Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions.

Abstract

This editorial offers commentary on the article which aimed to forecast the likelihood of short-term major postoperative complications (Clavien-Dindo grade ≥ III), including anastomotic fistula, intra-abdominal sepsis, bleeding, and intestinal obstruction within 30 days, as well as prolonged hospital stays following ileocecal resection in patients with Crohn's disease (CD). This prediction relied on a machine learning (ML) model trained on a cohort that integrated a nomogram predictive model derived from logistic regression analysis and a random forest (RF) model. Both the nomogram and RF showed good performance, with the RF model demonstrating superior predictive ability. Key variables identified as potentially critical include a preoperative CD activity index ≥ 220, low preoperative serum albumin levels, and prolonged operation duration. Applying ML approaches to predict surgical recurrence have the potential to enhance patient risk stratification and facilitate the development of preoperative optimization strategies, ultimately aiming to improve post-surgical outcomes. However, there is still room for improvement, particularly by the inclusion of additional relevant clinical parameters, consideration of medical therapies, and potentially integrating molecular biomarkers in future research efforts.