Sobutay E, Bilgiç Ç, Kabaoğlu B, Yavuz Y. Can Weight of The Resected Stomach Predict Weight Loss Results After Laparoscopic Sleeve Gastrectomy?
Surg Laparosc Endosc Percutan Tech 2024;
34:29-34. [PMID:
38306493 DOI:
10.1097/sle.0000000000001260]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/11/2023] [Indexed: 02/04/2024]
Abstract
BACKGROUND
Laparoscopic sleeve gastrectomy (LSG) is the most commonly performed bariatric procedure worldwide. Many factors have been investigated in the literature to predict weight loss outcomes after LSG. However, insufficient data regarding the resected stomach weight (RGW) exists. This retrospective study aimed to investigate the association between RGW and weight loss outcomes 1 year after LSG.
MATERIALS AND METHODS
Fifty-four patients who underwent LSG in a tertiary care center were evaluated retrospectively. The statistical analyses were performed to investigate the correlation between preoperative demographics, RGW, and the excess weight loss percentage (%EWL) and percent total weight loss (%TWL).
RESULTS
The mean RGW was 169.7±40.1, ranging from 101 to 295 grams. The RGW was significantly correlated with preoperative weight (r=0.486; P<0.001), body mass index (r=0.420; P=0.002), and age (r=0.327; P=0.01). However, RGW did not predict postoperative weight loss, as measured by percent total weight loss (%TWL) and percent excess weight loss (%EWL), respectively (r=0.044; P=0.75 and r=-0.216; P=0.11). Multiple linear regression analysis identified age as a negative predictor for both %TWL (β=-0.351, P=0.005) and %EWL (β=-0.265, P=0.03), while preoperative body mass index was a negative predictor for %EWL (β=-0.469, P<0.001).
CONCLUSION
The RGW, although correlated with patient characteristics, does not serve as a reliable predictor of postoperative weight loss in the first year after LSG. Further research is needed to improve predictive models and patient care in bariatric surgery.
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