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 (R2  = 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.