Validation of GLIM malnutrition criteria in cancer patients undergoing major abdominal surgery: a large-scale prospective study
Background & aims - The Global Leadership Initiative on Malnutrition (GLIM) is a new framework to evaluate nutritional status. It has been validated mostly by retrospective studies, which often failed to follow the framework as recommended due to their retrospective nature. This study aims to va...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 January 2022
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| In: |
Clinical nutrition
Year: 2022, Jahrgang: 41, Heft: 3, Pages: 599-609 |
| ISSN: | 1532-1983 |
| DOI: | 10.1016/j.clnu.2022.01.010 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.clnu.2022.01.010 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0261561422000127 
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| Verfasserangaben: | Shanjun Tan, Junjie Wang, Feng Zhou, Min Tang, Jiahao Xu, Yanni Zhang, Mingyue Yan, Shuhao Li, Zhige Zhang, Guohao Wu |
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| 245 | 1 | 0 | |a Validation of GLIM malnutrition criteria in cancer patients undergoing major abdominal surgery |b a large-scale prospective study |c Shanjun Tan, Junjie Wang, Feng Zhou, Min Tang, Jiahao Xu, Yanni Zhang, Mingyue Yan, Shuhao Li, Zhige Zhang, Guohao Wu |
| 264 | 1 | |c 13 January 2022 | |
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| 520 | |a Background & aims - The Global Leadership Initiative on Malnutrition (GLIM) is a new framework to evaluate nutritional status. It has been validated mostly by retrospective studies, which often failed to follow the framework as recommended due to their retrospective nature. This study aims to validate GLIM with a large prospective study and investigate its role in predicting short-term surgical outcomes. - Methods - Patients who underwent abdominal surgery for digestive cancer in our center were prospectively included between January 2020 and December 2020. Data collected included demographic information, clinical and pathological information, lab results, and computed tomography scans. Muscle strength, physical performances, quality of life and cancer symptom burden were evaluated upon admission. Multiple tools for nutritional risk screening were used. Nutritional status was evaluated with Subjective Global Assessment (SGA) and GLIM. Postoperative outcomes collected included return of gastrointestinal function, postoperative length of stay, complications, 30-day readmission and 30-day mortality. - Results - A total of 1115 patients were evaluated with GLIM criteria. Inter-rater reliability was good [k = 0.76, 95% confidence interval (CI): 0.72-0.80]. The accuracy of GLIM diagnosis compared to the semi-gold standard SGA varied with the screening method used. GLIM with MNA-SF screening [area under the curve (AUC): 0.78] and GLIM without screening (AUC: 0.77) were the two most accurate protocols, and there was no significant difference between the two from a clinical perspective. GLIM can predict preoperative nutritional status, functional status, symptom burden and quality of life. It can also predict complications after surgery [odds ratio (OR) = 2.31, 95% CI: 1.67-3.21], especially infection related complications (OR = 2.19, 95% CI: 1.38-3.49) and wound healing related complications (OR = 2.54, 95% CI: 1.38-4.71). - Conclusions - GLIM malnutrition criteria showed good inner-rater reliability and moderate agreement with SGA. GLIM can be used to predict preoperative nutritional status, functional status, cancer related symptoms, and quality of life. It can also predict postoperative outcomes especially complications that are related to infection and wound healing. In surgical candidates, the screening process could potentially be skipped so that GLIM assessment can be faster, more accessible, and more sensitive. | ||
| 650 | 4 | |a Cancer | |
| 650 | 4 | |a GLIM | |
| 650 | 4 | |a Malnutrition criteria | |
| 650 | 4 | |a Surgery | |
| 650 | 4 | |a Validation | |
| 700 | 1 | |a Wang, Junjie |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhou, Feng |e VerfasserIn |0 (DE-588)1238499740 |0 (DE-627)1765921082 |4 aut | |
| 700 | 1 | |a Tang, Min |e VerfasserIn |4 aut | |
| 700 | 1 | |a Xu, Jiahao |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhang, Yanni |e VerfasserIn |4 aut | |
| 700 | 1 | |a Yan, Mingyue |e VerfasserIn |4 aut | |
| 700 | 1 | |a Li, Shuhao |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zhang, Zhige |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wu, Guohao |e VerfasserIn |4 aut | |
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