A modified GLIM criteria-based nomogram for the survival prediction of Gastric Cancer Patients undergoing Surgical Resection

A modified GLIM criteria-based nomogram for the survival prediction of Gastric Cancer Patients undergoing Surgical Resection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A modified GLIM criteria-based nomogram for the survival prediction of Gastric Cancer Patients undergoing Surgical Resection Xi Luo, Bin Cai, Weiwei Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4348710/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study aimed to develop a comprehensive model based on five GLIM variables to predict the individual survival and provide more appropriate patient counseling. Methods: This retrospective cohort study included 210 gastric cancer (GC) patients undergoing radical resection, among whom 150 patients in the development cohort and 60 patients in the external validation cohort. C-reactive protein (CRP) as an inflammatory marker was included in GLIM criteria and a nomogram for predicting 5-year overall survival (OS) in GC patients was established. Results: Of the total 210 patients, 16 (7.62%) died within 5 years. CRP improved the sensitivity and accuracy of the survival prediction model (AUC=0.779, 0.563 to 0.849 for the model without CRP; AUC=0.896, 0.645 to 0.963 for the model adding CRP). Besides, a GLIM-based nomogram was established with an AUC of 0.896. The C-index for predicting OS was 0.804 (95% CI: 0.645 to 0.963), and the calibration curve fitted well. Decision curve analysis (DCA) showed the clinical utility of the nomogram based on GLIM. Conclusion: The addition of CRP improved the sensitivity and accuracy of the survival prediction model. The 5-year survival probability of GC patients undergoing radical resection can be reliably predicted by the nomogram presented in this study. GLIM C-reactive protein malnutrition gastric cancer overall survival prediction Figures Figure 1 Figure 2 Figure 3 Background As a common digestive tract tumor, gastric cancer (GC) has a high incidence in East Asia, especially in China, Japan, and South Korea [ 1 – 3 ]. According to the latest data in 2020, the incidence and mortality of GC in China ranked third among all malignancies [ 3 ]. To date, surgical resection remains the main treatment option for GC [ 3 ]. However, the stress of surgery can rapidly deplete the body’s nutrient reserves, thereby affecting the body's functional recovery and wound healing. Conditions such as neoadjuvant therapy implemented postoperatively may also contribute to the impairment of nutrient reserves, further affecting the patient’s recovery. In addition, it is well known that malnutrition is a common problem among patients with cancer [ 4 ]. The prevalence of malnutrition in patients with GC is the highest among all malignancies [ 5 ] due to factors such as digestive tract obstruction, delayed gastric emptying, impaired digestion and absorption, which directly affect nutrient uptake, digestion, and absorption [ 4 ]. Early diagnosis and timely intervention of malnutrition can significantly reduce medical costs, shorten hospital stays, improve treatment outcomes, and prolong patient survival [ 6 – 8 ]. Nevertheless, there have been no internationally recognized standards in the diagnosis of malnutrition. To identify malnutrition, different studies have used different assessment tools. Research in this field is significantly behind other disease areas [ 9 , 10 ]. Therefore, the European Society for Parenteral and Enteral Nutrition (ESPEN) and the American Society for Parenteral Enteral Nutrition (ASPEN) published the GLIM consensus on the diagnosis definition of malnutrition [ 11 , 12 ]. Since publication, validation has been reported in patients with head and neck [ 13 ], gastrointestinal [ 14 , 15 ], and pulmonary [ 16 , 17 ] tumors. However, none of these studies explored the diagnostic and predictive value of inflammatory indicators. More information on the choice of indicators for the evaluation of inflammation in etiological criteria is still pending. Besides, the GLIM criteria for identifying malnutrition require patients to meet at least one etiologic criterion and one phenotypic criterion [ 11 , 12 ]. The six different diagnostic combinations formed by the two etiologic and three phenotypic criteria of the GLIM consensus may lead to differences in the prediction of clinical outcome and survival. Accurate nutritional diagnosis is the prerequisite for rational nutritional therapy. Thus, a comprehensive model built based on these variables is required for patient counseling and survival prediction. In this study, the diagnostic value of C-reactive protein (CRP), mentioned several times in the GLIM consensus, was evaluated. Furthermore, a GLIM-based nomogram was established using the five indicators of GLIM. Methods Study population This single-center, observational, and hospital-based retrospective cohort study aimed to evaluate the prevalence of malnutrition in GC patients undergoing surgical resection as the first-line treatment and the correlation of malnutrition diagnosed by the GLIM criteria with overall survival (OS). All participants who met the following inclusion and exclusion criteria were consecutively enrolled between January 2015 and December 2019. Inclusion criteria: (1) aged ≥ 18 years old; (2) hospitalized patients properly diagnosed with GC and undergoing elective surgery; (3) never received surgery, radiotherapy, chemotherapy, and other anti-tumor treatments (including immunotherapy); (4) willing to participate in this study. Exclusion criteria: (1) aged < 18 years old; (2) with admission time no more than 48 hours; (3) suffered from severe heart, liver, and brain dysfunction; (4) suffered from active systemic infection; (5) hospitalized patients properly diagnosed with GC and undergoing palliative surgery; (6) refused to participate in this study. Data was obtained from the Electronic Medical Record (EMR) System and retrospectively analyzed. Within the first 48 hours after admission, general information, anthropometric data, laboratory results, existing comorbidities, nutrition-related data, and medical history of all patients were gathered and documented by doctors, nurses, and clinical dietitians. The oncotherapy-related data and follow-up information were also recorded in the EMR system. The eighth edition of the AJCC TNM staging system was used to determine all pathological staging. The primary outcome of this study was mortality in the 5-year follow-up survival cohort. All participants were monitored from the initial admission to death or until the end of November 2023. The primary outcome of this study was mortality in the 5-year follow-up survival cohort. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving patients were approved by the ethics committee of Tongde Hospital of Zhejiang Province (No.2022-147-JY). Written informed consent was obtained from all patients. Malnutrition diagnosed by GLIM criteria The GLIM criteria for the diagnosis of malnutrition require that individuals meet at least one etiologic criterion and one phenotypic criterion [ 11 , 12 ]. The GLIM criteria are summarized in Table 1 . All data were available from medical records and nutritional assessment records. Table 2 provides a list of the GLIM indicators for diagnosing and assessing the severity of nutritional status [ 18 , 19 ]. Table 1 Phenotypic and etiologic criteria for the diagnosis of malnutrition Phenotypic criteria Etiologic criteria Weight loss (%) Low BMI (kg/m 2 ) Reduced muscle mass Reduced food intake or assimilation Disease burden/ inflammation > 5% within the past 6 months, or > 10% beyond 6 months < 18.5 if < 70 years old, or < 20 if ≥ 70 years old Calf circumference < 33 cm in men or 1 week, or any reduction for > 2 weeks, or any chronic GI condition that adversely impacts food assimilation or absorption Acute disease/ injury or chronic disease-related inflammation (CRP > 10 mg/L) GLIM, the global leadership initiative on malnutrition; BMI, body mass index; GI, gastrointestinal; ER, energy requirements; CRP, C-reactive protein. Table 2 Parameters and thresholds used for GLIM severity grading in this study Grade Phenotypic criteria Weight loss (%) Low BMI (kg/m 2 ) Reduced muscle mass Stage I/Moderate malnutrition 5–10% within the past 6 months, or 10–20% beyond 6 months < 20 if < 70 years old, or < 22 if ≥ 70 years old Calf circumference < 33 cm in men or 10% within the past 6 months, or > 20% beyond 6 months < 18.5 if < 70 years old, or < 20 if ≥ 70 years old Not applicable, no Asian standards GLIM, the global leadership initiative on malnutrition; BMI, body mass index; CRP, C-reactive protein. Development and validation of a nomogram model The five indicators including two etiologic indexes and three phenotypic indexes were applied to establish a prediction model. The relationship between the GLIM criteria and OS was verified using a multivariate Cox regression analysis, then a nomogram based on each GLIM criterion's hazard ratio (HR) was created. Harrell's concordance index (C-index) was calculated by the bootstrap approach with 500 resamples to evaluate the discrimination of the nomogram. The predictive accuracy for the 5-year OS was examined using the area under receiver operating characteristic curve (AUC). Through a comparison of observed and predicted survival, the nomogram for 5-year OS was calibrated. Finally, decision curve analysis (DCA) was used to evaluate the clinical utility of the GLIM-based nomogram. Statistical analysis The data were analyzed with the statistical package R (The R Foundation; http://www.r-project.org ; version 3.6.3). Quantitative data were presented as mean ± standard deviation. Differences between the two groups were analyzed by the Students' t-test. Non-parametric tests (Mann Whitney or Kruskall Wallis) were utilized for variables that did not follow a normal distribution. A chi-square test was used to compare qualitative variables, with Fisher adjustment if necessary. Besides, OS data were analyzed using the Cox regression and Kaplan- Meier curve. To account for potential confounders, a multivariate Cox regression analysis was also carried out utilizing backward selection. Finally, a GLIM-based nomogram that was adjusted for potential confounders was built. The threshold of statistical significance was set at P -value < 0.05. Results The baseline characteristics of the patients are summarized in Table 3 . The nutritional status of the patients was retrospectively assessed using GLIM criteria. The incidence of malnutrition in GC patients was 40%, of which 58.33% were moderate malnutrition and 41.67% were severe malnutrition. Kaplan-Meier curve was used to analyze the relationship between GLIM and OS. The 5-year survival rate was 97.78% (88/90) in the normally nourished group and 85.00% (51/60) in the malnutrition group. Compared to patients in the normally nourished group, patients in the malnourished group had a poorer OS rate (Fig. 1 A). Additionally, the degree of malnutrition status was linked to OS (Fig. 1 B). Cox model validation showed that nutritional status was strongly related to an increased risk of mortality according to GLIM criteria. Severe malnutrition continued to be an independent predictive factor even after adjustment for confounding variables. Table 3 Characteristics of GC patients Variables Development cohort Validation cohort Total (n = 150) Normal (n = 90) Malnutrition (n = 60) Total (n = 60) Normal (n = 37) Malnutrition (n = 23) General information Age, years, mean ± SD 63.00 ± 11.43 59.51 ± 10.67 68.23 ± 10.58 63.05 ± 12.90 58.89 ± 10.58 69.74 ± 10.89 Sex, male, n (%) 103 (68.67) 65 (72.22) 38 (63.33) 44 (73.33) 26 (70.27) 18 (78.26) Education, above high school, n (%) 32 (21.33) 22 (24.22) 10 (16.67) 20 (33.33) 13 (35.14) 7 (30.43) LOS, days, median (IQR) 21 (19,27) 21 (19,24) 23 (19,31) 22 (19,30) 20 (16,24) 27 (21,32) Alcohol, yes, n (%) 39 (26.00) 28 (31.11) 11 (18.33) 10 (16.67) 8 (21.62) 2 (8.70) Smoking, yes, n (%) 43 (28.67) 27 (30.00) 16 (26.67) 15 (25.00) 9 (27.03) 6 (26.10) Family history of GC, yes, n (%) 15 (10.00) 10 (11.11) 5 (8.33) 4 (6.67) 3 (8.11) 1 (4.35) Chronic disease history Diabetes mellitus, n (%) 17 (11.33) 13 (14.44) 4 (6.67) 12 (20.00) 10 (27.03) 2 (8.70) Cardiovascular disease, n (%) 54 (36.00) 30 (33.33) 24 (40.00) 17 (28.33) 11 (29.73) 6 (26.09) Anemia, n (%) 13 (8.67) 5 (5.62) 8 (13.33) 5 (8.33) 1 (2.70) 4 (17.40) Other, n (%) 8 (5.33) 4 (4.44) 4 (6.67) 4 (6.67) 1 (2.70) 3 (13.04) Current disease and treatment Adenocarcinoma, n (%) 124 (82.67) 73 (81.11) 51 (85.00) 48 (80.00) 28 (75.68) 20 (86.96) SRCC, n (%) 20 (13.33) 14 (15.56) 6 (10.00) 9 (15.00) 7 (18.92) 2 (8.70) Others, n (%) 6 (4.00) 3 (3.33) 3 (5.00) 3 (5.00) 2 (5.41) 1 (4.35) Helicobacter Pylori, positive, n (%) 24 (16.00) 17 (18.89) 7 (11.67) 10 (16.67) 4 (10.81) 6 (26.09) Stage AJCC version 8, n (%) I 37 (24.67) 28 (31.11) 9 (15.00) 20 (33.33) 13 (36.11) 7 (30.43) II 55 (36.67) 31 (34.44) 24 (40.00) 14 (23.33) 8 (21.62) 6 (26.09) III 43 (28.67) 25 (27.78) 18 (30.00) 19 (31.67) 11 (30.56) 8 (34.78) IV 15 (10.00) 6 (6.67) 9 (15.00) 7 (11.67) 5 (13.89) 2 (8.70) Differentiation grade, n (%) Poor 100 (66.67) 57 (63.33) 43 (71.67) 37 (61.67) 20 (54.05) 17 (73.91) Moderate 38 (25.33) 29 (32.22) 9 (15.00) 20 (33.3) 15 (40.54) 5 (21.74) Well 12 (8.00) 4 (4.44) 8 (13.33) 3 (5.00) 2 (5.41) 1 (4.35) Operation method, n (%) Radical total gastrectomy 61 (40.67) 32 (35.56) 29 (48.33) 24 (40.00) 14 (37.84) 10 (43.48) Distal gastrectomy 83 (55.33) 54 (60.00) 29 (48.33) 33 (55.00) 23 (62.16) 10 (43.48) Proximal gastrectomy 6 (4.00) 4 (4.44) 2 (3.33) 3 (5.00) 0 (0) 3 (13.04) Curative chemotherapy, yes, n (%) 117 (79.33) 73 (81.11) 44 (75.86) 50 (83.33) 30 (81.08) 20 (86.96) Nutrition-related information BMI, kg/m2, mean ± SD 21.73 ± 3.25 22.88 ± 3.17 20.02 ± 2.57 21.55 ± 2.62 22.50 ± 2.39 22.50 ± 2.39 NRS2002 score, ≥ 3, n (%) 60 (40.00) 0 (0) 60 (100) 23 (38.33) 0 (0) 23 (100) PNS, yes, n (%) 139 (92.67) 83 (92.22) 56 (93.33) 59 (98.33) 36 (97.30) 23 (100) ENS, yes, n (%) 48 (32.00) 25 (27.78) 23 (38.33) 14 (23.33) 8 (21.62) 6 (26.09) GLIM severity grading, n (%) Normal 90 (60.00) 90 (100) 0 (0) 37 (61.67) 37 (100) 0 (0) Moderate malnutrition 35 (23.33) 0 (0) 35 (58.33) 13 (21.67) 0 (0) 13 (56.52) Severe malnutrition 25 (16.67) 0 (0) 25 (41.67) 10 (16.67) 0 (0) 10 (43.48) Laboratory findings ALB, g/dL, mean ± SD 3.86 ± 0.46 4.00 ± 0.43 3.66 ± 0.42 4.00 ± 0.43 3.66 ± 0.42 3.66 ± 0.42 CRP, median (IQR) 2.44 (0.88,10.25) 2.30 (0.71,2.44) 8.95 (2.52,20.90) 1.69 (0.71,6.21) 1.40 (0.71,2.55) 1.78 (0.64,9.98) NLR, mean ± SD 2.27 ± 0.70 2.19 ± 0.63 2.38 ± 0.80 3.02 ± 2.16 3.04 ± 2.28 3.04 ± 2.28 GLIM, the global leadership initiative on malnutrition; SD, standard deviation; LOS, length of stay; GC, gastric cancer; SRCC, Signet-ring cell carcinoma; NRS2002, the nutritional risk screening 2002; PNS, parenteral nutritional support; ENS, enteral nutritional support; ALB, albumin; CRP, C-reactive protein; NLR, neutrophil-lymphocyte ratio. Among the five criteria, unintentional weight loss (HR = 14.13, 95% CI: 1.70 to 117.39 for moderate malnutrition and HR = 12.50, 95% CI: 1.40 to 111.89 for severe malnutrition) was found to be the main factor affecting mortality when the weights of each GLIM criterion were calculated (Table 4 ). Besides, the inclusion of CRP as an inflammatory marker in GLIM criteria improved the sensitivity and accuracy of the survival prediction model (Model 1 without CRP, AUC 0.779, 95% CI: 0.563 to 0.849, sensitivity: 0.875, specificity: 0.627; Model 2 inclusion of CRP, AUC 0.896, 95% CI: 0.645 to 0.963, sensitivity: 0.810, specificity: 0.842), as shown in Fig. 2 . Table 4 Associations between GLIM components and the 5-year OS in GC patients GLIM criteria HR 95% CI P value Unintentional weight loss Normal Reference Moderate malnutrition 14.13 1.70-117.39 0.014 Severe malnutrition 12.50 1.40-111.89 0.024 Low BMI Normal Reference Moderate malnutrition 3.37 0.76–15.05 0.111 Severe malnutrition 5.64 1.26–25.22 0.024 Reduce muscle Normal Reference Malnutrition 5.35 1.63–17.54 0.006 Reduce intake Normal Reference Malnutrition 3.75 1.00-14.13 0.050 CRP index CRP < 10 mg/L Reference CRP ≥ 10 mg/L 6.93 1.68–28.55 0.007 HR, hazard ratio; CI, confidence interval; GLIM, the global leadership initiative on malnutrition; BMI, body mass index; CRP, C-reactive protein. Adjusting for age, albumin and hemoglobin. A GLIM-based nomogram was established using the five indicators of GLIM. Each subtype within these variables was assigned a score on the point scale. The nomogram was developed as shown in Fig. 3 . The AUC of model for predicting 5-year mortality was 0.896 (95% CI: 0.645 to 0.963). The C-index for OS prediction was 0.804 (95% CI: 0.645 to 0.963), and the calibration curve fitted well. In a validation cohort, the C-index for OS prediction was 0.756 (95% CI: 0.613 to 0.896), and the 5-year AUC was 0.819 (95% CI: 0.736 to 0.902). Additionally, DCA showed that the GLIM-based nomogram had clinical application value. Discussion It is well-recognized that malnutrition has an independently impact on mortality [ 6 , 8 ], particularly in cancer patients [ 20 ]. Completing nutritional assessment before starting cancer treatment is imperative. In this study, we found that the GLIM criteria could effectively evaluate nutritional status. The prevalence of malnutrition demonstrated in this study was 40%, which was similar to an earlier published study [ 20 ]. Besides, our results suggested that malnutrition diagnosed by GLIM was an independent risk factor for OS. Among the phenotypic criteria of GLIM consensus, unintentional weight loss was a key phenotypic characteristic that must be taken into account in the evaluation of cancer patients' nutritional status [ 20 ]. It has been shown that cancer patients who lose weight will face more and heavier adverse reaction of chemotherapy, often resulting in shorter OS and poorer quality of life [ 21 ]. In actuality, weight loss was the first obvious or observable indication in cancer patients. A study has shown that approximately 40% of cancer patients reported weight loss of more than 10% at the time of their first diagnosis [ 20 ]. In our cohort, 44.67% of the study population satisfied the standards of unintentional weight loss. In addition, during a five-year follow-up, unintentional weight loss was the main factor contributing to mortality in the study population. The results obtained confirmed previous observations that unintentional weight loss was a reliable and independent predictor of OS in cancer patients. Muscle mass loss was also a direct manifestation of malnutrition. Previous studies have shown that approximately 20–70% of cancer patients suffer from muscle mass loss [ 4 , 5 , 20 ], which is related to metabolic abnormalities in patients with cancer. Catabolic proinflammatory cytokines induced by tumor cells could induce catabolism of fat and muscle while inhibiting anabolism [ 22 ], which can reduce the tolerance and effectiveness of antitumor therapy and further affect clinical outcomes. Anthropometric and physical examinations, such as CC and mid-arm muscle circumference, were routinely performed during nutritional screening and assessment. Thus, CC was used as an indicator of muscle mass loss in this study. According to the newly published consensus on the diagnosis and treatment of sarcopenia in Asia, the cut-off values of CC were set to < 33 cm in men and < 32 cm in women [ 18 , 19 ]. Additionally, an earlier study confirmed that BMI was an independent predictor of OS in cancer patients [ 21 ]. However, a low BMI has a restricted ability to assess nutritional status [ 21 ]. In the current study, a low BMI also showed a relatively weak effect on survival among the five criteria of GLIM consensus. Besides, only 15.33% of the patients in this study were under the BMI threshold. Cancer patients were frequently found to be overweight or obese, with some even having fluid retention that can mask weight reduction and cause an unnaturally high BMI. Numerous studies have revealed the potential value of inflammatory factors (such as CRP, lymphocyte, and neutrophil, etc.) in assessing tumor prognosis [ 23 ]. However, none of the previous studies discussed the diagnostic and predictive value of inflammatory indicators incorporated into the GLIM-based model. Hence, more information about inflammatory indicators is needed. As a widely recognized representative of the systemic inflammatory response, CRP is also associated with the progression and prognosis of cancer [ 23 ]. An elevated CRP value indicates that the body is in a severe state of inflammation and stress, while the metabolic status of the body changes, and the resting energy expenditure increases, which could aggravate the malnutrition of cancer patients [ 23 ]. Numerous previous studies also found that patients with high CRP value were generally associated with lower levels of albumin, albumin, total protein, hemoglobin, and lymphocyte [ 23 ]. Therefore, despite all included patients automatically satisfied the standard of etiology, CRP was still applied as an inflammatory marker in our prognostic model. The modified Glasgow Prognostic Score (mGPS) has been widely used in the assessment of systemic inflammation in the body, and it is relatively simple, objective, and easy to be implemented in clinical practice [ 24 ]. Thus, the cut-off values of the inflammatory markers applied in our model were referenced to the mGPS. Our result revealed that CRP ≥ 10 mg/L had an impact on survival (HR = 6.93, 95% CI: 1.68 to 28.55). Furthermore, we found that CRP improved the sensitivity and accuracy of the survival prediction model. In comparison with an earlier published study, our model had a higher sensitivity and specificity [ 19 ]. Another aspect of the GLIM etiologic criteria may be the evaluation and quantification of various symptoms representing obstacles to dietary intake. In newly diagnosed cancer patients, anorexia occurs in roughly 50% of cases [ 25 ], which may be associated with the release of specific active factors, such as tumor necrosis factor-alpha, interleukin-1, interleukin-6, and 5-hydroxytryptamine, induced by tumor cells [ 25 ]. Symptoms such as anorexia and early satiety may also result from disruption of neuroendocrine pathways between neuropeptides and other neurotransmitters in the central nervous system [ 21 , 25 ]. In addition, gastrointestinal obstruction caused by gastrointestinal cancers can result in abdominal distention and poor appetite. In this study, 42.67% of subjects had dietary intake symptoms, and malnourished patients showed more symptoms of dietary intake than non-malnourished patients. Reduced appetite can cause inadequate nutrient intake, which in turn can lead to malnutrition and cachexia. Cachexia has been proven to be prevalent in cancer patients, especially in upper gastrointestinal and pancreatic cancers [ 25 ]. The primary limitation of this research is the limited sample size. The assessment of muscle mass loss was assessed via anthropometric data in this study, and there was no body composition analysis data available. Therefore, our team has initiated an observational, multi-center, and hospital-based prospective cohort study on GLIM. Conclusions In summary, the inclusion of CRP as an inflammatory marker in GLIM criteria could improve the sensitivity and accuracy of the survival prediction model. Our study also confirmed that GLIM-diagnosed malnutrition was an independent risk factor for predicting mortality and a major negative factor for clinical prognosis in GC patients undergoing surgical resection. The quantitative scoring system for GC was helpful for accurate nutrition diagnosis and can be applied in individualized clinical nutritional therapy in the perioperative period. Abbreviations GC: gastric cancer; ESPEN: European Society for Parenteral and Enteral Nutrition; ASPEN: American Society for Parenteral Enteral Nutrition; CRP: C-reactive protein; OS: overall survival; EMR: Electronic Medical Record; HR: hazard ratio; AUC: area under receiver operating characteristic curve; DCA: decision curve analysis Declarations Ethics approval and consent to participate The study was approved by the Medical Ethics Committee of Tongde Hospital of Zhejiang Province (No.2022-147-JY). The need for written informed consent was waived by the Institutional Review Board of Tongde Hospital of Zhejiang Province due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research supported by Medical Science and Technology Project of Zhejiang Province (Grant No.2023KY360), Chinese Medicine Research Program of Zhejiang Province (Grant No.2024ZL 349) and Research Fund Project of Zhejiang Nutrition Society (Grant No.ZN-YCHP-2023-005). Authors' contributions Xi Luo designed the study and wrote the manuscript. Bin Cai and Weiwei Jin collected, analyzed, and interpreted the data. Xi Luo and Bin Cai critically reviewed, edited, and approved the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Petryszyn P, Chapelle N, Matysiak-Budnik T. Gastric Cancer: Where Are We Heading? Digestive diseases (Basel, Switzerland). 2020; 38: 280-5. Arnold M, Park JY, Camargo MC, Lunet N, Forman D, Soerjomataram I. Is gastric cancer becoming a rare disease? A global assessment of predicted incidence trends to 2035. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4348710","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299778048,"identity":"4a7385f2-5991-4026-8394-286213d934f1","order_by":0,"name":"Xi Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIie3QMQqDMBSA4YjwXGLdyhMHrxB3DxMRnBw6OhTaKd2cWwo9SyCgizdoB6VgD1AonaS2YylEtw75hkz5eXkhxDD+0OJ9cDZshLMlclICn6SQVknlnIQ00john/gwwLTCTlxs8G+dWq1J6C01wwCzlCWiBwhypvYViQ5HzTjAPGoTYdMxIYrC+BVnfcLkmCD4davoMC2JWt4oBkiYcsWUhPYp40XGgY67uCXqd/GctPKfLObhrr7e6SMOvUCTfMN51w3DMIzfXhGXPkiBpAj3AAAAAElFTkSuQmCC","orcid":"","institution":"Tongde Hospital of Zhejiang Province","correspondingAuthor":true,"prefix":"","firstName":"Xi","middleName":"","lastName":"Luo","suffix":""},{"id":299778050,"identity":"20be71d0-49da-498b-8ecf-424ab62fda09","order_by":1,"name":"Bin Cai","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Cai","suffix":""},{"id":299778052,"identity":"df2873eb-e01d-45f6-bf36-77f6310aae6f","order_by":2,"name":"Weiwei Jin","email":"","orcid":"","institution":"Tongde Hospital of Zhejiang Province","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-04-30 11:29:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4348710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4348710/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56198069,"identity":"97e2a6d2-0746-4daa-ab4d-6faf4fe4850c","added_by":"auto","created_at":"2024-05-09 18:27:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curve stratified by the GLIM criteria (A). Kaplan-Meier curve stratified by the GLIM severity grade (B). adjusted HR: adjusted by age, gender, chemotherapy, cTNM, differentiation grade, CRP, NLR. CRP: C-reactive protein; NLR: neutrophil-lymphocyte ratio.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4348710/v1/4d19ad9dcee5259fc78b232b.jpg"},{"id":56198068,"identity":"edfcc663-5f11-4bcb-b8a2-0c62a290dae4","added_by":"auto","created_at":"2024-05-09 18:27:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArea under the ROC curves (AUC) for predicting the overall survival at 5 years.\u003c/strong\u003eModel 1 including unintentional weight loss, low BMI, reduced muscle mass and reduced intake. Model 2 add CRP index. ROC: receiver operator characteristic. AUC: area under curve; BMI, body mass index; CRP: C-reactive protein.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4348710/v1/bdfcb12d12a2d83c04bb3545.jpg"},{"id":56198070,"identity":"3c4a47ba-4bab-4f42-8f90-398d78bbbd17","added_by":"auto","created_at":"2024-05-09 18:27:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":363662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe nomogram used to quantize the GLIM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The nomogram used to quantize the GLIM. First, locate each GLIM criteria site on the axis, then draw a line straight upward to the Points axis to determine how many points the patient receives for the variable. Add the points for each of these predicators together and locate the sum on the total points axis to get the scored GLIM. (B) Calibration curves for the nomogram in the development cohort (B1) and validation cohort (B2). The x-axis represents the nomogram-predicted probability and y-axis represents the actual probability of the 5 years overall survival. Perfect prediction would correspond to 45°red line. (C) ROC curve analysis for predicting the overall survival in the development cohort (C1) and validation cohort (C2). (D) Decision curve analysis on the GLIM based system (blue line). The red line denotes the assumption that all patients have outcome event (death) during follow-up. Green line represents the assumption that no patients have outcome event (death) during follow-up. ROC: receiver operator characteristic. AUC: area under curve.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4348710/v1/64c5227e9eeeea33783f71f5.jpg"},{"id":56338643,"identity":"91b35b07-ad18-4546-bdc1-dea9ee758c84","added_by":"auto","created_at":"2024-05-12 19:46:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1295914,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4348710/v1/e7f0a0c5-e188-4092-b883-0a85893c9f40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A modified GLIM criteria-based nomogram for the survival prediction of Gastric Cancer Patients undergoing Surgical Resection","fulltext":[{"header":"Background","content":"\u003cp\u003eAs a common digestive tract tumor, gastric cancer (GC) has a high incidence in East Asia, especially in China, Japan, and South Korea [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the latest data in 2020, the incidence and mortality of GC in China ranked third among all malignancies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To date, surgical resection remains the main treatment option for GC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the stress of surgery can rapidly deplete the body\u0026rsquo;s nutrient reserves, thereby affecting the body's functional recovery and wound healing. Conditions such as neoadjuvant therapy implemented postoperatively may also contribute to the impairment of nutrient reserves, further affecting the patient\u0026rsquo;s recovery. In addition, it is well known that malnutrition is a common problem among patients with cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The prevalence of malnutrition in patients with GC is the highest among all malignancies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] due to factors such as digestive tract obstruction, delayed gastric emptying, impaired digestion and absorption, which directly affect nutrient uptake, digestion, and absorption [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly diagnosis and timely intervention of malnutrition can significantly reduce medical costs, shorten hospital stays, improve treatment outcomes, and prolong patient survival [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, there have been no internationally recognized standards in the diagnosis of malnutrition. To identify malnutrition, different studies have used different assessment tools. Research in this field is significantly behind other disease areas [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, the European Society for Parenteral and Enteral Nutrition (ESPEN) and the American Society for Parenteral Enteral Nutrition (ASPEN) published the GLIM consensus on the diagnosis definition of malnutrition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Since publication, validation has been reported in patients with head and neck [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], gastrointestinal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and pulmonary [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] tumors. However, none of these studies explored the diagnostic and predictive value of inflammatory indicators. More information on the choice of indicators for the evaluation of inflammation in etiological criteria is still pending. Besides, the GLIM criteria for identifying malnutrition require patients to meet at least one etiologic criterion and one phenotypic criterion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The six different diagnostic combinations formed by the two etiologic and three phenotypic criteria of the GLIM consensus may lead to differences in the prediction of clinical outcome and survival. Accurate nutritional diagnosis is the prerequisite for rational nutritional therapy. Thus, a comprehensive model built based on these variables is required for patient counseling and survival prediction.\u003c/p\u003e \u003cp\u003eIn this study, the diagnostic value of C-reactive protein (CRP), mentioned several times in the GLIM consensus, was evaluated. Furthermore, a GLIM-based nomogram was established using the five indicators of GLIM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis single-center, observational, and hospital-based retrospective cohort study aimed to evaluate the prevalence of malnutrition in GC patients undergoing surgical resection as the first-line treatment and the correlation of malnutrition diagnosed by the GLIM criteria with overall survival (OS). All participants who met the following inclusion and exclusion criteria were consecutively enrolled between January 2015 and December 2019. Inclusion criteria: (1) aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years old; (2) hospitalized patients properly diagnosed with GC and undergoing elective surgery; (3) never received surgery, radiotherapy, chemotherapy, and other anti-tumor treatments (including immunotherapy); (4) willing to participate in this study. Exclusion criteria: (1) aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years old; (2) with admission time no more than 48 hours; (3) suffered from severe heart, liver, and brain dysfunction; (4) suffered from active systemic infection; (5) hospitalized patients properly diagnosed with GC and undergoing palliative surgery; (6) refused to participate in this study.\u003c/p\u003e \u003cp\u003eData was obtained from the Electronic Medical Record (EMR) System and retrospectively analyzed. Within the first 48 hours after admission, general information, anthropometric data, laboratory results, existing comorbidities, nutrition-related data, and medical history of all patients were gathered and documented by doctors, nurses, and clinical dietitians. The oncotherapy-related data and follow-up information were also recorded in the EMR system. The eighth edition of the AJCC TNM staging system was used to determine all pathological staging.\u003c/p\u003e \u003cp\u003eThe primary outcome of this study was mortality in the 5-year follow-up survival cohort. All participants were monitored from the initial admission to death or until the end of November 2023. The primary outcome of this study was mortality in the 5-year follow-up survival cohort. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving patients were approved by the ethics committee of Tongde Hospital of Zhejiang Province (No.2022-147-JY). Written informed consent was obtained from all patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMalnutrition diagnosed by GLIM criteria\u003c/h2\u003e \u003cp\u003eThe GLIM criteria for the diagnosis of malnutrition require that individuals meet at least one etiologic criterion and one phenotypic criterion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The GLIM criteria are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All data were available from medical records and nutritional assessment records. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a list of the GLIM indicators for diagnosing and assessing the severity of nutritional status [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhenotypic and etiologic criteria for the diagnosis of malnutrition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePhenotypic criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eEtiologic criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow BMI\u003c/p\u003e \u003cp\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduced muscle mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduced food intake\u003c/p\u003e \u003cp\u003eor assimilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDisease burden/\u003c/p\u003e \u003cp\u003einflammation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5% within the past 6 months,\u003c/p\u003e \u003cp\u003eor \u0026gt;\u0026thinsp;10% beyond 6 months\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5 if\u0026thinsp;\u0026lt;\u0026thinsp;70 years old,\u003c/p\u003e \u003cp\u003eor \u0026lt;\u0026thinsp;20 if\u0026thinsp;\u0026ge;\u0026thinsp;70 years old\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalf circumference\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;33 cm in men\u003c/p\u003e \u003cp\u003eor \u0026lt;\u0026thinsp;32 cm in women\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50% of ER\u0026thinsp;\u0026gt;\u0026thinsp;1 week,\u003c/p\u003e \u003cp\u003eor any reduction for \u0026gt;\u0026thinsp;2 weeks,\u003c/p\u003e\u003cp\u003eor any chronic GI condition that adversely impacts food assimilation or absorption\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAcute disease/ injury or chronic disease-related inflammation (CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGLIM, the global leadership initiative on malnutrition; BMI, body mass index; GI, gastrointestinal; ER, energy requirements; CRP, C-reactive protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters and thresholds used for GLIM severity grading in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePhenotypic criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight loss (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReduced muscle mass\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I/Moderate malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10% within the past 6 months,\u003c/p\u003e \u003cp\u003eor 10\u0026ndash;20% beyond 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20 if\u0026thinsp;\u0026lt;\u0026thinsp;70 years old,\u003c/p\u003e \u003cp\u003eor \u0026lt;\u0026thinsp;22 if\u0026thinsp;\u0026ge;\u0026thinsp;70 years old\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalf circumference\u0026thinsp;\u0026lt;\u0026thinsp;33 cm in men or \u0026lt;\u0026thinsp;32 cm in women\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II/Severe malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10% within the past 6 months,\u003c/p\u003e \u003cp\u003eor \u0026gt;\u0026thinsp;20% beyond 6 months\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5 if\u0026thinsp;\u0026lt;\u0026thinsp;70 years old,\u003c/p\u003e \u003cp\u003eor \u0026lt;\u0026thinsp;20 if\u0026thinsp;\u0026ge;\u0026thinsp;70 years old\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot applicable, no Asian standards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGLIM, the global leadership initiative on malnutrition; BMI, body mass index; CRP, C-reactive protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of a nomogram model\u003c/h2\u003e \u003cp\u003eThe five indicators including two etiologic indexes and three phenotypic indexes were applied to establish a prediction model. The relationship between the GLIM criteria and OS was verified using a multivariate Cox regression analysis, then a nomogram based on each GLIM criterion's hazard ratio (HR) was created. Harrell's concordance index (C-index) was calculated by the bootstrap approach with 500 resamples to evaluate the discrimination of the nomogram. The predictive accuracy for the 5-year OS was examined using the area under receiver operating characteristic curve (AUC). Through a comparison of observed and predicted survival, the nomogram for 5-year OS was calibrated. Finally, decision curve analysis (DCA) was used to evaluate the clinical utility of the GLIM-based nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were analyzed with the statistical package R (The R Foundation; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; version 3.6.3). Quantitative data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Differences between the two groups were analyzed by the Students' t-test. Non-parametric tests (Mann Whitney or Kruskall Wallis) were utilized for variables that did not follow a normal distribution. A chi-square test was used to compare qualitative variables, with Fisher adjustment if necessary. Besides, OS data were analyzed using the Cox regression and Kaplan- Meier curve. To account for potential confounders, a multivariate Cox regression analysis was also carried out utilizing backward selection. Finally, a GLIM-based nomogram that was adjusted for potential confounders was built. The threshold of statistical significance was set at \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of the patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The nutritional status of the patients was retrospectively assessed using GLIM criteria. The incidence of malnutrition in GC patients was 40%, of which 58.33% were moderate malnutrition and 41.67% were severe malnutrition. Kaplan-Meier curve was used to analyze the relationship between GLIM and OS. The 5-year survival rate was 97.78% (88/90) in the normally nourished group and 85.00% (51/60) in the malnutrition group. Compared to patients in the normally nourished group, patients in the malnourished group had a poorer OS rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Additionally, the degree of malnutrition status was linked to OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Cox model validation showed that nutritional status was strongly related to an increased risk of mortality according to GLIM criteria. Severe malnutrition continued to be an independent predictive factor even after adjustment for confounding variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of GC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDevelopment cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalnutrition\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMalnutrition\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.51\u0026thinsp;\u0026plusmn;\u0026thinsp;10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.23\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.74\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (68.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (72.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (63.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (73.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26 (70.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 (78.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, above high school, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (21.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (24.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (35.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (30.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS, days, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (19,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (19,24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (19,31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (19,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (16,24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27 (21,32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (31.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (18.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (21.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (8.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (28.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (27.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (26.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of GC, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (4.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic disease history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (11.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (14.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10 (27.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (8.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (36.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (28.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (29.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (26.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (17.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (13.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent disease and treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (82.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (81.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (75.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (86.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRCC, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (15.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (18.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (8.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (4.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelicobacter Pylori, positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (26.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage AJCC version 8, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (24.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (31.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (36.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 (30.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (36.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (34.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (21.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (26.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (28.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (31.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (30.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (34.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 (13.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (8.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation grade, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (63.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (71.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (61.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20 (54.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17 (73.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (25.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (32.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (40.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (21.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (4.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation method, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadical total gastrectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (40.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (35.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (48.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (37.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (43.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistal gastrectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (55.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (48.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (55.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (62.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (43.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximal gastrectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (13.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurative chemotherapy, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (79.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (81.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (75.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (83.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (81.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (86.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutrition-related information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRS2002 score, \u0026ge;\u0026thinsp;3, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (38.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNS, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (92.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (92.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (93.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (98.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (97.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENS, yes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (32.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (38.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (21.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 (26.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLIM severity grading, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (61.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (58.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (21.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (56.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (41.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (43.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44 (0.88,10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30 (0.71,2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.95 (2.52,20.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69 (0.71,6.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.40 (0.71,2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.78 (0.64,9.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eGLIM, the global leadership initiative on malnutrition; SD, standard deviation; LOS, length of stay; GC, gastric cancer; SRCC, Signet-ring cell carcinoma; NRS2002, the nutritional risk screening 2002; PNS, parenteral nutritional support; ENS, enteral nutritional support; ALB, albumin; CRP, C-reactive protein; NLR, neutrophil-lymphocyte ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the five criteria, unintentional weight loss (HR\u0026thinsp;=\u0026thinsp;14.13, 95% CI: 1.70 to 117.39 for moderate malnutrition and HR\u0026thinsp;=\u0026thinsp;12.50, 95% CI: 1.40 to 111.89 for severe malnutrition) was found to be the main factor affecting mortality when the weights of each GLIM criterion were calculated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Besides, the inclusion of CRP as an inflammatory marker in GLIM criteria improved the sensitivity and accuracy of the survival prediction model (Model 1 without CRP, AUC 0.779, 95% CI: 0.563 to 0.849, sensitivity: 0.875, specificity: 0.627; Model 2 inclusion of CRP, AUC 0.896, 95% CI: 0.645 to 0.963, sensitivity: 0.810, specificity: 0.842), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between GLIM components and the 5-year OS in GC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLIM criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnintentional weight loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70-117.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40-111.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026ndash;25.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduce muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u0026ndash;17.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduce intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00-14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026lt;\u0026thinsp;10 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;10 mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u0026ndash;28.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHR, hazard ratio; CI, confidence interval; GLIM, the global leadership initiative on malnutrition; BMI, body mass index; CRP, C-reactive protein.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAdjusting for age, albumin and hemoglobin.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA GLIM-based nomogram was established using the five indicators of GLIM. Each subtype within these variables was assigned a score on the point scale. The nomogram was developed as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The AUC of model for predicting 5-year mortality was 0.896 (95% CI: 0.645 to 0.963). The C-index for OS prediction was 0.804 (95% CI: 0.645 to 0.963), and the calibration curve fitted well. In a validation cohort, the C-index for OS prediction was 0.756 (95% CI: 0.613 to 0.896), and the 5-year AUC was 0.819 (95% CI: 0.736 to 0.902). Additionally, DCA showed that the GLIM-based nomogram had clinical application value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is well-recognized that malnutrition has an independently impact on mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], particularly in cancer patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Completing nutritional assessment before starting cancer treatment is imperative. In this study, we found that the GLIM criteria could effectively evaluate nutritional status. The prevalence of malnutrition demonstrated in this study was 40%, which was similar to an earlier published study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Besides, our results suggested that malnutrition diagnosed by GLIM was an independent risk factor for OS.\u003c/p\u003e \u003cp\u003eAmong the phenotypic criteria of GLIM consensus, unintentional weight loss was a key phenotypic characteristic that must be taken into account in the evaluation of cancer patients' nutritional status [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It has been shown that cancer patients who lose weight will face more and heavier adverse reaction of chemotherapy, often resulting in shorter OS and poorer quality of life [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In actuality, weight loss was the first obvious or observable indication in cancer patients. A study has shown that approximately 40% of cancer patients reported weight loss of more than 10% at the time of their first diagnosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our cohort, 44.67% of the study population satisfied the standards of unintentional weight loss. In addition, during a five-year follow-up, unintentional weight loss was the main factor contributing to mortality in the study population. The results obtained confirmed previous observations that unintentional weight loss was a reliable and independent predictor of OS in cancer patients. Muscle mass loss was also a direct manifestation of malnutrition. Previous studies have shown that approximately 20\u0026ndash;70% of cancer patients suffer from muscle mass loss [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which is related to metabolic abnormalities in patients with cancer. Catabolic proinflammatory cytokines induced by tumor cells could induce catabolism of fat and muscle while inhibiting anabolism [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which can reduce the tolerance and effectiveness of antitumor therapy and further affect clinical outcomes. Anthropometric and physical examinations, such as CC and mid-arm muscle circumference, were routinely performed during nutritional screening and assessment. Thus, CC was used as an indicator of muscle mass loss in this study. According to the newly published consensus on the diagnosis and treatment of sarcopenia in Asia, the cut-off values of CC were set to \u0026lt;\u0026thinsp;33 cm in men and \u0026lt;\u0026thinsp;32 cm in women [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, an earlier study confirmed that BMI was an independent predictor of OS in cancer patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, a low BMI has a restricted ability to assess nutritional status [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the current study, a low BMI also showed a relatively weak effect on survival among the five criteria of GLIM consensus. Besides, only 15.33% of the patients in this study were under the BMI threshold. Cancer patients were frequently found to be overweight or obese, with some even having fluid retention that can mask weight reduction and cause an unnaturally high BMI.\u003c/p\u003e \u003cp\u003eNumerous studies have revealed the potential value of inflammatory factors (such as CRP, lymphocyte, and neutrophil, etc.) in assessing tumor prognosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, none of the previous studies discussed the diagnostic and predictive value of inflammatory indicators incorporated into the GLIM-based model. Hence, more information about inflammatory indicators is needed. As a widely recognized representative of the systemic inflammatory response, CRP is also associated with the progression and prognosis of cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. An elevated CRP value indicates that the body is in a severe state of inflammation and stress, while the metabolic status of the body changes, and the resting energy expenditure increases, which could aggravate the malnutrition of cancer patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Numerous previous studies also found that patients with high CRP value were generally associated with lower levels of albumin, albumin, total protein, hemoglobin, and lymphocyte [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, despite all included patients automatically satisfied the standard of etiology, CRP was still applied as an inflammatory marker in our prognostic model. The modified Glasgow Prognostic Score (mGPS) has been widely used in the assessment of systemic inflammation in the body, and it is relatively simple, objective, and easy to be implemented in clinical practice [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, the cut-off values of the inflammatory markers applied in our model were referenced to the mGPS. Our result revealed that CRP\u0026thinsp;\u0026ge;\u0026thinsp;10 mg/L had an impact on survival (HR\u0026thinsp;=\u0026thinsp;6.93, 95% CI: 1.68 to 28.55). Furthermore, we found that CRP improved the sensitivity and accuracy of the survival prediction model. In comparison with an earlier published study, our model had a higher sensitivity and specificity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another aspect of the GLIM etiologic criteria may be the evaluation and quantification of various symptoms representing obstacles to dietary intake. In newly diagnosed cancer patients, anorexia occurs in roughly 50% of cases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which may be associated with the release of specific active factors, such as tumor necrosis factor-alpha, interleukin-1, interleukin-6, and 5-hydroxytryptamine, induced by tumor cells [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Symptoms such as anorexia and early satiety may also result from disruption of neuroendocrine pathways between neuropeptides and other neurotransmitters in the central nervous system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, gastrointestinal obstruction caused by gastrointestinal cancers can result in abdominal distention and poor appetite. In this study, 42.67% of subjects had dietary intake symptoms, and malnourished patients showed more symptoms of dietary intake than non-malnourished patients. Reduced appetite can cause inadequate nutrient intake, which in turn can lead to malnutrition and cachexia. Cachexia has been proven to be prevalent in cancer patients, especially in upper gastrointestinal and pancreatic cancers [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary limitation of this research is the limited sample size. The assessment of muscle mass loss was assessed via anthropometric data in this study, and there was no body composition analysis data available. Therefore, our team has initiated an observational, multi-center, and hospital-based prospective cohort study on GLIM.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the inclusion of CRP as an inflammatory marker in GLIM criteria could improve the sensitivity and accuracy of the survival prediction model. Our study also confirmed that GLIM-diagnosed malnutrition was an independent risk factor for predicting mortality and a major negative factor for clinical prognosis in GC patients undergoing surgical resection. The quantitative scoring system for GC was helpful for accurate nutrition diagnosis and can be applied in individualized clinical nutritional therapy in the perioperative period.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGC: gastric cancer; ESPEN: European Society for Parenteral and Enteral Nutrition; ASPEN: American Society for Parenteral Enteral Nutrition; CRP: C-reactive protein; OS: overall survival; EMR: Electronic Medical Record; HR: hazard ratio; AUC: area under receiver operating characteristic curve; DCA: decision curve analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Ethics Committee of Tongde Hospital of Zhejiang Province (No.2022-147-JY). The need for written informed consent was waived by the Institutional Review Board of Tongde Hospital of Zhejiang Province due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research supported by Medical Science and Technology Project of Zhejiang Province (Grant No.2023KY360), Chinese Medicine Research Program of Zhejiang Province (Grant No.2024ZL 349) and Research Fund Project of Zhejiang Nutrition Society (Grant No.ZN-YCHP-2023-005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXi Luo designed the study and wrote the manuscript. Bin Cai and Weiwei Jin collected, analyzed, and interpreted the data. Xi Luo\u0026nbsp;and Bin Cai\u0026nbsp;critically reviewed, edited, and approved the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePetryszyn P, Chapelle N, Matysiak-Budnik T. Gastric Cancer: Where Are We Heading? Digestive diseases (Basel, Switzerland). 2020; 38: 280-5.\u003c/li\u003e\n\u003cli\u003eArnold M, Park JY, Camargo MC, Lunet N, Forman D, Soerjomataram I. Is gastric cancer becoming a rare disease? A global assessment of predicted incidence trends to 2035. Gut. 2020; 69: 823-9.\u003c/li\u003e\n\u003cli\u003eHealth Commission Of The People\u0026apos;s Republic Of China N. National guidelines for diagnosis and treatment of gastric cancer 2022 in China (English version). 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Nutrition journal. 2019; 18: 33.\u003c/li\u003e\n\u003cli\u003eLeiva Badosa E, Badia Tahull M, Virgili Casas N, Elguezabal Sangrador G, Faz M\u0026eacute;ndez C, Herrero Meseguer I, et al. Hospital malnutrition screening at admission: malnutrition increases mortality and length of stay. Nutricion hospitalaria. 2017; 34: 907-13.\u003c/li\u003e\n\u003cli\u003eCurtis LJ, Bernier P, Jeejeebhoy K, Allard J, Duerksen D, Gramlich L, et al. Costs of hospital malnutrition. Clinical nutrition (Edinburgh, Scotland). 2017; 36: 1391-6.\u003c/li\u003e\n\u003cli\u003eCorreia M. Nutrition Screening vs Nutrition Assessment: What\u0026apos;s the Difference? Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition. 2018; 33: 62-72.\u003c/li\u003e\n\u003cli\u003eSchneider SM, Correia M. Epidemiology of weight loss, malnutrition and sarcopenia: A transatlantic view. Nutrition (Burbank, Los Angeles County, Calif). 2020; 69: 110581.\u003c/li\u003e\n\u003cli\u003eJensen GL, Cederholm T, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM Criteria for the Diagnosis of Malnutrition: A Consensus Report From the Global Clinical Nutrition Community. JPEN Journal of parenteral and enteral nutrition. 2019; 43: 32-40.\u003c/li\u003e\n\u003cli\u003eCederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clinical nutrition (Edinburgh, Scotland). 2019; 38: 1-9.\u003c/li\u003e\n\u003cli\u003eMulasi U, Vock DM, Kuchnia AJ, Jha G, Fujioka N, Rudrapatna V, et al. Malnutrition Identified by the Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition Consensus Criteria and Other Bedside Tools Is Highly Prevalent in a Sample of Individuals Undergoing Treatment for Head and Neck Cancer. JPEN Journal of parenteral and enteral nutrition. 2018; 42: 139-47.\u003c/li\u003e\n\u003cli\u003eHuang DD, Yu DY, Song HN, Wang WB, Luo X, Wu GF, et al. The relationship between the GLIM-defined malnutrition, body composition and functional parameters, and clinical outcomes in elderly patients undergoing radical gastrectomy for gastric cancer. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology. 2021; 47: 2323-31.\u003c/li\u003e\n\u003cli\u003eQin L, Tian Q, Zhu W, Wu B. The Validity of the GLIM Criteria for Malnutrition in Hospitalized Patients with Gastric Cancer. Nutrition and cancer. 2021; 73: 2732-9.\u003c/li\u003e\n\u003cli\u003eYin L, Lin X, Li N, Zhang M, He X, Liu J, et al. Evaluation of the Global Leadership Initiative on Malnutrition Criteria Using Different Muscle Mass Indices for Diagnosing Malnutrition and Predicting Survival in Lung Cancer Patients. JPEN Journal of parenteral and enteral nutrition. 2021; 45: 607-17.\u003c/li\u003e\n\u003cli\u003eKakavas S, Karayiannis D, Bouloubasi Z, Poulia KA, Kompogiorgas S, Konstantinou D, et al. Global Leadership Initiative on Malnutrition Criteria Predict Pulmonary Complications and 90-Day Mortality after Major Abdominal Surgery in Cancer Patients. Nutrients. 2020; 12.\u003c/li\u003e\n\u003cli\u003eBarazzoni R, Jensen GL, Correia M, Gonzalez MC, Higashiguchi T, Shi HP, et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition (GLIM) diagnosis of malnutrition. Clinical nutrition (Edinburgh, Scotland). 2022; 41: 1425-33.\u003c/li\u003e\n\u003cli\u003eChen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Journal of the American Medical Directors Association. 2020; 21: 300-7.e2.\u003c/li\u003e\n\u003cli\u003eZhang Q, Zhang KP, Zhang X, Tang M, Song CH, Cong MH, et al. Scored-GLIM as an effective tool to assess nutrition status and predict survival in patients with cancer. Clinical nutrition (Edinburgh, Scotland). 2021; 40: 4225-33.\u003c/li\u003e\n\u003cli\u003eArends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, et al. ESPEN guidelines on nutrition in cancer patients. Clinical nutrition (Edinburgh, Scotland). 2017; 36: 11-48.\u003c/li\u003e\n\u003cli\u003eNishikawa H, Goto M, Fukunishi S, Asai A, Nishiguchi S, Higuchi K. Cancer Cachexia: Its Mechanism and Clinical Significance. International journal of molecular sciences. 2021; 22.\u003c/li\u003e\n\u003cli\u003eVermeire S, Van Assche G, Rutgeerts P. The role of C-reactive protein as an inflammatory marker in gastrointestinal diseases. Nature clinical practice Gastroenterology \u0026amp; hepatology. 2005; 2: 580-6.\u003c/li\u003e\n\u003cli\u003eSilva GAD, Wiegert EVM, Calixto-Lima L, Oliveira LC. Clinical utility of the modified Glasgow Prognostic Score to classify cachexia in patients with advanced cancer in palliative care. Clinical nutrition (Edinburgh, Scotland). 2020; 39: 1587-92.\u003c/li\u003e\n\u003cli\u003eKim AJ, Hong DS, George GC. Diet-related interventions for cancer-associated cachexia. Journal of cancer research and clinical oncology. 2021; 147: 1443-50.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GLIM, C-reactive protein, malnutrition, gastric cancer, overall survival, prediction","lastPublishedDoi":"10.21203/rs.3.rs-4348710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4348710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThis study aimed to develop a comprehensive model based on five GLIM variables to predict the individual survival and provide more appropriate patient counseling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective cohort study included 210 gastric cancer (GC) patients undergoing radical resection, among whom 150 patients in the development cohort and 60 patients in the external validation cohort. C-reactive protein (CRP) as an inflammatory marker was included in GLIM criteria and a nomogram for predicting 5-year overall survival (OS) in GC patients was established.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eOf the total 210 patients, 16 (7.62%) died within 5 years.\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eCRP improved the sensitivity and accuracy of the survival prediction model (AUC=0.779, 0.563 to 0.849 for the model without CRP; AUC=0.896, 0.645 to 0.963 for the model adding CRP). Besides, a GLIM-based nomogram was established with an AUC of 0.896. The C-index for predicting OS was 0.804 (95% CI: 0.645 to 0.963), and the calibration curve fitted well. Decision curve analysis (DCA) showed the clinical utility of the nomogram based on GLIM.\u003c/p\u003e\n\u003cp\u003eConclusion: The addition of CRP improved the sensitivity and accuracy of the survival prediction model. The 5-year survival probability of GC patients undergoing radical resection can be reliably predicted by the nomogram presented in this study.\u003c/p\u003e","manuscriptTitle":"A modified GLIM criteria-based nomogram for the survival prediction of Gastric Cancer Patients undergoing Surgical Resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 18:26:30","doi":"10.21203/rs.3.rs-4348710/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd298ff0-64bc-41ce-b276-29d6c10cc6f1","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-12T19:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 18:26:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4348710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4348710","identity":"rs-4348710","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}