{"paper_id":"3d8eb0b3-0517-43d3-8e38-2b1d647de9b1","body_text":"Low BMI Demonstrates Satisfactory Specificity for Diagnosing Malnutrition and is Associated with Longer Hospitalization in Patients with Gastrointestinal or Head and Neck Cancer: A Prospective Cohort Study | 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 Low BMI Demonstrates Satisfactory Specificity for Diagnosing Malnutrition and is Associated with Longer Hospitalization in Patients with Gastrointestinal or Head and Neck Cancer: A Prospective Cohort Study Camilla Soares, Giovanna Stefani, Laura Scott, Mariana Crestani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849041/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 Purpose Few studies have evaluated the individual performance of the nutritional indicators body mass index (BMI), calf circumference (CC), and handgrip strength (HGS) for the diagnosis of malnutrition in the cancer setting. We aimed to evaluate the ability of these nutritional indicators to accurately diagnose malnutrition and their association with hospital length of stay (LOS) in patients with cancer. Methods This cohort study prospectively evaluated 171 patients with gastrointestinal or head and neck cancer. Nutritional status was assessed within 48 hours of hospital admission using BMI, CC, and HGS as well as 2 reference standards: Subjective Global Assessment (SGA) and Patient-Generated SGA (PG-SGA). The accuracy of each nutritional indicator was measured by the area under the receiver operating characteristic curve (AUC), compared with the reference standards. Multiple logistic regression analysis, adjusted for confounders, was used to determine whether malnutrition was associated with LOS. Results Of 171 patients, 59.1% had low CC, 46.2% had low HGS, and 13.5% had low BMI. The SGA and PG-SGA scores indicated malnutrition in 57.3% and 87.1% of patients, respectively. All nutritional indicators had poor accuracy in diagnosing malnutrition (AUC < 0.70). However, compared with SGA and PG-SGA, low BMI had satisfactory specificity (> 80%) and was associated with 1.79 times higher odds of LOS ≥ 6 days. Malnutrition diagnosed by SGA and PG-SGA increased the odds of LOS ≥ 6 days by 3.60-fold and 2.78-fold, respectively. Conclusion Low BMI showed adequate specificity for diagnosing malnutrition and was associated with longer LOS in patients with gastrointestinal or head and neck cancer. Cancer Anthropometry Nutritional Status Malnutrition Length of Hospital Stay Figures Figure 1 Figure 2 Figure 3 Introduction In the cancer setting, malnutrition is considered a risk factor for many complications, such as increased hospital length of stay (LOS), hospital readmission, poor response to treatment, and mortality [ 1 – 3 ]. Involuntary weight loss can affect 50–80% of patients with cancer and will vary in degree depending on the type, stage, and location of the tumor [ 4 ]. In patients with gastrointestinal or head and neck cancer, both nutritional risk and malnutrition prevalence are increased due to nutritional deficits present in these types of cancer caused by the disease itself and the effects of antitumor treatment, which contribute to decreased nutrient intake [ 5 – 7 ]. In gastrointestinal tumors, mechanical obstruction can occur leading to nutrient malabsorption [ 8 ]. In head and neck tumors, the main symptoms are dysphagia, mucositis, difficulty chewing, odynophagia, and decreased food intake [ 9 ]. Continuous nutrition monitoring is required in patients with cancer due to the close relationship of nutritional deficits with reduced response to antitumor therapy and decreased quality of life [ 10 ]. The validated tools for early detection of malnutrition in hospitalized patients with cancer include the Subjective Global Assessment (SGA) and the Patient-Generated SGA (PG-SGA) [ 11 ]. SGA is the method of choice to assess nutritional status [ 12 ], where poor status is associated with increased LOS and mortality [ 13 , 14 ]. PG-SGA is used in the cancer setting [ 15 ], and a PG-SGA diagnosis of malnutrition is also strongly associated with prolonged LOS [ 16 , 17 ]. Nutritional indicators have been used as a complement to nutritional assessment in patients with cancer because of their relationship to malnutrition, including body mass index (BMI), calf circumference (CC), and handgrip strength (HGS) [ 18 – 22 ]. BMI most commonly categorizes patients into underweight, normal weight, overweight, and obesity [ 23 ], where very low BMI (< 18 kg/m 2 ) has been associated with poor clinical outcomes, including an increased risk of death [ 24 , 25 ]. CC measurement is strongly associated with skeletal muscle mass, serving as a useful predictor of hospital readmission and mortality in patients with cancer [ 20 , 21 , 26 ]. HGS has been used to assess muscle function and functional capacity [ 27 , 28 ]. In patients with different types of cancer, those with low HGS were found to be 3 times less likely to be discharged from the hospital [ 16 ]. Because malnutrition is prevalent in patients with gastrointestinal or head and neck cancer, this study aimed to evaluate the ability of individual nutritional indicators (BMI, CC, and HGS) to accurately diagnose malnutrition in these patients, using SGA and PG-SGA as the reference standard methods, and to examine potential associations of nutritional indicators and malnutrition with LOS as the outcome. Methods Study design and participants The data analyzed in this study are part of a previous cohort study including patients with different types of cancer admitted to a teaching hospital from May 2021 to March 2022 [ 29 ]. The research was prepared in accordance with the Declaration of Helsinki and approved by the Ethics Commission of the Hospital de Clínicas de Porto Alegre (protocol #2019.0708), and each study participant provided written informed consent before data collection. The inclusion criteria were age ≥ 18 years, a diagnosis of gastrointestinal or head and neck cancer, ability to communicate coherently and intelligibly, and ability to undergo an HGS test and CC measurement. Patients in the emergency department, in the intensive care unit, receiving palliative care, or with COVID-19 were excluded. Figure 1 shows a flowchart of the patient selection process. Data collection A trained researcher collected patient data at the bedside within 48 hours of hospital admission. The researcher also reviewed electronic medical records to collect sociodemographic data (e.g., age and sex) and clinical characteristics (e.g., cancer type and stage, metastases, chronic diseases, and treatment). Ethnicity was self-reported by the patient or a family member on hospital admission. Self-reported physical activity was obtained by asking patients the following question: “Are you engaged in any kind of physical activity?” (yes/no); if yes, the patients were also asked: “What kind of physical activity?”; “How many times a week do you usually do this activity?”; and “How long have you been doing this activity?”. All patients were followed until discharge for the assessment of LOS, in-hospital mortality, and hospital readmission (within 30 days). Prolonged LOS was defined as LOS ≥ 6 days (this categorization was based on median values). Nutritional assessment and nutritional indicators Trained researchers performed nutritional assessment and calculated nutritional indicators for each study participant within 48 hours of hospital admission. Nutritional assessment Patients were screened for nutritional risk with the PG-SGA Short Form (PG-SGA SF), consisting of 4 sections to be completed by the patient: (1) weight history, (2) food intake, (3) nutrition impact symptoms, and (4) physical function. The 4 scores are summed and a total score ≥ 4 is indicative of nutritional risk and < 4 of no risk [ 30 ]. Both SGA and PG-SGA were used to diagnose malnutrition. The SGA rating divides patients into well nourished (category A), moderately or suspected of being malnourished (category B), and severely malnourished (category C). The diagnosis of malnutrition was found to be most affected by loss of subcutaneous fat, weight loss, muscle wasting, and poor dietary intake [ 12 ]. The PG-SGA is an oncology-specific tool consisting of 2 components: (1) patient-generated component, which corresponds to sections 1–4 of the PG-SGA SF; and (2) professional component, to be completed by the researcher and includes providing information on diagnosis, age, metabolic demand, and physical examination [ 15 ]. After the assessments were completed, the nutritional staging was categorized as follows: well nourished (category A), moderately or suspected of being malnourished (category B), or severely malnourished (category C). Patients categorized in the SGA and PG-SGA as being moderately or severely malnourished were grouped together as having malnutrition. Nutritional indicators The nutritional indicators evaluated in this study were BMI, HGS, and CC. BMI was calculated by dividing the patient’s weight (kg) by height squared (m 2 ) and classified according to the World Health Organization (WHO) criteria [ 23 ] as low BMI (< 18.5 kg/m 2 , underweight), normal BMI (18.5–24.99 kg/m 2 , normal weight), and high BMI (≥ 25 kg/m 2 , overweight). These classifications were transformed to binary categorical variables (low BMI/high BMI) for analysis. Patients were tested for dominant hand HGS (kg) with a previously calibrated Jamar® hand dynamometer. In the sitting position and comfortably holding the dynamometer in the dominant hand, with the arm resting at a right angle with the forearm, patients were instructed to squeeze the handle as hard as possible for at least 3 seconds. The best of 3 attempts, with a 60-second interval between them, was used as maximal muscle strength [ 31 ]. Low HGS was defined as low muscle strength interpreted according to the following cutoffs: < 27 kg for men and < 16 kg for women [ 18 ]. With the patient in the sitting position with the legs at a right angle with the thigh, CC was measured at the point of maximum circumference using a Sanny® tape measure. To remove adiposity confounders, CC values were adjusted for BMI by subtracting 3 cm (BMI: 25–29.9 kg/m 2 ) or 7 cm (BMI: 30–40 kg/m 2 ) from the CC measurement [ 32 ]. Low CC was defined as muscle loss interpreted according to the following cutoffs: ≤ 34 cm for men and ≤ 33 cm for women [ 33 ]. Statistical analysis This study is a second part of a cohort study that included patients with solid tumors [ 29 ]. The Kolmogorov-Smirnov test assessed the normality of the data. Continuous variables were presented as mean (SD) or median (25th–75th percentiles), whereas categorical variables were presented as counts and percentages. The accuracy of each nutritional indicator (BMI, HGS, and CC) in diagnosing malnutrition was measured by the area under the receiver operating characteristic curve (AUC) compared with the reference standards (SGA and PG-SGA), using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with their 95% CIs. Based on the AUC value, the diagnostic accuracy was defined as follows: 0.5–0.6, very poor; 0.6–0.7, poor; 0.7–0.8, moderate; 0.8–0.9, good; and > 0.9, excellent [ 34 ]. Sensitivity and specificity were satisfactory if their values exceeded 80% [ 35 ]. The kappa coefficient was calculated to measure the degree of agreement between the nutritional indicators and the reference standards [ 36 ]. Multiple logistic regression analysis was used to determine whether malnutrition was associated with prolonged LOS (≥ 6 days) as the dependent variable by calculating the odds ratio (OR) and 95% CI. The most important covariates were identified by stepwise regression based on their independent contributions to the models, which were adjusted for sex, age, chronic disease, and metastasis. Data were analyzed using MedCalc, version 20.116 (MedCalc Software, Mariakerke, Belgium), and SPSS, version 25.0 (IBM, Chicago, IL, USA). Statistical significance was set at p value < 0.05. Results General and clinical results and outcomes We evaluated 171 patients, with a mean age of 61.9 (SD, 12.9) years. Older adults accounted for 64.3% of the sample; 52.0% were men, 87.7% self-identified as White, and 83% were physically inactive. Gastrointestinal cancer accounted for 57.9% of the cases (n = 99) and head and neck cancer for 42.1% (n = 72). Regarding treatment, 58.5% (n = 100) were treated surgically, 8.8% (n = 15) received chemotherapy, 1.8% (n = 3) were treated with radiotherapy, and 23.4% (n = 40) received combined treatment. The cancer was diagnosed at an advanced stage (III or IV) in 33.3% of patients (n = 57), and 26.9% (n = 46) had metastases. Chronic diseases included hypertension (50.3%), diabetes (21.1%), and cardiovascular disease (12.3%). The median LOS was 6 (3–11) days; 56.7% were hospitalized for ≥ 6 days. Regarding hospital readmission, 20.5% were readmitted within 30 days of discharge. The average in-hospital mortality rate was 7% (n = 12). Table 1 shows these results. Table 1 Characteristics of patients with gastrointestinal and head and neck cancer (n = 171). Characteristics Values General Age (years) 63 (54–72) a Older adults (≥ 60 years) 110 (64.3) b Sex (male) 89 (52%) b Ethnicity (white) 150 (87.7%) b Physical activity (no) 142 (83%) b Prevalence of type of cancer Gastrointestinal 99 (57.9%) b Head and neck 72 (42.1%) b Treatment of cancer Surgery 100 (58.5%) b Chemotherapy 15 (8.8%) b Radiotherapy 3 (1.8%) b Combined treatment 40 (23.4%) b Tumor stage III/IV 57 (33.3%) b Presence of metastasis (yes) 46 (26.9%) b Chronic diseases Hypertension 86 (50.3%) b Diabetes 36 (21.1%) b Cardiovascular disease 21 (12.3%) b Outcomes Length of stay (days) 6 (3–11) a ≥ 6 days 97 (56.7%) b Readmission in 30 days (yes) 35 (20.5%) b Death (yes) 12 (7%) b a Data expressed as median (p25-p75); b Data expressed as n (%). Prevalence of nutritional risk, malnutrition, and nutritional indicators Table 2 describes participants’ nutritional characteristics. According to the PG-SGA SF, 72.5% of patients (n = 124) were at nutritional risk. Overall, malnutrition was diagnosed in 57.3% of patients with the SGA and in 87.1% of patients with the PG-SGA. Regarding nutritional indicators, 59.1% of patients had low CC, 46.2% had low HGS, and 13.5% had low BMI. In addition, patients at nutritional risk, with malnutrition, low BMI, and low CC were hospitalized longer than the remaining patients (Fig. 2 ). In this group, the main nutrition impact symptoms reported by the patients were loss of appetite (56.2%), xerostomia (25.1%), nausea (24%), and constipation (19.3%). Table 2 Nutritional characteristics of patients with gastrointestinal and head and neck cancer (n = 171). Risk and Nutritional Status Values PG-SGA SF (score ≥ 4) 124 (72.5) a SGA (moderately and severely malnourished) 98 (57.3%) a PG-SGA (moderately and severely malnourished) 149 (87.1%) a Nutritional indicators BMI (kg/m²) 26.2 (5–41) b Low BMI c 23 (13.5%) a Normal BMI d 74 (43.3%) a High BMI e 74 (43.3%) a Low HGS (kg) f 79 (46.2%) a Low CC (cm) g 101 (59.1%) a Treatment symptoms and nutritional effects Appetite loss 96 (56.2%) a Xerostomia 43 (25.1%) a Nausea 41 (24%) a Constipation 33 (19.3%) a a Data expressed as n (%). b Data expressed as median (p25-p75); c Low BMI = < 18.5 kg/m² 23 d Normal BMI = ≥ 18.5–24.99 kg/m² 23 e High BMI = BMI ≥ 25 kg/m 2 23 f Low HGS = Male (< 27 kg); Female (< 16 kg) 18 g Low CC : Male (≤ 34 cm); Female (≤ 33 cm) 33 CC values were adjusted by patients’ BMI to remove the confounding effects of adiposity. 32 Abbreviations: PG-SGA SF , Patient-Generated Subjective Global Assessment Short; SGA , Subjective Global Assessment; PG-SGA , Patient-Generated Subjective Global Assessment; BMI , Body Mass Index; HGS , hand grip strength; CC , calf circumference. Accuracy of each nutritional indicator in diagnosing malnutrition Table 3 shows how accurately each nutritional indicator (BMI, HGS, and CC) can diagnose malnutrition, using SGA and PG-SGA as the reference standard methods (Fig. 3). All nutritional indicators performed poorly in diagnosing malnutrition (AUC < 0.70) and were only in fair agreement with SGA and PG-SGA (kappa < 0.40). However, despite a sensitivity of only 21.4%, low BMI had the highest specificity (97.3%) and PPV (91.3%) compared with the other nutritional indicators. Table 3 Accuracy of isolated nutritional indicators (body mass index, handgrip strength, and calf circumference) in diagnosing malnutrition in patients with gastrointestinal and head and neck cancer (using SGA and PG-SGA as reference methods) (n = 171). Nutritional Indicators Low BMI a Low HGS b Low CC c SGA as reference Kappa ( P value) 0.165 ( p < 0.001) * 0.156 ( p = 0.037) ** 0.147 ( p = 0.054) Accuracy (%) 53.8 57.3 58.5 AUC ROC (CI 95%) 0.593 (0.509–0.678) 0.580 (0.494–0.667) 0.573 (0.486–0.660) Sensitivity (%) 21.4 53.1 65.3 Specificity (%) 97.3 63.0 49.3 Positive predictive value (%) 91.3 65.8 63.4 Negative predictive value (%) 47.9 50.0 51.4 PG-SGA as reference Kappa ( P value) 0.045 ( p = 0.048) ** 0.114 ( p = 0.018) ** 0.054 ( p = 0.354) Accuracy (%) 26.3 53.2 59.1 AUC ROC (CI 95%) 0.577 (0.462–0.692) 0.635 (0.517–0.752) 0.552 (0.422–0.682) Sensitivity (%) 15.4 49.7 60.4 Specificity (%) 100.0 77.3 50.0 Positive predictive value (%) 100.0 93.6 89.1 Negative predictive value (%) 14.9 18.58 15.8 SGA : 57.3% malnutrition prevalence. PG-SGA : 87.1% malnutrition prevalence. a Low BMI : according to WHO (<18.5 kg/m²) 23 b Low HGS = Male (<27 kg); Female (<16 kg) 18 c Low CC : Male (≤34 cm); Female (≤33 cm). 33 CC values were adjusted by the patient’s BMI, to help to remove the confounding effects of adiposity 32 * p < 0.001; ** p < 0.05. Abbreviations: BMI , Body Mass Index; HGS , hand grip strength; CC , calf circumference; SGA , Subjective Global Assessment; PG-SGA , Patient-Generated Subjective Global Assessment. Association between nutritional indicators and malnutrition as a predictor of prolonged hospitalization Table 4 describes the association of nutritional indicators (BMI, HGS, and CC) and malnutrition (SGA and PG-SGA) with prolonged LOS (≥ 6 days). The logistic regression model, adjusted for sex, age, chronic disease, and metastasis, showed that patients with low BMI and low CC were 1.79 ( p = 0.026) and 1.71 ( p = 0.111) times more likely to be hospitalized for ≥ 6 days, respectively. Furthermore, malnourished patients, as diagnosed by SGA and SGA-PG, were 3.60 ( p < 0.001) and 2.78 ( p = 0.048) times more likely to have prolonged LOS, respectively, than well-nourished patients. Table 4 Nutritional indicators and malnutrition associated with hospitalization (≥ 6 days) in patients with gastrointestinal and head and neck cancer: Logistic regression model (n = 171). OR a 95%CI p value Nutritional Indicators Low BMI b 1.79 1.64–5.00 0.026 Low HGS c 1.53 0.77–3.02 0.218 Low CC d 1.71 0.88–3.31 0.111 Malnutrition SGA (B and C) 3.60 1.83–7.09 < 0.001 * PG-SGA (B and C) 2.78 1.01–7.67 0.048 ** a Models were adjusted by age, sex, presence of metastasis, and chronic diseases. b Low BMI = < 18.5 kg/m²) according to the WHO. 23 c Malnutrition: patients classified as moderately (B) and severely malnourished (C) were grouped. d Low HGS = Male (< 27 kg); Female (< 16 kg) 18 ; Low CC = Male (≤ 34 cm); Female (≤ 33 cm). 33 CC values were adjusted by patients’ BMI to remove the confounding effects of adiposity. 32 * p < 0.001; ** p < 0.05. Abbreviations: OR , Odds Ratio; CI , Confidence Interval; BMI , Body Mass Index; SGA , Subjective Global Assessment; PG-SGA , Patient-Generated Subjective Global Assessment Discussion In this study, we showed that individual nutritional indicators (BMI, HGS, and CC) had poor performance (AUC < 0.70) and agreement (kappa < 0.20) in diagnosing malnutrition compared with SGA and PG-SGA. Low BMI (< 18.5 kg/m 2 ), however, had adequate PPV and specificity (> 80%) for the diagnosis of malnutrition. Therefore, the BMI cutoff of 18.5 kg/m 2 could serve as a complement to nutritional assessment in patients with gastrointestinal or head and neck cancer. Our results also showed a positive association between low BMI and LOS ≥ 6 days. Prevalence of nutritional risk, malnutrition, and nutritional indicators Malnutrition is highly prevalent in hospitalized patients with cancer, varying in degree depending on the location and stage of the tumor [ 37 ]. In our cohort of patients with gastrointestinal or head and neck cancer, nutritional risk was identified in 72.5% (PG-SGA SF) and malnutrition in 57.3% (SGA) and 87.1% (PG-SGA). Our results are consistent with those of previous studies investigating these two types of cancer, with malnutrition ranging from 43.8–86.3% depending on the nutritional assessment tool used [ 38 – 41 ]. In fact, PG-SGA can identify a higher prevalence of malnutrition because it is an oncology-specific tool that assesses symptoms and clinical conditions specific to patients with cancer [ 15 ]. Individual nutritional indicators (BMI, HGS, and CC) can complement nutritional assessment and have been associated with negative outcomes in patients with cancer [ 25 , 41 ]. In our study, low BMI according to the WHO criteria [ 23 ] was identified in 13.5% of patients, and this group had longer LOS than patients with normal BMI. HGS assesses muscle function and has recently been recommended as a complementary measure in hospitalized patients [ 10 ]. Almost half of our patients (46.2%) had low HGS, which agrees with previous studies in which low HGS was identified in 38% of patients with advanced cancer [ 14 ] and in 48% of malnourished patients with cancer [ 29 ]. Also, having low HGS was found to decrease the odds of hospital discharge by 3-fold compared with having high HGS [ 16 ]. CC is an indicator of muscle mass [ 20 ], and low CC was identified in 59.1% of our patients after adjusting these values for BMI [ 32 ]. Likewise, a prevalence of low CC of 56% was reported in patients with gastric and colorectal cancer [ 8 ]. Moreover, in patients with and without cancer, reduced CC values were associated with mortality [ 21 ] and hospital readmission [ 26 ]. Accuracy of each nutritional indicator in diagnosing malnutrition Only low BMI (< 18.5 kg/m 2 ) had adequate specificity for the diagnosis of malnutrition, compared with SGA and PG-SGA. In patients with gastric and colorectal cancer, low BMI (compared with SGA) also demonstrated satisfactory specificity [ 8 ]. Low HGS had a high PPV for the diagnosis of malnutrition compared with PG-SGA. Because malnourished patients with cancer are functionally impaired due to loss of muscle function, low HGS may anticipate the detection of nutritional losses, even before the detection of changes in body composition is possible [ 33 ]. However, in our study, HGS alone could not diagnose malnutrition. A previous study of patients with cancer showed an association between reduced HGS and malnutrition, which may be explained by the ability of cancer-induced nutritional damage to accelerate the decrease in HGS [ 29 ]. CC is closely related to whole-body muscle mass and offers prognostic value for clinical and oncological outcomes [ 20 , 21 ]. In our study, low CC agreed poorly with SGA and PG-SGA in the diagnosis of malnutrition. Fair agreement has also been found between low CC (< 31 cm as the cutoff) and PG-SGA in older patients with cancer [ 21 ]. We highlight that, in the present study, we used cutoffs according to sex [ 33 ] and adjusted CC for BMI, thus avoiding adiposity confounders [ 32 ]. This adjustment is appropriate because it can minimize classification errors, such as classifying patients with obesity as having normal CC values. For example, in the present study, approximately 40% of patients had high BMI. Association of nutritional indicators and malnutrition with hospitalization Patients with low BMI and CC had longer LOS in our study. These data are consistent with the results of a meta-analysis suggesting a relationship between BMI < 18.5 kg/m 2 and LOS in older patients with cancer [ 24 ] and a study reporting an association between low CC and prolonged LOS (≥ 9 days) in hospitalized adults [ 42 ]. In the present study, in addition to a positive association between low BMI and LOS ≥ 6 days, we found that malnourished patients diagnosed by SGA and PG-SGA were 3.60 and 2.78 times more likely to have prolonged LOS, respectively, than well-nourished patients. In patients with different types of cancer, poorer nutritional status has been associated with prolonged LOS [ 13 , 14 , 16 , 17 ]. Implications for clinical practice BMI, HGS, and CC are widely used in clinical practice, but our findings suggest that these nutritional indicators might inappropriately diagnose malnutrition when used alone in the cancer setting. In patients with gastrointestinal or head and neck cancer, however, BMI < 18.5 kg/m 2 may be used in combination with SGA or PG-SGA for nutritional assessment. This information has great utility in the clinical setting because BMI values can differ between patient groups [ 8 , 43 , 44 ]. HGS is a measure used to assess muscle function that can complement nutritional assessment and is associated with reduced functional capacity and malnutrition [ 29 ], a scenario further complicated in older patients [ 45 ]. Approximately 65% of the patients in our sample were older adults. CC is considered a sensitive anthropometric index to assess muscle mass and an important measure to evaluate loss of muscle mass (20), but the use of BMI-adjusted cutoffs has been suggested [ 32 ]. This study used PG-SGA, an oncology-specific tool, as a reference to evaluate the diagnostic accuracy of the nutritional indicators. The use of this tool in our population was important because it evaluates signs and symptoms specific to patients with cancer [ 15 ]. For instance, 56.2% of our patients reported loss of appetite, which directly affects their nutritional status. Also, LOS may negatively affect patients’ nutritional status; in the present study, malnourished patients had longer LOS than well-nourished patients regardless of the tool used for diagnosis. Limitations Limitations of this study include heterogeneous ages and cancer stages in our sample, but their effects were minimized by adjusting the logistic regression model for sex, age, chronic disease, and metastasis. However, our data support that malnutrition is highly prevalent among patients with gastrointestinal or head and neck cancer upon hospital admission, making early nutritional assessment even more important to help mitigate poor clinical outcomes, such as prolonged LOS. Conclusion Our findings demonstrated that low BMI (< 18.5 kg/m 2 ), low HGS (< 27 kg for men and < 16 kg for women), and low CC (≤ 34 cm for men and ≤ 33 cm for women) were poorly accurate in diagnosing malnutrition in patients with gastrointestinal or head and neck cancer, compared with SGA (general tool) and PG-SGA (oncology-specific tool) as the reference standards. However, because low BMI was found to be an independent predictor of longer LOS in our sample, it may be used as a complement to nutritional assessment performed with SGA and PG-SGA in these cancers. Declarations Funding This study was supported by the Fundo de Incentivo a Pesquisa of the Hospital de Clínicas de Porto Alegre, Brazil. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) – Finance Code 001 and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS). Mariana S. Crestani and Giovanna P. Stefani received a scholarship from CAPES. Laura M. Scott received a scholarship from Universidade Federal do Rio Grande do Sul. Competing Interests The authors declare no competing interests. Author Contributions TS and MSC conceived and designed this study. CHS, MSC, GPS, and LMS contributed to data acquisition. CHS and TS analyzed and interpreted data. CHS, GPS, and TS drafted this manuscript, and all authors critically revised it and approved its final version. TS guarantees and attests that all listed authors meet authorship criteria and that no others who meet these criteria have been omitted. Ethics approval This research was prepared in accordance with the Declaration of Helsinki and approved by the Ethics Commission of the Hospital de Clínicas de Porto Alegre (protocol #2019.0708). Consent to participate All participants provided informed consent before data collection. 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Arq Gastroenterol 54(2):148–155. https://doi.org/10.1590/S0004-2803.201700000-05 Contreras-Bolívar V, Sánchez-Torralvo FJ, Ruiz-Vico M, González-Almendros I, Barrios M, Padín S, Alba E, Olveira G (2019) GLIM Criteria Using Hand Grip Strength Adequately Predict Six-Month Mortality in Cancer Inpatients. Nutrients 11(9):2043. https://doi.org/10.3390/nu11092043 Ottery FD (1996) Definition of standardized nutritional assessment and interventional pathways in oncology. Nutrition 12: S15–9. https://doi.org/10.1016/0899-9007(96)90011-8 Mendes J, Alves P, Amaral TF (2014) Comparison of nutritional status assessment parameters in predicting length of hospital stay in cancer patients. Clin Nutr 33(3):466–470. https://doi.org/10.1016/j.clnu.2013.06.016 Yang J, Yuan K, Huang Y, Yu M, Huang X, Chen C, Fu J, Shi Y, Shi H (2016) Comparison of NRS 2002 and PG-SGA for the assessment of nutritional status in cancer patients. Biomed Res 27(4):1178–1182. https://api.semanticscholar.org/CorpusID:51961102 Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA et al (2019) Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48(1):16–31. https://doi.org/10.1093/ageing/afy169 Leandro-Merhi VA, de Aquino JLB, Reis LO (2017) Predictors of Nutritional Risk According to NRS-2002 and Calf Circumference in Hospitalized Older Adults with Neoplasms. Nutr Cancer 69(8):1219–1226. https://doi.org/10.1080/01635581.2017.1367942 Real GG, Frühauf IR, Sedrez JHK, Dall'Aqua EJF, Gonzalez MC (2018) Calf Circumference: A Marker of Muscle Mass as a Predictor of Hospital Readmission. JPEN J Parenter Enteral Nutr. 42(8):1272–1279. https://doi.org/10.1002/jpen.1170 Sousa IM, Bielemann RM, Gonzalez MC, da Rocha IMG, Barbalho ER, de Carvalho ALM, Dantas MAM, Medeiros GOC, Silva FM, Fayh APT (2020) Low calf circumference is an independent predictor of mortality in cancer patients: a prospective cohort study. Nutrition 79–80:110816. https://doi.org/10.1016/j.nut.2020.110816 Garcia MF, Meireles MS, Fuhr LM, Donini AB, Wazlawik E (2013) Relationship between hand grip strength and nutritional assessment methods used of hospitalized patients. Rev Nutr 26(1):49–57. https://doi.org/10.1590/S1415-52732013000100005 WHO (1995) Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 854:1-452. Bullock AF, Greenley SL, McKenzie GAG, Paton LW, Johnson MJ (2020) Relationship between markers of malnutrition and clinical outcomes in older adults with cancer: systematic review, narrative synthesis, and meta-analysis. Eur J Clin Nutr 74(11):1519–1535. https://doi.org/10.1038/s41430-020-0629-0 Hobday S, Armache M, Paquin R, Nurimba M, Baddour K, Linder D, Kouame G, Kharrington S, Albergotti WG, Mady LJ (2023) The Body Mass Index Paradox in Head and Neck Cancer: A Systematic Review and Meta-Analysis. Nutr Cancer 75(1):48–60. https://doi.org/10.1080/01635581.2022.2102659 Wei J, Jiao J, Chen CL, Tao WY, Ying YJ, Zhang WW, Wu XJ, Zhang XM (2022) The association between low calf circumference and mortality: a systematic review and meta-analysis. Eur Geriatr Med 13(3):597–609. https://doi.org/10.1007/s41999-021-00603-3 Schlüssel MM, dos Anjos LA, de Vasconcellos MTL, Kac G (2008) Reference values of handgrip dynamometry of healthy adults: a population-based study. Clin Nutr 27(4):601–607. https://doi.org/10.1016/j.clnu.2008.04.004 Kilgour RD, Viago A, Trutschnigg B, Lucar E, Borod M, Morais JA (2013) Handgrip strength predicts survival and is associated with markers of clinical and functional outcomes in advanced cancer patients. Support Care Cancer 21(12):3261–70. https://doi.org/10.1007/s00520-013-1894-4 Crestani MS, Stefani GP, Scott LM, Steemburgo T (2023) Accuracy of the GLIM Criteria and SGA Compared to PG-SGA for the Diagnosis of Malnutrition and Its Impact on Prolonged Hospitalization: A Prospective Study in Patients with Cancer. Nutr Cancer 75(4):1177–1188. https://doi.org/10.1080/01635581.2023.2184748 Jager-Wittenaar H, Ottery FD (2017) Assessing nutritional status in cancer: role of the Patient-Generated Subjective Global Assessment. Curr Opin Clin Nutr Metab Care 20(5):322–329. https://doi.org/10.1097/mco.0000000000000389 Dodds RM, Syddall HE, Cooper R, Benzeval M, Deary IJ, Dennison EM, Der G, Gale CR, Inskip HM, Jagger C et al (2014) Grip strength across the life course: normative data from twelve British studies. PLoS One 9(12):e113637. https://doi.org/10.1371/journal.pone.0113637 Gonzalez MC, Mehrnezhad A, Razaviarab N, Barbosa-Silva TG, Heymsfield SB (2021) Calf circumference: cutoff values from the NHANES 1999-2006. Am J Clin Nutr 113(6):1679–1687. https://doi.org/10.1093/ajcn/nqab029 Barbosa-Silva TG, Bielemann RM, Gonzalez MC, Menezes AM (2016) Prevalence of sarcopenia among community-dwelling elderly of a medium-sized South American city: results of the COMO VAI? study. J Cachexia Sarcopenia Muscle 7(2):136–143. https://dx.doi.org/10.1002%2Fjcsm.12049 Metz CE (1978) Basic principles of ROC analysis. Semin Nucl Med 8(4):283–298. https://doi.org/10.1016/S0001-2998(78)80014-2 de van der Schueren MAE, Keller H, GLIM Consortium, Cederholm T, Barazzoni R, Compher C, Correia MITD, Gonzalez MC, Jager-Wittenaar H, Pirlich M (2020) Global Leadership Initiative on Malnutrition (GLIM): Guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults. Clin Nutr 39(9):2872–2880. https://doi.org/10.1016/j.clnu.2019.12.022 Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174. https://pubmed.ncbi.nlm.nih.gov/843571/ Wie GA, Cho YA, Kim SY, Kim SM, Bae JM, Joung H (2010) Prevalence and risk factors of malnutrition among cancer patients according to tumor location and stage in the National Cancer Center in Korea. Nutrition 26(3):263–268. https://doi.org/10.1016/j.nut.2009.04.013 Martin L, Findlay M, Bauer JD, Dhaliwal R, de van der Schueren M, Laviano A, Widaman A, Baracos VE, Day AG, Gramlich LM (2022) Multi-Site, International Audit of Malnutrition Risk and Energy and Protein Intakes in Patients Undergoing Treatment for Head Neck and Esophageal Cancer: Results from INFORM. Nutrients 14(24):5272. https://doi.org/10.3390/nu14245272 Arribas L, Hurtós L, Milà R, Fort E, Peiró I (2013) Predict factors associated with malnutrition from patient-generated subjective global assessment (PG-SGA) in head and neck cancer. Nutr Hosp 28(1):155–163. https://doi.org/10.3305/nh.2013.28.1.6168 Stefani GP, Crestani MS, Scott LM, Soares CH, Steemburgo T (2023) Complementarity of nutritional assessment tools to predict prolonged hospital stay and readmission in older patients with solid tumors: a secondary analysis of a cohort study. Nutrition 113:112089. https://doi.org/10.1016/j.nut.2023.112089 Gama RR, Song Y, Zhang Q, Brown MC, Wang J, Habbous S, Tong L, Huang SH, O’Sulilivan B, Waldron J et al (2017) Body mass index and prognosis in patients with head and neck cancer. Head Neck 39(6):1226–1233. https://doi.org/10.1002/hed.24760 Tarnowski M, Stein E, Marcadenti A, Fink J, Rabito E, Silva FM (2020) Calf Circumference Is a Good Predictor of Longer Hospital Stay and Nutritional Risk in Emergency Patients: A Prospective Cohort Study. J Am Coll Nutr 39(7):645–649. https://doi.org/10.1080/07315724.2020.1723452 Wang WJ, Li TT, Wang X, Li W, Cui JW (2020) Combining the Patient-Generated Subjective Global Assessment (PG-SGA) and Objective Nutrition Assessment Parameters Better Predicts Malnutrition in Elderly Patients with Colorectal Cancer. J Nutr Oncol 5(1):22–30. https://doi.org/10.34175/jno202001003 Norman K, Stobäus N, Smoliner C, Zocher D, Scheufele R, Valentini L, Lochs H, Pirlich M (2010) Determinants of hand grip strength, knee extension strength and functional status in cancer patients. Clin Nutr 29(5):586–591. https://doi.org/10.1016/j.clnu.2010.02.007 Hu CL, Yu M, Yuan KT, Yu HL, Shi YY, Yang JJ, Li W, Jiang H, Li Z, Xu H (2018) Determinants and nutritional assessment value of hand grip strength in patients hospitalized with cancer. Asia Pac J Clin Nutr 27(4):777–784. https://doi.org/10.6133/apjcn.072017.04 Additional Declarations No competing interests reported. Supplementary Files STROBEchecklistcohort.docx 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. 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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-3849041\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":266397696,\"identity\":\"5cb2a56a-218a-4363-99b2-579bdbb60888\",\"order_by\":0,\"name\":\"Camilla Soares\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal do Rio Grande do Sul\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Camilla\",\"middleName\":\"\",\"lastName\":\"Soares\",\"suffix\":\"\"},{\"id\":266397697,\"identity\":\"50f71ec1-9811-40a3-826e-d1ad04096e85\",\"order_by\":1,\"name\":\"Giovanna Stefani\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal do Rio Grande do Sul\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Giovanna\",\"middleName\":\"\",\"lastName\":\"Stefani\",\"suffix\":\"\"},{\"id\":266397698,\"identity\":\"738f8a09-c329-4301-95d3-cb076905b54e\",\"order_by\":2,\"name\":\"Laura Scott\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal do Rio Grande do Sul\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Laura\",\"middleName\":\"\",\"lastName\":\"Scott\",\"suffix\":\"\"},{\"id\":266397699,\"identity\":\"93d38c4a-dcce-481f-ad32-f107b90a3823\",\"order_by\":3,\"name\":\"Mariana Crestani\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal do Rio Grande do Sul\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mariana\",\"middleName\":\"\",\"lastName\":\"Crestani\",\"suffix\":\"\"},{\"id\":266397700,\"identity\":\"5bd61803-7a87-453b-88a4-7a4a9de9b037\",\"order_by\":4,\"name\":\"Thais Steemburgo\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYPCCAyCC8QGQ4OEjpJaHgRmuhdkAJMBGihY2CTBJSIs9//mDDz7m3Enc3r/4WOXXHDsZNgbmh49u4LNFIpnZcOa2Z4lzbjxLuy27LRnoMDZj4xy8WpjZpHm3HU6cIXHG7LbkNmagFh42abxa+A+z//4L1VIsua2eCC0MyWzMjCAt/D1mjB+3HSZCy41kY8nebc+MZ0iwJUszbjvOw8ZMwC/s/Qcffvi57Y7sDP7DBz/+3FZtz8/e/PAxPi0IIJHAwMwDYjATpRwE+A8wMP4gWvUoGAWjYBSMJAAA36JHjFo2Z54AAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Universidade Federal do Rio Grande do Sul\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Thais\",\"middleName\":\"\",\"lastName\":\"Steemburgo\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-09 19:14:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3849041/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3849041/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":49539143,\"identity\":\"a5d95f3b-b962-4ecf-b9bd-73fc60737f24\",\"added_by\":\"auto\",\"created_at\":\"2024-01-12 17:04:00\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":322633,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart of patient selection\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3849041/v1/15ae419ed1492ed4d9e66a93.jpg\"},{\"id\":49539146,\"identity\":\"e6d0956d-8d6e-4e71-abe5-3ec1897137a7\",\"added_by\":\"auto\",\"created_at\":\"2024-01-12 17:04:00\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":198870,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelation between nutritional characteristics and hospitalization (≥ 6 days) in patients with gastrointestinal and head and neck cancer (\\u003cem\\u003en\\u003c/em\\u003e = 171)\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: \\u003cem\\u003ePG-SGA SF\\u003c/em\\u003e, Patient-Generated Subjective Global Assessment Short; \\u003cem\\u003eSGA\\u003c/em\\u003e, Subjective Global Assessment; \\u003cem\\u003ePG-SGA\\u003c/em\\u003e, Patient-Generated SGA; \\u003cem\\u003eBMI\\u003c/em\\u003e, Body Mass Index; HGS, Hand Grip Strength;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCC,\\u003c/em\\u003e Calf Circumference.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Chi square test.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003e*\\u003c/sup\\u003e\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3849041/v1/c2784078e29f326bf1642ed9.jpg\"},{\"id\":49539144,\"identity\":\"2c8210cc-7f3d-40e2-b0cb-8fe30df8f21f\",\"added_by\":\"auto\",\"created_at\":\"2024-01-12 17:04:00\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":310686,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eReceiver operating characteristic (ROC) curves using nutritional indicators to diagnose malnutrition in patients with gastrointestinal and head and neck cancer (SGA and PG-SGA as reference methods)\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: BMI, Body Mass Index; HGS, Hand Grip Strength; CC, Calf Circumference; SGA, Subjective Global Assessment; PG-SGA, Patient-Generated Subjective Global Assessment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3849041/v1/1150b942f49de0f26863473a.jpg\"},{\"id\":57470345,\"identity\":\"4e16a3bd-6dd0-4b3c-aad4-b5a3c787994f\",\"added_by\":\"auto\",\"created_at\":\"2024-05-31 06:20:23\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1702246,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3849041/v1/95b90c05-ae3b-4d0a-97a6-b7577b776fd2.pdf\"},{\"id\":49539145,\"identity\":\"7a1ebff2-7f2d-4abd-9e82-5a730beed945\",\"added_by\":\"auto\",\"created_at\":\"2024-01-12 17:04:00\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":33910,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"STROBEchecklistcohort.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3849041/v1/66320f762387630da53c6f49.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Low BMI Demonstrates Satisfactory Specificity for Diagnosing Malnutrition and is Associated with Longer Hospitalization in Patients with Gastrointestinal or Head and Neck Cancer: A Prospective Cohort Study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eIn the cancer setting, malnutrition is considered a risk factor for many complications, such as increased hospital length of stay (LOS), hospital readmission, poor response to treatment, and mortality [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Involuntary weight loss can affect 50\\u0026ndash;80% of patients with cancer and will vary in degree depending on the type, stage, and location of the tumor [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. In patients with gastrointestinal or head and neck cancer, both nutritional risk and malnutrition prevalence are increased due to nutritional deficits present in these types of cancer caused by the disease itself and the effects of antitumor treatment, which contribute to decreased nutrient intake [\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. In gastrointestinal tumors, mechanical obstruction can occur leading to nutrient malabsorption [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. In head and neck tumors, the main symptoms are dysphagia, mucositis, difficulty chewing, odynophagia, and decreased food intake [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eContinuous nutrition monitoring is required in patients with cancer due to the close relationship of nutritional deficits with reduced response to antitumor therapy and decreased quality of life [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. The validated tools for early detection of malnutrition in hospitalized patients with cancer include the Subjective Global Assessment (SGA) and the Patient-Generated SGA (PG-SGA) [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. SGA is the method of choice to assess nutritional status [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], where poor status is associated with increased LOS and mortality [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. PG-SGA is used in the cancer setting [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], and a PG-SGA diagnosis of malnutrition is also strongly associated with prolonged LOS [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNutritional indicators have been used as a complement to nutritional assessment in patients with cancer because of their relationship to malnutrition, including body mass index (BMI), calf circumference (CC), and handgrip strength (HGS) [\\u003cspan additionalcitationids=\\\"CR19 CR20 CR21\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. BMI most commonly categorizes patients into underweight, normal weight, overweight, and obesity [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], where very low BMI (\\u0026lt;\\u0026thinsp;18 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) has been associated with poor clinical outcomes, including an increased risk of death [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. CC measurement is strongly associated with skeletal muscle mass, serving as a useful predictor of hospital readmission and mortality in patients with cancer [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. HGS has been used to assess muscle function and functional capacity [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. In patients with different types of cancer, those with low HGS were found to be 3 times less likely to be discharged from the hospital [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBecause malnutrition is prevalent in patients with gastrointestinal or head and neck cancer, this study aimed to evaluate the ability of individual nutritional indicators (BMI, CC, and HGS) to accurately diagnose malnutrition in these patients, using SGA and PG-SGA as the reference standard methods, and to examine potential associations of nutritional indicators and malnutrition with LOS as the outcome.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and participants\\u003c/h2\\u003e \\u003cp\\u003eThe data analyzed in this study are part of a previous cohort study including patients with different types of cancer admitted to a teaching hospital from May 2021 to March 2022 [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. The research was prepared in accordance with the Declaration of Helsinki and approved by the Ethics Commission of the Hospital de Cl\\u0026iacute;nicas de Porto Alegre (protocol #2019.0708), and each study participant provided written informed consent before data collection. The inclusion criteria were age\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years, a diagnosis of gastrointestinal or head and neck cancer, ability to communicate coherently and intelligibly, and ability to undergo an HGS test and CC measurement. Patients in the emergency department, in the intensive care unit, receiving palliative care, or with COVID-19 were excluded. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows a flowchart of the patient selection process.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData collection\\u003c/h2\\u003e \\u003cp\\u003eA trained researcher collected patient data at the bedside within 48 hours of hospital admission. The researcher also reviewed electronic medical records to collect sociodemographic data (e.g., age and sex) and clinical characteristics (e.g., cancer type and stage, metastases, chronic diseases, and treatment). Ethnicity was self-reported by the patient or a family member on hospital admission. Self-reported physical activity was obtained by asking patients the following question: \\u0026ldquo;Are you engaged in any kind of physical activity?\\u0026rdquo; (yes/no); if yes, the patients were also asked: \\u0026ldquo;What kind of physical activity?\\u0026rdquo;; \\u0026ldquo;How many times a week do you usually do this activity?\\u0026rdquo;; and \\u0026ldquo;How long have you been doing this activity?\\u0026rdquo;.\\u003c/p\\u003e \\u003cp\\u003eAll patients were followed until discharge for the assessment of LOS, in-hospital mortality, and hospital readmission (within 30 days). Prolonged LOS was defined as LOS\\u0026thinsp;\\u0026ge;\\u0026thinsp;6 days (this categorization was based on median values).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNutritional assessment and nutritional indicators\\u003c/h2\\u003e \\u003cp\\u003eTrained researchers performed nutritional assessment and calculated nutritional indicators for each study participant within 48 hours of hospital admission.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNutritional assessment\\u003c/h2\\u003e \\u003cp\\u003ePatients were screened for nutritional risk with the PG-SGA Short Form (PG-SGA SF), consisting of 4 sections to be completed by the patient: (1) weight history, (2) food intake, (3) nutrition impact symptoms, and (4) physical function. The 4 scores are summed and a total score\\u0026thinsp;\\u0026ge;\\u0026thinsp;4 is indicative of nutritional risk and \\u0026lt;\\u0026thinsp;4 of no risk [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBoth SGA and PG-SGA were used to diagnose malnutrition. The SGA rating divides patients into well nourished (category A), moderately or suspected of being malnourished (category B), and severely malnourished (category C). The diagnosis of malnutrition was found to be most affected by loss of subcutaneous fat, weight loss, muscle wasting, and poor dietary intake [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The PG-SGA is an oncology-specific tool consisting of 2 components: (1) patient-generated component, which corresponds to sections 1\\u0026ndash;4 of the PG-SGA SF; and (2) professional component, to be completed by the researcher and includes providing information on diagnosis, age, metabolic demand, and physical examination [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. After the assessments were completed, the nutritional staging was categorized as follows: well nourished (category A), moderately or suspected of being malnourished (category B), or severely malnourished (category C). Patients categorized in the SGA and PG-SGA as being moderately or severely malnourished were grouped together as having malnutrition.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNutritional indicators\\u003c/h2\\u003e \\u003cp\\u003eThe nutritional indicators evaluated in this study were BMI, HGS, and CC. BMI was calculated by dividing the patient\\u0026rsquo;s weight (kg) by height squared (m\\u003csup\\u003e2\\u003c/sup\\u003e) and classified according to the World Health Organization (WHO) criteria [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e] as low BMI (\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, underweight), normal BMI (18.5\\u0026ndash;24.99 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, normal weight), and high BMI (\\u0026ge;\\u0026thinsp;25 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, overweight). These classifications were transformed to binary categorical variables (low BMI/high BMI) for analysis.\\u003c/p\\u003e \\u003cp\\u003ePatients were tested for dominant hand HGS (kg) with a previously calibrated Jamar\\u0026reg; hand dynamometer. In the sitting position and comfortably holding the dynamometer in the dominant hand, with the arm resting at a right angle with the forearm, patients were instructed to squeeze the handle as hard as possible for at least 3 seconds. The best of 3 attempts, with a 60-second interval between them, was used as maximal muscle strength [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Low HGS was defined as low muscle strength interpreted according to the following cutoffs: \\u0026lt; 27 kg for men and \\u0026lt;\\u0026thinsp;16 kg for women [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWith the patient in the sitting position with the legs at a right angle with the thigh, CC was measured at the point of maximum circumference using a Sanny\\u0026reg; tape measure. To remove adiposity confounders, CC values were adjusted for BMI by subtracting 3 cm (BMI: 25\\u0026ndash;29.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) or 7 cm (BMI: 30\\u0026ndash;40 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) from the CC measurement [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Low CC was defined as muscle loss interpreted according to the following cutoffs: \\u0026le; 34 cm for men and \\u0026le;\\u0026thinsp;33 cm for women [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThis study is a second part of a cohort study that included patients with solid tumors [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe Kolmogorov-Smirnov test assessed the normality of the data. Continuous variables were presented as mean (SD) or median (25th\\u0026ndash;75th percentiles), whereas categorical variables were presented as counts and percentages.\\u003c/p\\u003e \\u003cp\\u003eThe accuracy of each nutritional indicator (BMI, HGS, and CC) in diagnosing malnutrition was measured by the area under the receiver operating characteristic curve (AUC) compared with the reference standards (SGA and PG-SGA), using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with their 95% CIs. Based on the AUC value, the diagnostic accuracy was defined as follows: 0.5\\u0026ndash;0.6, very poor; 0.6\\u0026ndash;0.7, poor; 0.7\\u0026ndash;0.8, moderate; 0.8\\u0026ndash;0.9, good; and \\u0026gt;\\u0026thinsp;0.9, excellent [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Sensitivity and specificity were satisfactory if their values exceeded 80% [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. The kappa coefficient was calculated to measure the degree of agreement between the nutritional indicators and the reference standards [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eMultiple logistic regression analysis was used to determine whether malnutrition was associated with prolonged LOS (\\u0026ge;\\u0026thinsp;6 days) as the dependent variable by calculating the odds ratio (OR) and 95% CI. The most important covariates were identified by stepwise regression based on their independent contributions to the models, which were adjusted for sex, age, chronic disease, and metastasis.\\u003c/p\\u003e \\u003cp\\u003eData were analyzed using MedCalc, version 20.116 (MedCalc Software, Mariakerke, Belgium), and SPSS, version 25.0 (IBM, Chicago, IL, USA). 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\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eGeneral and clinical results and outcomes\\u003c/h2\\u003e\\n\\u003cp\\u003eWe evaluated 171 patients, with a mean age of 61.9 (SD, 12.9) years. Older adults accounted for 64.3% of the sample; 52.0% were men, 87.7% self-identified as White, and 83% were physically inactive. Gastrointestinal cancer accounted for 57.9% of the cases (n\\u0026thinsp;=\\u0026thinsp;99) and head and neck cancer for 42.1% (n\\u0026thinsp;=\\u0026thinsp;72). Regarding treatment, 58.5% (n\\u0026thinsp;=\\u0026thinsp;100) were treated surgically, 8.8% (n\\u0026thinsp;=\\u0026thinsp;15) received chemotherapy, 1.8% (n\\u0026thinsp;=\\u0026thinsp;3) were treated with radiotherapy, and 23.4% (n\\u0026thinsp;=\\u0026thinsp;40) received combined treatment. The cancer was diagnosed at an advanced stage (III or IV) in 33.3% of patients (n\\u0026thinsp;=\\u0026thinsp;57), and 26.9% (n\\u0026thinsp;=\\u0026thinsp;46) had metastases. Chronic diseases included hypertension (50.3%), diabetes (21.1%), and cardiovascular disease (12.3%). The median LOS was 6 (3\\u0026ndash;11) days; 56.7% were hospitalized for \\u0026ge;\\u0026thinsp;6 days. Regarding hospital readmission, 20.5% were readmitted within 30 days of discharge. The average in-hospital mortality rate was 7% (n\\u0026thinsp;=\\u0026thinsp;12). Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows these results.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eCharacteristics of patients with gastrointestinal and head and neck cancer (n\\u0026thinsp;=\\u0026thinsp;171).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eValues\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eGeneral\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAge (years)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e63 (54\\u0026ndash;72)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eOlder adults (\\u0026ge;\\u0026thinsp;60 years)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e110 (64.3)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSex (male)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e89 (52%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEthnicity (white)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e150 (87.7%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePhysical activity (no)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e142 (83%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePrevalence of type of cancer\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eGastrointestinal\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e99 (57.9%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHead and neck\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e72 (42.1%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTreatment of cancer\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSurgery\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e100 (58.5%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eChemotherapy\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e15 (8.8%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eRadiotherapy\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e3 (1.8%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCombined treatment\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e40 (23.4%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTumor stage III/IV\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e57 (33.3%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePresence of metastasis (yes)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e46 (26.9%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eChronic diseases\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHypertension\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e86 (50.3%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDiabetes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e36 (21.1%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCardiovascular disease\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e21 (12.3%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOutcomes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLength of stay (days)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e6 (3\\u0026ndash;11)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u0026ge; 6 days\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e97 (56.7%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eReadmission in 30 days (yes)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e35 (20.5%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDeath (yes)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e12 (7%)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003ctfoot\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Data expressed as median (p25-p75);\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Data expressed as n (%).\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tfoot\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003ePrevalence of nutritional risk, malnutrition, and nutritional indicators\\u003c/h2\\u003e\\n\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e describes participants\\u0026rsquo; nutritional characteristics. According to the PG-SGA SF, 72.5% of patients (n\\u0026thinsp;=\\u0026thinsp;124) were at nutritional risk. Overall, malnutrition was diagnosed in 57.3% of patients with the SGA and in 87.1% of patients with the PG-SGA. Regarding nutritional indicators, 59.1% of patients had low CC, 46.2% had low HGS, and 13.5% had low BMI. In addition, patients at nutritional risk, with malnutrition, low BMI, and low CC were hospitalized longer than the remaining patients (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In this group, the main nutrition impact symptoms reported by the patients were loss of appetite (56.2%), xerostomia (25.1%), nausea (24%), and constipation (19.3%).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eNutritional characteristics of patients with gastrointestinal and head and neck cancer (n\\u0026thinsp;=\\u0026thinsp;171).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eRisk and Nutritional Status\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eValues\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePG-SGA SF (score\\u0026thinsp;\\u0026ge;\\u0026thinsp;4)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e124 (72.5)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSGA (moderately and severely malnourished)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e98 (57.3%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePG-SGA (moderately and severely malnourished)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e149 (87.1%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNutritional indicators\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eBMI (kg/m\\u0026sup2;)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e26.2 (5\\u0026ndash;41)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow BMI\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e23 (13.5%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNormal BMI\\u003csup\\u003ed\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e74 (43.3%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eHigh BMI\\u003csup\\u003ee\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e74 (43.3%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow HGS (kg)\\u003csup\\u003ef\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e79 (46.2%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow CC (cm)\\u003csup\\u003eg\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e101 (59.1%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTreatment symptoms and nutritional effects\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAppetite loss\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e96 (56.2%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eXerostomia\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e43 (25.1%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNausea\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e41 (24%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eConstipation\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e33 (19.3%)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003ctfoot\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Data expressed as n (%).\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Data expressed as median (p25-p75);\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e Low \\u003cem\\u003eBMI\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;18.5 kg/m\\u0026sup2; \\u003csup\\u003e23\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003ed\\u003c/sup\\u003e Normal \\u003cem\\u003eBMI\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026ge;\\u0026thinsp;18.5\\u0026ndash;24.99 kg/m\\u0026sup2; \\u003csup\\u003e23\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003ee\\u003c/sup\\u003e High BMI\\u0026thinsp;=\\u0026thinsp;\\u003cem\\u003eBMI\\u003c/em\\u003e\\u0026thinsp;\\u0026ge;\\u0026thinsp;25 kg/m\\u003csup\\u003e2 23\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003ef\\u003c/sup\\u003e Low \\u003cem\\u003eHGS\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;Male (\\u0026lt;\\u0026thinsp;27 kg); Female (\\u0026lt;\\u0026thinsp;16 kg)\\u003csup\\u003e18\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003e\\u003csup\\u003eg\\u003c/sup\\u003e Low \\u003cem\\u003eCC\\u003c/em\\u003e: Male (\\u0026le;\\u0026thinsp;34 cm); Female (\\u0026le;\\u0026thinsp;33 cm)\\u003csup\\u003e33\\u003c/sup\\u003e CC values were adjusted by patients\\u0026rsquo; \\u003cem\\u003eBMI\\u003c/em\\u003e to remove the confounding effects of adiposity.\\u003csup\\u003e32\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"2\\\"\\u003eAbbreviations: \\u003cem\\u003ePG-SGA SF\\u003c/em\\u003e, Patient-Generated Subjective Global Assessment Short; \\u003cem\\u003eSGA\\u003c/em\\u003e, Subjective Global Assessment; \\u003cem\\u003ePG-SGA\\u003c/em\\u003e, Patient-Generated Subjective Global Assessment; \\u003cem\\u003eBMI\\u003c/em\\u003e, Body Mass Index; \\u003cem\\u003eHGS\\u003c/em\\u003e, hand grip strength; \\u003cem\\u003eCC\\u003c/em\\u003e, calf circumference.\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tfoot\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eAccuracy of each nutritional indicator in diagnosing malnutrition\\u003c/h2\\u003e\\n\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows how accurately each nutritional indicator (BMI, HGS, and CC) can diagnose malnutrition, using SGA and PG-SGA as the reference standard methods (Fig.\\u0026nbsp;3). All nutritional indicators performed poorly in diagnosing malnutrition (AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.70) and were only in fair agreement with SGA and PG-SGA (kappa\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.40). However, despite a sensitivity of only 21.4%, low BMI had the highest specificity (97.3%) and PPV (91.3%) compared with the other nutritional indicators.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eAccuracy of isolated nutritional indicators (body mass index, handgrip strength, and calf circumference) in diagnosing malnutrition in patients with gastrointestinal and head and neck cancer (using SGA and PG-SGA as reference methods) (n\\u0026thinsp;=\\u0026thinsp;171).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNutritional Indicators\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow BMI\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow HGS\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow CC\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003cth colspan=\\\"3\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSGA as reference\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eKappa (\\u003cem\\u003eP\\u003c/em\\u003e value)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.165 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.156 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.037) \\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.147 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.054)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAccuracy (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e53.8\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e57.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e58.5\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAUC ROC (CI 95%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.593 (0.509\\u0026ndash;0.678)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.580 (0.494\\u0026ndash;0.667)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.573 (0.486\\u0026ndash;0.660)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSensitivity (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e21.4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e53.1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e65.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSpecificity (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e97.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e63.0\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e49.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePositive predictive value (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e91.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e65.8\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e63.4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNegative predictive value (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e47.9\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e50.0\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e51.4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"6\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePG-SGA as reference\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eKappa (\\u003cem\\u003eP\\u003c/em\\u003e value)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.045 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048) \\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.114 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.018) \\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.054 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.354)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAccuracy (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e26.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e53.2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e59.1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAUC ROC (CI 95%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.577 (0.462\\u0026ndash;0.692)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.635 (0.517\\u0026ndash;0.752)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.552 (0.422\\u0026ndash;0.682)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSensitivity (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e15.4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e49.7\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e60.4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSpecificity (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e100.0\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e77.3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e50.0\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePositive predictive value (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e100.0\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e93.6\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e89.1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNegative predictive value (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e14.9\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e18.58\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e15.8\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSGA\\u003c/em\\u003e: 57.3% malnutrition prevalence.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePG-SGA\\u003c/em\\u003e: 87.1% malnutrition prevalence.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ea \\u003c/sup\\u003eLow \\u003cem\\u003eBMI\\u003c/em\\u003e: according to WHO (\\u0026lt;18.5 kg/m\\u0026sup2;)\\u003csup\\u003e23\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003eb \\u003c/sup\\u003eLow \\u003cem\\u003eHGS\\u003c/em\\u003e = Male (\\u0026lt;27 kg); Female (\\u0026lt;16 kg)\\u003csup\\u003e18\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003csup\\u003ec \\u003c/sup\\u003eLow \\u003cem\\u003eCC\\u003c/em\\u003e: Male (\\u0026le;34 cm); Female (\\u0026le;33 cm).\\u003csup\\u003e33\\u003c/sup\\u003e CC values were adjusted by the patient\\u0026rsquo;s BMI, to help to remove the confounding effects of adiposity\\u003csup\\u003e32\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e* \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.001; **\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: \\u003cem\\u003eBMI\\u003c/em\\u003e, Body Mass Index; \\u003cem\\u003eHGS\\u003c/em\\u003e, hand grip strength; \\u003cem\\u003eCC\\u003c/em\\u003e, calf circumference; \\u003cem\\u003eSGA\\u003c/em\\u003e, Subjective Global Assessment; \\u003cem\\u003ePG-SGA\\u003c/em\\u003e, Patient-Generated Subjective Global Assessment.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eAssociation between nutritional indicators and malnutrition as a predictor of prolonged hospitalization\\u003c/h2\\u003e\\n\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e describes the association of nutritional indicators (BMI, HGS, and CC) and malnutrition (SGA and PG-SGA) with prolonged LOS (\\u0026ge;\\u0026thinsp;6 days). The logistic regression model, adjusted for sex, age, chronic disease, and metastasis, showed that patients with low BMI and low CC were 1.79 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.026) and 1.71 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.111) times more likely to be hospitalized for \\u0026ge;\\u0026thinsp;6 days, respectively. Furthermore, malnourished patients, as diagnosed by SGA and SGA-PG, were 3.60 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 2.78 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.048) times more likely to have prolonged LOS, respectively, than well-nourished patients.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eNutritional indicators and malnutrition associated with hospitalization (\\u0026ge;\\u0026thinsp;6 days) in patients with gastrointestinal and head and neck cancer: Logistic regression model (n\\u0026thinsp;=\\u0026thinsp;171).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eOR\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e95%CI\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e value\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNutritional Indicators\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow BMI\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.79\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.64\\u0026ndash;5.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.026\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow HGS\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.53\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.77\\u0026ndash;3.02\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.218\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow CC\\u003csup\\u003ed\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.71\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.88\\u0026ndash;3.31\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.111\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMalnutrition\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSGA (B and C)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e3.60\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.83\\u0026ndash;7.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePG-SGA (B and C)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e2.78\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.01\\u0026ndash;7.67\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.048\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003ctfoot\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e Models were adjusted by age, sex, presence of metastasis, and chronic diseases.\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e Low \\u003cem\\u003eBMI\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;\\u0026lt;\\u0026thinsp;18.5 kg/m\\u0026sup2;) according to the WHO.\\u003csup\\u003e23\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003e\\u003csup\\u003ec\\u003c/sup\\u003e Malnutrition: patients classified as moderately (B) and severely malnourished (C) were grouped.\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003e\\u003csup\\u003ed\\u003c/sup\\u003eLow \\u003cem\\u003eHGS\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;Male (\\u0026lt;\\u0026thinsp;27 kg); Female (\\u0026lt;\\u0026thinsp;16 kg)\\u003csup\\u003e18\\u003c/sup\\u003e; Low \\u003cem\\u003eCC\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;Male (\\u0026le;\\u0026thinsp;34 cm); Female (\\u0026le;\\u0026thinsp;33 cm).\\u003csup\\u003e33\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003eCC\\u003c/em\\u003e values were adjusted by patients\\u0026rsquo; BMI to remove the confounding effects of adiposity.\\u003csup\\u003e32\\u003c/sup\\u003e\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003e* \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; **\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003eAbbreviations: \\u003cem\\u003eOR\\u003c/em\\u003e, Odds Ratio; \\u003cem\\u003eCI\\u003c/em\\u003e, Confidence Interval; \\u003cem\\u003eBMI\\u003c/em\\u003e, Body Mass Index; \\u003cem\\u003eSGA\\u003c/em\\u003e, Subjective\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"4\\\"\\u003eGlobal Assessment; \\u003cem\\u003ePG-SGA\\u003c/em\\u003e, Patient-Generated Subjective Global Assessment\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tfoot\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this study, we showed that individual nutritional indicators (BMI, HGS, and CC) had poor performance (AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.70) and agreement (kappa\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.20) in diagnosing malnutrition compared with SGA and PG-SGA. Low BMI (\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e), however, had adequate PPV and specificity (\\u0026gt;\\u0026thinsp;80%) for the diagnosis of malnutrition. Therefore, the BMI cutoff of 18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e could serve as a complement to nutritional assessment in patients with gastrointestinal or head and neck cancer. Our results also showed a positive association between low BMI and LOS\\u0026thinsp;\\u0026ge;\\u0026thinsp;6 days.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePrevalence of nutritional risk, malnutrition, and nutritional indicators\\u003c/h2\\u003e \\u003cp\\u003eMalnutrition is highly prevalent in hospitalized patients with cancer, varying in degree depending on the location and stage of the tumor [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. In our cohort of patients with gastrointestinal or head and neck cancer, nutritional risk was identified in 72.5% (PG-SGA SF) and malnutrition in 57.3% (SGA) and 87.1% (PG-SGA). Our results are consistent with those of previous studies investigating these two types of cancer, with malnutrition ranging from 43.8\\u0026ndash;86.3% depending on the nutritional assessment tool used [\\u003cspan additionalcitationids=\\\"CR39 CR40\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. In fact, PG-SGA can identify a higher prevalence of malnutrition because it is an oncology-specific tool that assesses symptoms and clinical conditions specific to patients with cancer [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIndividual nutritional indicators (BMI, HGS, and CC) can complement nutritional assessment and have been associated with negative outcomes in patients with cancer [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. In our study, low BMI according to the WHO criteria [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e] was identified in 13.5% of patients, and this group had longer LOS than patients with normal BMI. HGS assesses muscle function and has recently been recommended as a complementary measure in hospitalized patients [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Almost half of our patients (46.2%) had low HGS, which agrees with previous studies in which low HGS was identified in 38% of patients with advanced cancer [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] and in 48% of malnourished patients with cancer [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Also, having low HGS was found to decrease the odds of hospital discharge by 3-fold compared with having high HGS [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. CC is an indicator of muscle mass [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], and low CC was identified in 59.1% of our patients after adjusting these values for BMI [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Likewise, a prevalence of low CC of 56% was reported in patients with gastric and colorectal cancer [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Moreover, in patients with and without cancer, reduced CC values were associated with mortality [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e] and hospital readmission [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAccuracy of each nutritional indicator in diagnosing malnutrition\\u003c/h2\\u003e \\u003cp\\u003eOnly low BMI (\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e) had adequate specificity for the diagnosis of malnutrition, compared with SGA and PG-SGA. In patients with gastric and colorectal cancer, low BMI (compared with SGA) also demonstrated satisfactory specificity [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eLow HGS had a high PPV for the diagnosis of malnutrition compared with PG-SGA. Because malnourished patients with cancer are functionally impaired due to loss of muscle function, low HGS may anticipate the detection of nutritional losses, even before the detection of changes in body composition is possible [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. However, in our study, HGS alone could not diagnose malnutrition. A previous study of patients with cancer showed an association between reduced HGS and malnutrition, which may be explained by the ability of cancer-induced nutritional damage to accelerate the decrease in HGS [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCC is closely related to whole-body muscle mass and offers prognostic value for clinical and oncological outcomes [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. In our study, low CC agreed poorly with SGA and PG-SGA in the diagnosis of malnutrition. Fair agreement has also been found between low CC (\\u0026lt;\\u0026thinsp;31 cm as the cutoff) and PG-SGA in older patients with cancer [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. We highlight that, in the present study, we used cutoffs according to sex [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e] and adjusted CC for BMI, thus avoiding adiposity confounders [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. This adjustment is appropriate because it can minimize classification errors, such as classifying patients with obesity as having normal CC values. For example, in the present study, approximately 40% of patients had high BMI.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation of nutritional indicators and malnutrition with hospitalization\\u003c/h2\\u003e \\u003cp\\u003ePatients with low BMI and CC had longer LOS in our study. These data are consistent with the results of a meta-analysis suggesting a relationship between BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e and LOS in older patients with cancer [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] and a study reporting an association between low CC and prolonged LOS (\\u0026ge;\\u0026thinsp;9 days) in hospitalized adults [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. In the present study, in addition to a positive association between low BMI and LOS\\u0026thinsp;\\u0026ge;\\u0026thinsp;6 days, we found that malnourished patients diagnosed by SGA and PG-SGA were 3.60 and 2.78 times more likely to have prolonged LOS, respectively, than well-nourished patients. In patients with different types of cancer, poorer nutritional status has been associated with prolonged LOS [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImplications for clinical practice\\u003c/h2\\u003e \\u003cp\\u003eBMI, HGS, and CC are widely used in clinical practice, but our findings suggest that these nutritional indicators might inappropriately diagnose malnutrition when used alone in the cancer setting. In patients with gastrointestinal or head and neck cancer, however, BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e may be used in combination with SGA or PG-SGA for nutritional assessment. This information has great utility in the clinical setting because BMI values can differ between patient groups [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. HGS is a measure used to assess muscle function that can complement nutritional assessment and is associated with reduced functional capacity and malnutrition [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], a scenario further complicated in older patients [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Approximately 65% of the patients in our sample were older adults. CC is considered a sensitive anthropometric index to assess muscle mass and an important measure to evaluate loss of muscle mass (20), but the use of BMI-adjusted cutoffs has been suggested [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThis study used PG-SGA, an oncology-specific tool, as a reference to evaluate the diagnostic accuracy of the nutritional indicators. The use of this tool in our population was important because it evaluates signs and symptoms specific to patients with cancer [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. For instance, 56.2% of our patients reported loss of appetite, which directly affects their nutritional status. Also, LOS may negatively affect patients\\u0026rsquo; nutritional status; in the present study, malnourished patients had longer LOS than well-nourished patients regardless of the tool used for diagnosis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLimitations\\u003c/h2\\u003e \\u003cp\\u003eLimitations of this study include heterogeneous ages and cancer stages in our sample, but their effects were minimized by adjusting the logistic regression model for sex, age, chronic disease, and metastasis. However, our data support that malnutrition is highly prevalent among patients with gastrointestinal or head and neck cancer upon hospital admission, making early nutritional assessment even more important to help mitigate poor clinical outcomes, such as prolonged LOS.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eOur findings demonstrated that low BMI (\\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e), low HGS (\\u0026lt;\\u0026thinsp;27 kg for men and \\u0026lt;\\u0026thinsp;16 kg for women), and low CC (\\u0026le;\\u0026thinsp;34 cm for men and \\u0026le;\\u0026thinsp;33 cm for women) were poorly accurate in diagnosing malnutrition in patients with gastrointestinal or head and neck cancer, compared with SGA (general tool) and PG-SGA (oncology-specific tool) as the reference standards. However, because low BMI was found to be an independent predictor of longer LOS in our sample, it may be used as a complement to nutritional assessment performed with SGA and PG-SGA in these cancers.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by the Fundo de Incentivo a Pesquisa of the Hospital de Cl\\u0026iacute;nicas de Porto Alegre, Brazil. This study was also financed in part by the Coordena\\u0026ccedil;\\u0026atilde;o de Aperfei\\u0026ccedil;oamento de Pessoal de N\\u0026iacute;vel Superior - Brasil (CAPES) \\u0026ndash; Finance Code 001 and Funda\\u0026ccedil;\\u0026atilde;o de Amparo \\u0026agrave; Pesquisa do Estado do Rio Grande do Sul (FAPERGS). Mariana S. Crestani and Giovanna P. Stefani received a scholarship from CAPES. Laura M. Scott received a scholarship from Universidade Federal do Rio Grande do Sul.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTS and MSC conceived and designed this study. CHS, MSC, GPS, and LMS contributed to data acquisition. CHS and TS analyzed and interpreted data. CHS, GPS, and TS drafted this manuscript, and all authors critically revised it and approved its final version. TS guarantees and attests that all listed authors meet authorship criteria and that no others who meet these criteria have been omitted.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was prepared in accordance with the Declaration of Helsinki and approved by the Ethics Commission of the Hospital de Cl\\u0026iacute;nicas de Porto Alegre (protocol #2019.0708).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants provided informed consent before data collection.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWaitzberg DL, Caiaffa WT, Correia MI (2001) Hospital malnutrition: the Brazilian national survey (IBRANUTRI): a study of 4000 patients. Nutrition 17(7\\u0026ndash;8):573\\u0026ndash;580. https://doi.org/10.1016/s0899-9007(01)00573-1\\u003c/li\\u003e\\n\\u003cli\\u003eMuscaritoli M, Arends J, Aapro M (2019) From guidelines to clinical practice: a roadmap for oncologists for nutrition therapy for cancer patients. Ther Adv Med Oncol 11. https://doi.org/10.1177/1758835919880084\\u003c/li\\u003e\\n\\u003cli\\u003eReber E, Sch\\u0026ouml;nenberger KA, Vasiloglou MF, Stanga Z (2021) Nutritional Risk Screening in Cancer Patients: The First Step Toward Better Clinical Outcome. Front Nutr 8:603936. https://doi.org/10.3389%2Ffnut.2021.603936 \\u003c/li\\u003e\\n\\u003cli\\u003eRyan AM, Prado CM, Sullivan ES, Power DG, Daly LE (2019) Effects of weight loss and sarcopenia on response to chemotherapy, quality of life, and survival. Nutrition 67\\u0026ndash;68:110539. https://doi.org/10.1016/j.nut.2019.06.020 \\u003c/li\\u003e\\n\\u003cli\\u003eSimon SR, Pilz W, Hoebers FJP, Leeters IPM, Schols AMWJ, Willemsen ACH, Winkens B, Baijens LWJ (2021) Malnutrition screening in head and neck cancer patients with oropharyngeal dysphagia. Clin Nutr ESPEN 44:348\\u0026ndash;355. https://doi.org/10.1016/j.clnesp.2021.05.019\\u003c/li\\u003e\\n\\u003cli\\u003eDeftereos I, Djordjevic A, Carter VM, McManara J, Yeung JM, Kiss N (2021) Malnutrition screening tools in gastrointestinal cancer: A systematic review of concurrent validity. Sur Oncol 38:101627. https://doi.org/10.1016/j.suronc.2021.101627 \\u003c/li\\u003e\\n\\u003cli\\u003eMarshall KM, Loeliger J, Nolte L, Kelaart A, Kiss NK (2019) Prevalence of malnutrition and impact on clinical outcomes in cancer services: A comparison of two-time points. Clin Nutr 38(2):644\\u0026ndash;651. https://doi.org/10.1016/j.clnu.2018.04.007 \\u003c/li\\u003e\\n\\u003cli\\u003eSousa IM, Silva FM, Carvalho ALM, Rocha IMG, Fayh APT (2022) Accuracy of isolated nutrition indicators in diagnosing malnutrition and their prognostic value to predict death in patients with gastric and colorectal cancer: A prospective study. JPEN J Parenter Enteral Nutr 46(3):508\\u0026ndash;516. https://doi.org/10.1002/jpen.2199 \\u003c/li\\u003e\\n\\u003cli\\u003eSchiessel DL, Orrut\\u0026eacute;a AKG, Silva SE, Cavagnari MAV, Mazur CE, Gavarrete DD, Antunes LBB (2020) Weight loss in cancer patients: prevalence and prognosis related to sex, age, tumor site and nutritional impact symptoms. 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Age Ageing 48(1):16\\u0026ndash;31. https://doi.org/10.1093/ageing/afy169\\u003c/li\\u003e\\n\\u003cli\\u003eLeandro-Merhi VA, de Aquino JLB, Reis LO (2017) Predictors of Nutritional Risk According to NRS-2002 and Calf Circumference in Hospitalized Older Adults with Neoplasms. Nutr Cancer 69(8):1219\\u0026ndash;1226. https://doi.org/10.1080/01635581.2017.1367942\\u003c/li\\u003e\\n\\u003cli\\u003eReal GG, Fr\\u0026uuml;hauf IR, Sedrez JHK, Dall\\u0026apos;Aqua EJF, Gonzalez MC (2018) Calf Circumference: A Marker of Muscle Mass as a Predictor of Hospital Readmission. JPEN J Parenter Enteral Nutr. 42(8):1272\\u0026ndash;1279. https://doi.org/10.1002/jpen.1170\\u003cu\\u003e \\u003c/u\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003eSousa IM, Bielemann RM, Gonzalez MC, da Rocha IMG, Barbalho ER, de Carvalho ALM, Dantas MAM, Medeiros GOC, Silva FM, Fayh APT (2020) Low calf circumference is an independent predictor of mortality in cancer patients: a prospective cohort study. 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Eur J Clin Nutr 74(11):1519\\u0026ndash;1535. https://doi.org/10.1038/s41430-020-0629-0 \\u003c/li\\u003e\\n\\u003cli\\u003eHobday S, Armache M, Paquin R, Nurimba M, Baddour K, Linder D, Kouame G, Kharrington S, Albergotti WG, Mady LJ (2023) The Body Mass Index Paradox in Head and Neck Cancer: A Systematic Review and Meta-Analysis. Nutr Cancer 75(1):48\\u0026ndash;60. https://doi.org/10.1080/01635581.2022.2102659\\u003c/li\\u003e\\n\\u003cli\\u003eWei J, Jiao J, Chen CL, Tao WY, Ying YJ, Zhang WW, Wu XJ, Zhang XM (2022) The association between low calf circumference and mortality: a systematic review and meta-analysis. Eur Geriatr Med 13(3):597\\u0026ndash;609. https://doi.org/10.1007/s41999-021-00603-3 \\u003c/li\\u003e\\n\\u003cli\\u003eSchl\\u0026uuml;ssel MM, dos Anjos LA, de Vasconcellos MTL, Kac G (2008) Reference values of handgrip dynamometry of healthy adults: a population-based study. Clin Nutr 27(4):601\\u0026ndash;607. https://doi.org/10.1016/j.clnu.2008.04.004\\u003c/li\\u003e\\n\\u003cli\\u003eKilgour RD, Viago A, Trutschnigg B, Lucar E, Borod M, Morais JA (2013) Handgrip strength predicts survival and is associated with markers of clinical and functional outcomes in advanced cancer patients. Support Care Cancer 21(12):3261\\u0026ndash;70. https://doi.org/10.1007/s00520-013-1894-4\\u003c/li\\u003e\\n\\u003cli\\u003eCrestani MS, Stefani GP, Scott LM, Steemburgo T (2023) Accuracy of the GLIM Criteria and SGA Compared to PG-SGA for the Diagnosis of Malnutrition and Its Impact on Prolonged Hospitalization: A Prospective Study in Patients with Cancer. Nutr Cancer 75(4):1177\\u0026ndash;1188. https://doi.org/10.1080/01635581.2023.2184748\\u003c/li\\u003e\\n\\u003cli\\u003eJager-Wittenaar H, Ottery FD (2017) Assessing nutritional status in cancer: role of the Patient-Generated Subjective Global Assessment. 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J Cachexia Sarcopenia Muscle 7(2):136\\u0026ndash;143. https://dx.doi.org/10.1002%2Fjcsm.12049\\u003c/li\\u003e\\n\\u003cli\\u003eMetz CE (1978) Basic principles of ROC analysis. Semin Nucl Med 8(4):283\\u0026ndash;298. https://doi.org/10.1016/S0001-2998(78)80014-2\\u003c/li\\u003e\\n\\u003cli\\u003ede van der Schueren MAE, Keller H, GLIM Consortium, Cederholm T, Barazzoni R, Compher C, Correia MITD, Gonzalez MC, Jager-Wittenaar H, Pirlich M (2020) Global Leadership Initiative on Malnutrition (GLIM): Guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults. Clin Nutr 39(9):2872\\u0026ndash;2880. https://doi.org/10.1016/j.clnu.2019.12.022\\u003c/li\\u003e\\n\\u003cli\\u003eLandis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159\\u0026ndash;174. https://pubmed.ncbi.nlm.nih.gov/843571/ \\u003c/li\\u003e\\n\\u003cli\\u003eWie GA, Cho YA, Kim SY, Kim SM, Bae JM, Joung H (2010) Prevalence and risk factors of malnutrition among cancer patients according to tumor location and stage in the National Cancer Center in Korea. Nutrition 26(3):263\\u0026ndash;268. https://doi.org/10.1016/j.nut.2009.04.013\\u003c/li\\u003e\\n\\u003cli\\u003eMartin L, Findlay M, Bauer JD, Dhaliwal R, de van der Schueren M, Laviano A, Widaman A, Baracos VE, Day AG, Gramlich LM (2022) Multi-Site, International Audit of Malnutrition Risk and Energy and Protein Intakes in Patients Undergoing Treatment for Head Neck and Esophageal Cancer: Results from INFORM. Nutrients 14(24):5272. https://doi.org/10.3390/nu14245272 \\u003c/li\\u003e\\n\\u003cli\\u003eArribas L, Hurt\\u0026oacute;s L, Mil\\u0026agrave; R, Fort E, Peir\\u0026oacute; I (2013) Predict factors associated with malnutrition from patient-generated subjective global assessment (PG-SGA) in head and neck cancer. Nutr Hosp 28(1):155\\u0026ndash;163. https://doi.org/10.3305/nh.2013.28.1.6168 \\u003c/li\\u003e\\n\\u003cli\\u003eStefani GP, Crestani MS, Scott LM, Soares CH, Steemburgo T (2023) Complementarity of nutritional assessment tools to predict prolonged hospital stay and readmission in older patients with solid tumors: a secondary analysis of a cohort study. Nutrition 113:112089. https://doi.org/10.1016/j.nut.2023.112089\\u003c/li\\u003e\\n\\u003cli\\u003eGama RR, Song Y, Zhang Q, Brown MC, Wang J, Habbous S, Tong L, Huang SH, O\\u0026rsquo;Sulilivan B, Waldron J et al (2017) Body mass index and prognosis in patients with head and neck cancer. Head Neck 39(6):1226\\u0026ndash;1233. https://doi.org/10.1002/hed.24760 \\u003c/li\\u003e\\n\\u003cli\\u003eTarnowski M, Stein E, Marcadenti A, Fink J, Rabito E, Silva FM (2020) Calf Circumference Is a Good Predictor of Longer Hospital Stay and Nutritional Risk in Emergency Patients: A Prospective Cohort Study. J Am Coll Nutr 39(7):645\\u0026ndash;649. https://doi.org/10.1080/07315724.2020.1723452\\u003c/li\\u003e\\n\\u003cli\\u003eWang WJ, Li TT, Wang X, Li W, Cui JW (2020) Combining the Patient-Generated Subjective Global Assessment (PG-SGA) and Objective Nutrition Assessment Parameters Better Predicts Malnutrition in Elderly Patients with Colorectal Cancer. J Nutr Oncol 5(1):22\\u0026ndash;30. https://doi.org/10.34175/jno202001003 \\u003c/li\\u003e\\n\\u003cli\\u003eNorman K, Stob\\u0026auml;us N, Smoliner C, Zocher D, Scheufele R, Valentini L, Lochs H, Pirlich M (2010) Determinants of hand grip strength, knee extension strength and functional status in cancer patients. Clin Nutr 29(5):586\\u0026ndash;591. https://doi.org/10.1016/j.clnu.2010.02.007\\u003c/li\\u003e\\n\\u003cli\\u003eHu CL, Yu M, Yuan KT, Yu HL, Shi YY, Yang JJ, Li W, Jiang H, Li Z, Xu H (2018) Determinants and nutritional assessment value of hand grip strength in patients hospitalized with cancer. Asia Pac J Clin Nutr 27(4):777\\u0026ndash;784. https://doi.org/10.6133/apjcn.072017.04\\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\":\"info@researchsquare.com\",\"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\":\"Cancer, Anthropometry, Nutritional Status, Malnutrition, Length of Hospital Stay\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3849041/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3849041/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e \\u003cp\\u003eFew studies have evaluated the individual performance of the nutritional indicators body mass index (BMI), calf circumference (CC), and handgrip strength (HGS) for the diagnosis of malnutrition in the cancer setting. We aimed to evaluate the ability of these nutritional indicators to accurately diagnose malnutrition and their association with hospital length of stay (LOS) in patients with cancer.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis cohort study prospectively evaluated 171 patients with gastrointestinal or head and neck cancer. Nutritional status was assessed within 48 hours of hospital admission using BMI, CC, and HGS as well as 2 reference standards: Subjective Global Assessment (SGA) and Patient-Generated SGA (PG-SGA). The accuracy of each nutritional indicator was measured by the area under the receiver operating characteristic curve (AUC), compared with the reference standards. Multiple logistic regression analysis, adjusted for confounders, was used to determine whether malnutrition was associated with LOS.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eOf 171 patients, 59.1% had low CC, 46.2% had low HGS, and 13.5% had low BMI. The SGA and PG-SGA scores indicated malnutrition in 57.3% and 87.1% of patients, respectively. All nutritional indicators had poor accuracy in diagnosing malnutrition (AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.70). However, compared with SGA and PG-SGA, low BMI had satisfactory specificity (\\u0026gt;\\u0026thinsp;80%) and was associated with 1.79 times higher odds of LOS\\u0026thinsp;\\u0026ge;\\u0026thinsp;6 days. Malnutrition diagnosed by SGA and PG-SGA increased the odds of LOS\\u0026thinsp;\\u0026ge;\\u0026thinsp;6 days by 3.60-fold and 2.78-fold, respectively.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eLow BMI showed adequate specificity for diagnosing malnutrition and was associated with longer LOS in patients with gastrointestinal or head and neck cancer.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Low BMI Demonstrates Satisfactory Specificity for Diagnosing Malnutrition and is Associated with Longer Hospitalization in Patients with Gastrointestinal or Head and Neck Cancer: A Prospective Cohort Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-12 17:03:55\",\"doi\":\"10.21203/rs.3.rs-3849041/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"bc5ee5c2-909f-4766-9c4c-11cc90db7e6a\",\"owner\":[],\"postedDate\":\"January 12th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-05-31T06:12:12+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-01-12 17:03:55\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3849041\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3849041\",\"identity\":\"rs-3849041\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}