Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trial

The appropriate use of temporary diverting ileostomy (TDI) in rectal cancer surgery lacks standardized criteria. A randomized controlled trial was performed to evaluate whether the Risk-Guided Temporary Ileostomy Decision (RTID) system, a machine learning-based anastomotic leakage (AL) prediction model, could improve the suitability of TDI utilization. A total of 872 patients with stage I–III rectal cancer undergoing anterior resection were randomized 1:1 to surgeon discretion versus RTID-guided decisions. The final analysis included 750 patients (control, n = 368; RTID, n = 382). The RTID group showed lower overall TDI rate (18.6% vs. 40.5%; P < 0.001) and unnecessary stoma formation (17.7% vs. 41.3%; P < 0.001). Although a numerical increase in necessary TDI use was observed (55.6% vs. 10.0%), this difference was not significant (P = 0.057). Critically, the incidence of the co-primary safety outcome, AL, was comparable between the RTID and control groups (2.4% vs. 2.7%; P = 0.753), indicating that the pre-specified TDI reduction endpoint was met, while the study was underpowered to formally test non-inferiority for AL. RTID offers an objective tool to support TDI decision-making without a compromise in safety. The present study was registered on ClinicalTrials.gov (no. NCT04999007) on August 8, 2021. Currently, there are no reliable strategies to identify patients undergoing rectal cancer surgery who would derive the most benefit from temporary diverting ileostomy (TDI). Here, the authors report a randomised controlled trial investigating the use the RTID system, a machine learning-based anastomotic leakage prediction model, to improve rectal cancer patient selection for TDI.