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Ann Clin Nutr Metab : Annals of Clinical Nutrition and Metabolism

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2 "Seung-Wan Ryu"
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Original Article
Development and Internal/External Validation of a Prediction Model for Weight Loss Following Gastric Cancer Surgery: A Multicenter Retrospective Study
Ji-Hyeon Park, Seong-Ho Kong, Do Joong Park, Han-Kwang Yang, Jong Won Kim, Ki Bum Park, In Cho, Sun-Hwi Hwang, Dong-Wook Kim, Su Mi Kim, Seung-Wan Ryu, Seong Chan Gong, Pil Young Jung, Hoon Ryu, Sung Geun Kim, Chang In Choi, Dae-Hwan Kim, Sung-IL Choi, Ji-Ho Park, Dong Jin Park, Gyu-Yeol Kim, Yunhee Choi, Hyuk-Joon Lee
Ann Clin Nutr Metab 2022;14(2):55-65.   Published online December 1, 2022
DOI: https://doi.org/10.15747/ACNM.2022.14.2.55
AbstractAbstract PDF
Purpose: To develop an individualized model for predicting the extent of unintentional weight loss following gastrectomy in patients with gastric cancer based on related risk factors and to externally validate this model using multicenter clinical data in Korea.
Materials and Methods: Among gastric cancer patients who underwent curative gastrectomy at 14 different gastric cancer centers, clinical data from patients with more than one weight measurement during the three-year follow-up period were retrospectively collected. Risk factors associated with weight loss in gastric cancer patients after gastrectomy were analyzed, and a predictive model was developed. Internal and external validation were performed.
Results: The data from 2,649 patients were divided into a derivation set (n=1,420 from Seoul National University Hospital) and validation set (n=1,229 from 13 different gastric cancers). Postoperative duration (six vs. 12, 24, or 36 months), sex (female vs. male), age, preoperative body mass index, type of surgery (pylorus-preserving vs. total, distal or proximal gastrectomy), and cancer stage (I vs. II or III) were included in the final prediction model. The model showed approximately 20% accuracy in predicting weight loss at each period: R2 at six, 12, 24 and 36 months after gastrectomy in internal validation=0.20, 0.21, 0.17, and 0.18, respectively, and in external validation=0.20, 0.22, 0.18, and 0.18, respectively. Calibration slopes of internal and external validation were 0.95 and 1.0, respectively.
Conclusion: Although predictive accuracy of the model did not reach an acceptable level, repeated external validation measurements showed high reliability. The model may serve as a basic reference in clinical practice.
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Review Article
Clinical Characteristics of Sarcopenia and Cachexia
Seung-Wan Ryu
J Clin Nutr 2017;9(1):2-6.   Published online June 30, 2017
DOI: https://doi.org/10.15747/jcn.2017.9.1.2
AbstractAbstract PDF

Sarcopenia, which is defined as a decrease in skeletal muscle mass and strength with aging, is an important risk factor in clinical medicine that is associated with mortality, and poor surgical and nonsurgical outcomes. Sarcopenia is now recognized as a multifactorial geriatric syndrome. Cachexia is defined as a metabolic syndrome with inflammation as the key feature, so cachexia can be an underlying condition of sarcopenia. Recently, cachexia has been defined as a complex metabolic syndrome associated with an underlying illness and characterized by the loss of muscle mass with or without a loss of fat mass. These two conditions overlap but are not the same. In clinical practice, many factors related to sarcopenia (decreased food intake, inactivity, and decreased hormones) are reported frequently in patients with cachexia. On the contrary, systemic inflammation, the core feature of cachexia, can also be present in apparently healthy older sarcopenic patients. This suggests that new therapeutic approaches, alone or in combination, may be appropriate in both conditions.

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