Nomogram-based Prediction Model for Acute Postoperative Pain Following Radical Resection of Colorectal Cancer: A Retrospective Cohort Study.
This study aimed to develop and validate a nomogram-based predictive model for acute postoperative pain (APP) in patients undergoing radical resection for colorectal cancer (CRC).
This was a retrospective cohort study designed to develop and validate a nomogram for predicting acute postoperative pain in patients undergoing radical resection for colorectal cancer.
This retrospective analysis included patients who underwent radical CRC resection between January 2021 and December 2022. The study population was divided into a training cohort (n = 126) and a validation cohort (n = 54) using a 7:3 ratio. Risk factors associated with APP were initially screened using least absolute shrinkage and selection operator regression. Subsequently, multivariable logistic regression analysis was conducted to construct the regression model. A nomogram was generated to visualize the predictive model. Model performance was assessed using calibration curves, the Hosmer-Lemeshow goodness-of-fit test, receiver operating characteristic curves with corresponding area under the curve (AUC) values, and decision curve analysis.
The incidence of acute postoperative pain (APP) in the training cohort was 20.63% (26/126). Elevated CA19-9 and AST levels were identified as independent risk factors, while intraoperative nerve blockade, remifentanil administration, and local anesthesia were associated with reduced risk. The nomogram model achieved AUC values of 0.909 and 0.852 in the training and validation cohorts, respectively, demonstrating high sensitivity, specificity, and clinical utility.
Five clinical variables were identified as predictors of APP following radical CRC resection. The validated nomogram offers a practical tool for early risk stratification and may support the implementation of individualized pain management strategies for patients undergoing CRC surgery.
This was a retrospective cohort study designed to develop and validate a nomogram for predicting acute postoperative pain in patients undergoing radical resection for colorectal cancer.
This retrospective analysis included patients who underwent radical CRC resection between January 2021 and December 2022. The study population was divided into a training cohort (n = 126) and a validation cohort (n = 54) using a 7:3 ratio. Risk factors associated with APP were initially screened using least absolute shrinkage and selection operator regression. Subsequently, multivariable logistic regression analysis was conducted to construct the regression model. A nomogram was generated to visualize the predictive model. Model performance was assessed using calibration curves, the Hosmer-Lemeshow goodness-of-fit test, receiver operating characteristic curves with corresponding area under the curve (AUC) values, and decision curve analysis.
The incidence of acute postoperative pain (APP) in the training cohort was 20.63% (26/126). Elevated CA19-9 and AST levels were identified as independent risk factors, while intraoperative nerve blockade, remifentanil administration, and local anesthesia were associated with reduced risk. The nomogram model achieved AUC values of 0.909 and 0.852 in the training and validation cohorts, respectively, demonstrating high sensitivity, specificity, and clinical utility.
Five clinical variables were identified as predictors of APP following radical CRC resection. The validated nomogram offers a practical tool for early risk stratification and may support the implementation of individualized pain management strategies for patients undergoing CRC surgery.
Authors
Wang Wang, Yang Yang, Zhu Zhu, Yang Yang, Wang Wang, Zhu Zhu, Shen Shen, Yi Yi
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