-up. Multivariable logistic regression analysis was performed to identify risk factors for PS at primary presentation (T0) and after resection, in patients having a stoma in situ (T1). These risk factors were used to construct a web-based prediction tool. ResultsOf 2099 patients included in the study (T0), 779 had a PS (37%). A total of 1275 patients had a stoma in situ directly after resection (T1), of whom 674 had a PS (53%). Median follow-up was 34 months. Multivariable analysis showed that older patients, female sex, high ASA-score and open approach were independent predictors for PS in both the T0 and T1 population. Other predictors at T0 were sigmoid location, low Hb, high CRP, cM1 stage, and emergency resection. At T1, subtotal colectomy, no primary anastomosis, not receiving adjuvant chemotherapy and high pTNM stage were additional predictors. Two predictive models were built, with an AUC of 0.74 for T0 and an AUC of 0.81 for T1. ConclusionsPS is seen in 37% of the patients who have resection of LSOCC. In patients with a stoma in situ directly after resection, 53% PS are seen due to non-reversal. Not only baseline characteristics, but also treatment strategies determine the risk of a PS in patients with LSOCC. The developed predictive models will give physicians insight in the role of the individual variables on the risk of a PS and help in informing the patient about the probability of a PS.

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