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Abstrato

Laparoscopic Adjustable Gastric Banding is more Effective in Body Mass Index<40 Kg/m2 for Short Term Weight Loss

Ooi Tong Li, Hnin Hnin Oo, Lucy Kong RN, Anton Cheng and Kee Yuan Ngiam

Background: Obesity is a global health problem. Laparoscopic Adjustable Gastric Banding (LAGB) is a type of bariatric surgery that is effective for weight loss and control of co-morbidities. Long-term results of LAGB in different obesity groups have been widely studied in Western populations but not in a multi-racial Asian population such as in Singapore. This paper aims to compare the outcomes of LAGB and describe its complications in obese Singaporean patients with Body Mass Index (BMI) <40 kg/m2 vs BMI ≥ 40 kg/m2. Methods: 360 patients underwent LAGB surgery at two institutions in Singapore from June 2001 to July 2011. The patients were followed up for ten years post-surgery, out of which data from five years post-LAGB were analysed. Percentage weight loss (%WL), percentage excess weight loss (%EWL), morbidity and mortality were explored in both groups retrospectively. Results: Patients in Group A (BMI <40 kg/m2) were compared with patients in Group B (BMI ≥ 40 kg/m2). A significant difference in %EWL between the groups was noted at one year post-surgery where Group A achieved 30% EWL while Group B achieved 20.8% EWL (p-value=0.01). No other significant differences in %WL and %EWL between the two groups were noted in subsequent years (p-value >0.05). At the end of five years follow-up, Group A achieved 18.8%WL and 52.6% EWL while Group B achieved 18.7% WL and 34.9% EWL. In both groups, peak %WL was attained at 3 to 4 year follow-up before it tapered. A total of 90 patients (25%) developed complications during the study, with seven mortalities on follow-up, out of which two were band-related. Conclusion: LAGB is an effective bariatric surgical option for weight loss in obese Singaporean patients over a short-term but it is more beneficial for patients with BMI <40 kg/m2.

Isenção de responsabilidade: Este resumo foi traduzido usando ferramentas de inteligência artificial e ainda não foi revisado ou verificado.