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Nomogram-based Prediction Model for Acute Postoperative Pain Following Radical Resection of Colorectal Cancer: A
Chuanguang Wang1, Jinglin Yang1, Yayan Zhu2
1Department of Anesthesia, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China; Department of Anesthesia, Lishui Municipal Central Hospital, Lishui, Zhejiang, China.
Summary
This study developed a nomogram to predict acute postoperative pain (APP) in colorectal cancer (CRC) patients. The model, using five clinical variables, accurately identifies patients at high risk for APP.
Area of Science:
- Oncology
- Pain Management
- Surgical Research
Background:
- Acute postoperative pain (APP) is a significant concern for patients undergoing colorectal cancer (CRC) surgery.
- Effective prediction of APP is crucial for developing personalized pain management strategies.
- Current predictive tools for APP in CRC patients are limited.
Purpose of the Study:
- To develop and validate a nomogram-based predictive model for acute postoperative pain (APP) in patients undergoing radical resection for colorectal cancer (CRC).
- To identify independent risk factors and protective factors associated with APP in CRC patients.
- To provide a practical tool for early risk stratification of APP in CRC surgery.
Main Methods:
- Retrospective cohort study including 180 patients who underwent radical CRC resection (January 2021 - December 2022).
- Patients were divided into training (n=126) and validation (n=54) cohorts.
- Least absolute shrinkage and selection operator regression and multivariable logistic regression were used to construct and validate the nomogram model, assessing performance with AUC, calibration curves, and decision curve analysis.
Main Results:
- The incidence of APP in the training cohort was 20.63%.
- Elevated CA19-9 and AST levels were identified as independent risk factors for APP.
- Intraoperative nerve blockade, remifentanil, and local anesthesia were associated with reduced APP risk.
- The nomogram demonstrated high predictive accuracy with AUC values of 0.909 (training) and 0.852 (validation).
Conclusions:
- A validated nomogram incorporating five clinical variables can accurately predict APP in patients undergoing radical CRC resection.
- This nomogram serves as a valuable tool for early risk stratification of APP.
- The findings support the implementation of individualized pain management strategies for CRC surgical patients.
