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Predicting the Panniculectomy Patient
Christopher W. Chung, B.A., Russell E. Kling, B.A., Wesley N. Sivak, M.D., Ph.D., Jeffrey A. Gusenoff, M.D., and J. Peter Rubin, M.D.
Department of Plastic Surgery, University of Pittsburgh
2013-02-28
Presenter: Christopher Chung
Affidavit:
All of the above work represents the original work of the listed authors.
Director Name: Joseph Losee, M.D.
Author Category: Medical Student
Presentation Category: Clinical
Abstract Category: General Reconstruction
Background. As the incidence of obesity has increased so has the rate of bariatric surgery. 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. 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 gastric bypass surgery. 82% developed a symptomatic pannus 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-operative 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 post-bariatric surgery (ρ=-0.225, p=0.014).
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. 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.
