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,ble...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 follow-ing ileocecal resection in patients with Crohn’s disease(CD).This prediction re-lied on a machine learning(ML)model trained on a cohort that integrated a no-mogram 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 preope-rative serum albumin levels,and prolonged operation duration.Applying ML ap-proaches to predict surgical recurrence have the potential to enhance patient risk stratification and facilitate the development of preoperative optimization strate-gies,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 mole-cular biomarkers in future research efforts.展开更多
文摘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 follow-ing ileocecal resection in patients with Crohn’s disease(CD).This prediction re-lied on a machine learning(ML)model trained on a cohort that integrated a no-mogram 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 preope-rative serum albumin levels,and prolonged operation duration.Applying ML ap-proaches to predict surgical recurrence have the potential to enhance patient risk stratification and facilitate the development of preoperative optimization strate-gies,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 mole-cular biomarkers in future research efforts.
