RESUMO
BACKGROUND: Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care. AIM: To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR. METHODS: A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models. RESULTS: Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model. CONCLUSION: Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.
RESUMO
CXC chemokine receptor 6 (CXCR6), a seven-transmembrane domain G-protein-coupled receptor, plays a pivotal regulatory role in inflammation and tissue damage through its interaction with CXC chemokine ligand 16 (CXCL16). This axis is implicated in the pathogenesis of various fibrotic diseases and correlates with clinical parameters that indicate disease severity, activity, and prognosis in organ fibrosis, including afflictions of the liver, kidney, lung, cardiovascular system, skin, and intestines. Soluble CXCL16 (sCXCL16) serves as a chemokine, facilitating the migration and recruitment of CXCR6-expressing cells, while membrane-bound CXCL16 (mCXCL16) functions as a transmembrane protein with adhesion properties, facilitating intercellular interactions by binding to CXCR6. The CXCR6/CXCL16 axis is established to regulate the cycle of damage and repair during chronic inflammation, either through modulating immune cell-mediated intercellular communication or by independently influencing fibroblast homing, proliferation, and activation, with each pathway potentially culminating in the onset and progression of fibrotic diseases. However, clinically exploiting the targeting of the CXCR6/CXCL16 axis requires further elucidation of the intricate chemokine interactions within fibrosis pathogenesis. This review explores the biology of CXCR6/CXCL16, its multifaceted effects contributing to fibrosis in various organs, and the prospective clinical implications of these insights.
