Enhancing Patient Outcomes: A Novel Nomogram Prediction Model Based on Systemic Immune-Inflammation Index for...

Area of Science:

• Gastroenterology
• Oncology
• Surgical Innovation

Background:

• Endoscopic submucosal dissection (ESD) is a key treatment for early esophageal cancer.
• Postoperative esophageal stricture is a common complication, impacting patient quality of life and treatment success.
• Accurate prediction of esophageal stricture is needed to optimize patient management.

Purpose of the Study:

• To develop and validate a predictive model for esophageal stricture after ESD.
• To identify key clinical factors, including the Systemic Immune-inflammation Index (SII), associated with post-ESD stricture.
• To create a user-friendly nomogram for clinical application.

Main Methods:

• Retrospective analysis of 124 early esophageal cancer patients undergoing ESD.
• Logistic regression to identify risk factors for esophageal stricture.
• Construction and validation of a nomogram incorporating lesion depth, length, resection range, and SII using ROC curves, calibration curves, and DCA.

Main Results:

• Lesion infiltration depth, longitudinal length, circumferential resection range, and SII were independent risk factors for post-ESD esophageal stricture.
• The optimal SII cutoff for prediction was 312.67.
• The developed nomogram demonstrated satisfactory predictive performance and good agreement between predicted and observed outcomes.

Conclusions:

• A novel nomogram based on SII and other clinical factors accurately predicts esophageal stricture post-ESD.
• This predictive tool offers intuitive guidance for clinicians.
• The nomogram has the potential to improve treatment strategies and patient outcomes.