Risk Factors for Pannus Formation in the Post-Bariatric Population

Background. As the incidence of obesity has increased so has the rate of bariatric surgery.¹ Following massive weight loss a high percentage of patients develop redundant cutaneous skin folds. In addition to aesthetic concerns, excessive tissue mass can cause significant medical morbidity.^{2, 3} This study aimed to identify clinical measures predicting the need for panniculectomy.

Methods. Patient factors associated with symptomatic and medically non-responsive pannus were identified through a retrospective review of 168 post-bariatric surgery patients with at least one year of follow-up. Measures included age, gender, height, weight, BMI, resected pannus weight, and medical co-morbidities.

Results. 168 patients (154 female, 14 male) with a mean age of 45.0±9.9 years underwent laproscopic Roux-en-Y gastric bypass surgery and 138 (82%) subsequently developed symptomatic panniculi requiring panniculectomy. Spearman’s rank-correlation test for association with pannus mass was significant for age at bariatric surgery (ρ=0.154, p=0.033), height (ρ=0.155, p=0.031), pre-op weight (ρ=0.217, p=0.005), pre-op BMI (ρ=0.149, p=0.038), one year post-bariatric surgery BMI (ρ=0.189, p=0.032), and change in BMI one year after bariatric surgery (ρ=-0.225, p=0.014). No significant differences were found among included measures when comparing patients that received panniculectomy to those that did not. However, chi-squared analysis trended towards significance for hypertension (χ²=3.022, p=0.064.), with rates more prevalent among panniculectomy patients.

Conclusion. There are no criteria to identify which post-bariatric surgery patients will eventually develop a symptomatic pannus necessitating a panniculectomy. Consequently, insurance coverage for panniculectomy is limited to those with disabling panniculi that has not responded to medical treatment.⁴ This study identifies predictive clinical measures, which may allow for earlier patient identification. By expanding on these findings it may be possible to arrive at a predictive model that may benefit patients and insurance companies by eliminating unnecessary discomfort while reducing healthcare expenditures.