Evaluation and analysis of neurocognitive dysfunction in patients with colorectal cancer after radical resection: A retrospective study

BACKGROUND With the continuous progress of colorectal cancer treatment technology, the survival rate of patients has improved significantly, but the problem of postoperative neurocognitive dysfunction has gradually attracted attention. AIM To analyze the risk factors for delayed postoperative neurocognitive recovery (DNR) after laparoscopic colorectal cancer surgery and constructed a risk prediction model to provide an evidence-based reference for the prevention and treatment of DNR after laparoscopic colorectal cancer surgery. METHODS The clinical data of 227 patients with colorectal cancer who underwent laparoscopic surgery and regional cerebral saturation oxygenation (rScO(2)) monitoring at our hospital from March 2020 to July 2022 were retrospectively analyzed. Common factors and potential factors affecting postoperative DNR were used as analysis variables, and univariate analysis and multifactor analysis were carried out step by step to determine the predictors of the model and construct a risk prediction model. The predictive performance of the model was assessed by the receiver operating characteristic (ROC) curve, the calibration curve was used to assess the fit of the model to the data, and a nomogram was drawn. In addition, 30 patients who met the inclusion and exclusion criteria from January 2023 to July 2023 were selected for external verification of the prediction model. RESULTS The incidence of postoperative DNR in the modeling group was 15.4% (35/227). Multivariate analysis revealed that age, years of education, diabetes status, and the lowest rScO(2) value were the independent influencing factors of postoperative DNR (all P < 0.05). Accordingly, a DNR risk prediction model was constructed after laparoscopic colorectal cancer surgery. The area under the ROC curve of the model was 0.757 (95%CI: 0.676-0.839, P < 0.001), and the Hosmer-Lemeshow test of the calibration curve suggested that the model was well fitted (P = 0.516). The C-index for external validation of the row was 0.617. CONCLUSION The DNR risk prediction model associated with rScO(2) monitoring can be used for individualized assessment of patients undergoing laparoscopic colorectal cancer surgery and provides a clinical basis for the prevention of DNR after surgery.