Establishment of Difficult Caudal Epidural Blockade Prediction Model

Area of Science:

• Anesthesiology
• Medical Imaging
• Predictive Modeling

Background:

• Developing a predictive model for difficult caudal epidural blockade (CEB) is crucial when ultrasound is unavailable.
• Ultrasound's role in managing difficult CEB requires further verification.

Purpose of the Study:

• To develop and validate a predictive difficult caudal epidural blockade (pDCEB) model.
• To assess the effectiveness of ultrasound in reducing difficult CEB in high-risk patients.

Main Methods:

• A three-phase study involving 289 patients undergoing caudal epidural anesthesia.
• Development and validation of a predictive scoring system using logistic regression and ROC curves.
• Randomized trial comparing ultrasound-guided vs. landmark-guided CEB in high-risk patients (pDCEB score ≥3).

Main Results:

• The incidence of difficult CEB was 14.98%.
• Key predictors for difficult CEB included unclear sacral hiatus/cornua palpation, multiple sacral hiatuses, and prior difficult CEB history.
• The pDCEB model demonstrated good predictive ability (AUC 0.889 in development, 0.862 in validation).
• Ultrasound significantly reduced difficult CEB incidence in high-risk patients (9.38% vs. 55.56%, p < 0.001).

Conclusions:

• A novel pDCEB score effectively predicts difficult CEB.
• Ultrasound is essential for patients with a high pDCEB score (≥3) to minimize difficult CEB.
• The study provides a valuable tool for anesthesiologists to anticipate and manage CEB challenges.