Development and validation of a predictive model for postoperative pulmonary complications after colorectal cancer surgery: a retrospective study.
Postoperative pulmonary complications (PPCs) significantly impact patient outcomes after colorectal cancer (CRC) surgery. Studies on PPCs associated with CRC remain limited. This study was designed to create and verify a risk prediction model through the identification of factors associated with a higher risk of PPCs after CRC surgery.
Patients who underwent CRC surgery at Fujian Medical University Union Hospital from September 2019 through September 2021 were included in this study. They were divided into training and validation groups and categorized into the PPC group and non-PPC group. We used multivariable logistic regression analysis to determine independent predictors of PPCs. A nomogram was constructed using the identified risk factors and later validated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis to assess its predictive performance.
A total of 1861 patients were enrolled and allocated to training and testing sets at an 80:20 ratio. The analyzed results revealed that surgical site, surgical approach, duration of surgery, age, perioperative blood transfusion, asthma, and fasting plasma glucose were risk factors for PPCs in patients with CRC. The receiver operating characteristic curve areas under the curve (AUCs) for the training and validation sets were 0.734 and 0.732, respectively. We found that the model calibration curve showed favorable consistency, whereas decision curve analysis (DCA) revealed a substantial expected net benefit.
The model demonstrates high predictive accuracy and efficiency for PPCs in patients undergoing CRC surgery.
This study was registered at the Chinese Clinical Trials Registry ( http://www.chictr.org.cn ) on November 28, 2024, with registration number ChiCTR2400093107.
Patients who underwent CRC surgery at Fujian Medical University Union Hospital from September 2019 through September 2021 were included in this study. They were divided into training and validation groups and categorized into the PPC group and non-PPC group. We used multivariable logistic regression analysis to determine independent predictors of PPCs. A nomogram was constructed using the identified risk factors and later validated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis to assess its predictive performance.
A total of 1861 patients were enrolled and allocated to training and testing sets at an 80:20 ratio. The analyzed results revealed that surgical site, surgical approach, duration of surgery, age, perioperative blood transfusion, asthma, and fasting plasma glucose were risk factors for PPCs in patients with CRC. The receiver operating characteristic curve areas under the curve (AUCs) for the training and validation sets were 0.734 and 0.732, respectively. We found that the model calibration curve showed favorable consistency, whereas decision curve analysis (DCA) revealed a substantial expected net benefit.
The model demonstrates high predictive accuracy and efficiency for PPCs in patients undergoing CRC surgery.
This study was registered at the Chinese Clinical Trials Registry ( http://www.chictr.org.cn ) on November 28, 2024, with registration number ChiCTR2400093107.