A prediction score model of difficult endoscopic submucosal dissection for early esophageal cancer and precancerous lesions
2024

Prediction Model for Difficult Esophageal Endoscopic Submucosal Dissection

Sample size: 742 publication Evidence: moderate

Author Information

Author(s): Gao Li, Su Jing, Ding Hailong, Qian Haisheng, Zhang Guoxin, Yan Jin

Primary Institution: The First Affiliated Hospital with Nanjing Medical University

Hypothesis

Can a prediction model be established to triage difficult esophageal endoscopic submucosal dissection (ESD) to reduce complications?

Conclusion

A prediction model for assessing the difficulty of esophageal ESD was successfully established, providing a scientific basis for clinical management to reduce complications.

Supporting Evidence

  • The model was validated in a separate cohort with an area under the ROC curve of 0.719.
  • Risk factors included lesion length, circumference, and preoperative invasion depth.
  • Patients with a higher score had a significantly higher probability of difficult ESD.

Takeaway

Doctors created a scoring system to help predict how hard it will be to remove cancer from the esophagus using a special technique, which can help avoid problems during surgery.

Methodology

This retrospective study analyzed data from 742 patients who underwent ESD for early esophageal cancer and precancerous lesions, using logistic regression to identify predictors of technical difficulty.

Potential Biases

Selection bias may exist due to the study being conducted at a top-ranked hospital that receives more complex cases.

Limitations

The study was retrospective and conducted at a single center, which may affect the generalizability of the findings.

Participant Demographics

The study included 742 patients with a mean age of 64.58 years, consisting of 70.5% males and 29.5% females.

Statistical Information

P-Value

p<0.001

Confidence Interval

95% CI, 0.726–0.811

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1080/07853890.2024.2446706

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