Optimal Nutrition Formulas for Patients Undergoing Surgery for Colorectal Cancer: A Bayesian Network Analysis

Abstract

Optimal nutrition formulas for colorectal cancer patients underwent surgery remains uncertainty. We constructed an indirect comparison study to assess comparative efficacy of different immunonutrition formulas and standard nutrition in colorectal cancer patients underwent surgery. PubMed, the Cochrane Library, EMBASE, ClinicalTrials.gov and Web of Science databases were searched to identify RCTs that compared immunonutrition with standard nutrition or different immunonutrition formulas. Data on length of hospital stays (LOS), infectious complications (IC), noninfectious complications (NIC) and anastomotic leakage (AL) were extracted from the included RCTs for Bayesian network analysis using a random-effect model. Twelve articles that included 1032 individuals were incorporated into this study. The indirect comparison confirmed the potential improvement of arginine-based immunonutrition on IC (odds ratios [OR] = 0.43, 95%confidence interval [CI]: 0.17 to 0.95), glutamine on NIC (OR = 0.07 CI: 0.00 to 0.78) and LOS (MD=-3.91 CI: −6.33 to -1.69) and omega-3 polyunsaturated fatty acids on LOS (OR=-3.49 CI: −5.46 to -1.00). Results indicated that glutamine had the highest probability of reducing complications and hospital stays. As for colorectal cancer patients underwent surgery, this indirect comparison suggested some superiority of glutamine. Future more RCTs with larger scale are required to provide evidence for the optimal immunonutrition formulas.

Author Contributions

Xiao-han Jiang and Xi-jie Chen have made substantial contribution to conception and design of the study. Xin-you Wang and Yun-zhi Chen searched literature, extracted data from the collected literature and analyzed the data. Qin-qin Xie assessed each study. Jun-sheng Peng was the guarantor of overall content. All authors revised and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Guangzhou Minsheng Science and Technology Project (Grant no. 201803010040)