Predictors of cognitive impairment in patients undergoing ileostomy for colorectal cancer: a retrospective analysis

Abstract

Background: Early detection of cognitive impairment in patients undergoing ileostomy for colorectal cancer may help improve patient outcomes and quality of life. Identifying risk factors and clinically accessible factors is crucial for prevention and treatment.

Objective: This retrospective study aimed to identify risk factors for post-operative cognitive impairment in patients undergoing ileostomy for colorectal cancer and to explore potential factors for its prevention and treatment.

Methods: A total of 108 cases were selected and included in the study. Patient data including general characteristics, disease stage, complications, and chemotherapy status were collected, and sleep quality and cognitive function were assessed using questionnaires and follow-up. Patients were randomly divided into training and validation groups. A random forest model was used to rank clinical features based on their contribution to predicting the prognosis of cancer-related cognitive impairment (CRCI). Nomograms were constructed using the support vector machine-recursive feature elimination (SVM-RFE) method, and the minimal root-mean-square error (RMSE) values were compared to select the best model. Regression analysis was performed to determine independent predictors.

Results: Significant differences were observed in age, body mass index (BMI), alcohol consumption, frequency of physical activity, comorbidity, and cancer-related anemia (CRA) between the CRCI and non-CRCI groups. Random forest analysis revealed that age, BMI, exercise intensity, PSQI scores, and history of hypertension were the most significant predictors of outcome. Univariate logistic regression analysis of 18 variables revealed that age, alcohol consumption, exercise intensity, BMI, and comorbidity were significantly associated with the outcome of CRCI (p < 0.05). Univariate and multivariate models with P-values less than 0.1 and 0.2, respectively, showed better predictive performance for CRCI. The results of univariate analysis were plotted on a nomogram to evaluate the risk of developing CRCI after colorectal cancer surgery. The nomogram was found to have good predictive performance. Finally, regression analysis revealed that age, exercise intensity, BMI, comorbidity, and CRA were independent predictors of CRCI.

Conclusions: This retrospective cohort study revealed that age, exercise intensity, BMI, comorbidity, CRA, and mobility are independent predictors of cognitive impairment in patients undergoing ileostomy for colorectal cancer. Identifying these factors and potential factors may have clinical implications in predicting and managing post-operative cognitive impairment in this patient population.

Keywords: Clinical predictive models; Cognitive impairment; Ileostomy; Postoperative; Retrospective analysis.

Figure 1. Flow diagram of this study.

Figure 2. Random forest model ranked the 19 variables based on their contribution to predicting the outcome.

Variables that differed between the groups are highlighted in yellow.

Figure 3. Construction of an SVM-RFE based outcome prediction model.

(A) Models were constructed using 18 variables relevant for outcome prediction according to SVM-RFE; (B) for the training set, test set, and overall set, SVM models constructed based on filtered variables performed well; (C) these 18 screened variables showed good predictive power for outcome based on univariate logistic regression analysis; (D) clinical application of the SVM model was determined by a decision curve analysis.

Figure 4. Construction of an SVM-RFE based outcome prediction model.

(A) Models were constructed using 18 variables relevant for outcome prediction according to SVM-RFE. (B) For the training set, test set, and overall set, SVM models constructed based on filtered variables performed well. (C) These 18 screened variables showed good predictive power for outcome based on univariate logistic regression analysis. (D) Clinical application of the SVM model was determined by a decision curve analysis.

Figure 5. (A–C) Nomogram prediction model.

CRCI risk can be assessed by using a nomogram prediction model based on multifactorial logistics.

Figure 6. Evaluation of a multifactor logistic regression model for CRCI prediction.

(A) ROC analysis shows that the constructed model performs well on the training, test, and overall data sets. (B) Model predictions and true results are well aligned on the calibration curve. (C) The decision curve indicates that the model is clinically applicable.