A predictive model for patients with median arcuate ligament syndrome.

BACKGROUND

Due to the rarity of median arcuate ligament (MAL) syndrome, patient selection for surgery remains difficult. This study provides a predictive model to optimize patient selection and predict outcomes following a MAL release.

METHODS

Prospective data from patients undergoing a MAL release included demographics, radiologic studies, and SF-36 questionnaires. Successful postoperative changes in SF-36 was defined as an improvement > 10% in the total SF-36 score. A logistic regression model was used to develop a clinically applicable table to predict surgical outcomes. Celiac artery (CA) blood flow velocities were compared pre- and postoperatively and Pearson correlations were examined between velocities and SF-36 score changes.

RESULTS

42 patients underwent a laparoscopic MAL release with a mean follow-up of 28.5 ± 18.8 months. Postoperatively, all eight SF-36 scales improved significantly. The logistic regression model for predicting surgical benefit was significant (p = 0.0244) with a strong association between predictors and outcome (R = 0.36). Age and baseline CA expiratory velocity were significant predictors of improvement and predicted clinical improvement. There were significant differences between pre- and postoperative CA velocities. Postoperatively, the bodily pain scale showed the most significant increase (64%, p < 0.0001). A table was developed using age and preoperative CA expiratory velocities to predict clinical outcomes.

CONCLUSIONS

Laparoscopic MAL produces significant symptom improvement, particularly in bodily pain. This is one of the first studies that uses preoperative data to predict symptom improvement following a MAL release. Age and baseline CA expiratory velocity can be used to guide postoperative expectations in patients with MAL syndrome.