Which Tool Tells the Truth? A Diagnostic Accuracy Study of Malnutrition Screening in Older Hospitalized Adults Using GLIM as the Gold Standard

Which Tool Tells the Truth? 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A Diagnostic Accuracy Study of Malnutrition Screening in Older Hospitalized Adults Using GLIM as the Gold Standard Sevinj Sadigova, Sibel Akın, Neziha Özlem Deveci, Kamil Deveci This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6770752/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 Aim To analyze the prevalence of malnutrition in hospitalized older patients and to assess the efficacy of the most frequently utilized nutritional screening instruments in identifying individuals at risk of malnutrition. Methods Methods: A prospective cross-sectional study was conducted on 202 older inpatients (mean age: 70.6 years; 60.9% female) in internal medicine wards. Within 48 hours of admission, patients were screened using NRS-2002, MNA-SF, MUST, and ESPEN criteria. The GLIM framework was used as the reference standard. Sensitivity, specificity, predictive values, and Cohen’s kappa were calculated. Results Based on GLIM, 33.2% (n = 67) of patients were malnourished. These patients were older (72.8 ± 7.1 vs. 69 ± 7 years, p < 0.01) and had lower BMI (24.8 vs. 30 kg/m², p < 0.01). They also had reduced serum albumin and hemoglobin levels (p = 0.013 and p = 0.02). The prevalence of malnutrition risk was: MUST (41.6%), MNA-SF (38.4%), NRS-2002 (25.7%), and ESPEN (25.4%). MNA-SF and MUST had the highest sensitivity (94%), while ESPEN showed perfect specificity (100%) but low sensitivity (25.4%). MNA-SF had the strongest agreement with GLIM (κ = 0.700). Conclusion The MNA-SF and MUST exhibited the greatest sensitivity, while ESPEN displayed the best specificity but possessed the lowest sensitivity. The MNA-SF had the highest overall concordance with GLIM, as evidenced by Cohen’s kappa value reflecting considerable agreement. GLIM Nutritional assessment Nutrition screening tools Malnutrition Older adults Hospital Introduction Malnutrition, which impacts 20–50% of patients in hospitals and varies based on the population, underlying health issues, and tests used, greatly affects health and quality of life, especially in older adults. 1 – 6 It can happen because of medical treatments, long periods without food for tests, side effects of drugs, not enough nutritional support, and complicated changes in the body related to inflammation that disrupt normal nutrient use and increase the breakdown of body tissues and metabolism. 6 – 8 Hospital malnutrition can lead to marked weight loss, muscle wasting, and impaired immune function, thereby prolonging hospital stays and elevating the risk of complications, morbidity, and mortality. 9 Effectively addressing hospital malnutrition necessitates a comprehensive approach, including early screening, personalized nutritional interventions, and interdisciplinary collaboration to ensure optimal patient recovery. Despite its high prevalence and well-documented adverse health outcomes, malnutrition and the risk of it among hospitalized older adults remain underrecognized and inadequately managed. Although routine nutritional screening is recommended, only 10–20% of hospitalized patients—even in centers with established clinical nutrition departments—undergo such assessments. 10 , 11 This gap may stem from insufficient awareness regarding the validity and reliability of screening tools, uncertainty in selecting the most appropriate instrument, and the lack of standardized protocols for screening at hospital admission. 12 Malnutrition screening is considered the essential first step in nutritional care, as it enables early identification and timely intervention. An ideal screening tool should be quick and easy to administer while accurately identifying patients at risk and thereby facilitating the efficient allocation of resources for further nutritional evaluation. Such a tool should detect all malnourished individuals without incorrectly classifying well-nourished patients as at risk. Hospitalized and geriatric populations have recommended several validated screening instruments for use. However, one of the primary challenges in validating these tools is the absence of a universally accepted objective criterion—or “gold standard”—for diagnosing malnutrition. 13 Some of the most commonly used and trusted tools are the Nutrition Risk Screening (NRS 2002) 14 and the Mini Nutritional Assessment–Short Form (MNA-SF), 15 which use several signs to help diagnose nutrition issues and plan further actions. Clinical trials have also used these tools as outcome measures, potentially predicting the health status of hospitalized older adults. In 2019, we established the Global Leadership Initiative on Malnutrition (GLIM) to promote standardization in clinical practice. 16 The GLIM framework brings together signs of malnutrition, such as unexpected weight loss, low body mass index (BMI), and muscle loss, with causes such as not eating enough, problems with digestion, inflammation, and illness, to create a shared method for diagnosing malnutrition. A recent study that looked at 22 approved screening tools found that 28.0% of older adults in hospitals in Europe are at risk of protein-energy malnutrition, and a similar rate of 27.7% was seen in hospitals in Turkey. 17 The study was conducted in a region with a population of approximately 1.46 million and three hospitals with a total capacity of 3,688 beds. Older adults comprise about 10% of the population, and the area has been identified as having a high risk for malnutrition. The population primarily consists of individuals from middle- and low-income backgrounds, and traditional dietary habits—often rich in carbohydrates due to cultural and economic factors—may further contribute to nutritional inadequacy. Given the available data, it is necessary to conduct studies to assess the accuracy of various screening tools within a specific population. This study aims to find out how common malnutrition is among older adults in the hospital by using different nutritional screening tools (MNA-SF, NRS-2002, MUST, and ESPEN) and to see how closely they align with the GLIM criteria. The study also wants to see how well these tools can find malnutrition, aiming to identify the best and most trustworthy tool for treating this at-risk group. Materials and Methods This prospective cross-sectional study included adult patients (age > 60 years) admitted to the internal medicine wards at Erciyes University Hospital (Kayseri, Turkey) from August 2024 to November 2024. A solitary qualified internal medicine physician offered all newly admitted patients the opportunity to participate, and those who consented and gave written informed consent within 48 hours of admission were included. The Medical Research Ethics Committee of Erciyes University's Medical School approved the study. Patients were excluded if they were unable to communicate, had eating disorders, had cancer, experienced severe cognitive impairment or delirium, or were receiving end-of-life palliative care. We gathered sociodemographic and medical information during a structured interview, along with the results of other questionnaires. The data encompassed admission diagnoses, health-related behaviors, and sociodemographic factors (gender, date of birth, reason for hospitalization, and medications utilized). Within 48 hours of admission, we conducted nutritional screening using the following instruments: ESPEN, GLIM, MUST, NRS-2002, and MNA-SF. The ESPEN recommendations endorse the NRS-2002 as a nutritional screening instrument for assessing the nutritional status of hospitalized patients. More than just checking for malnutrition, the NRS-2002 has proven its usefulness. You can also use it to identify patients who would benefit from various forms of nutritional support. The overall NRS-2002 score varies from 0 to 7 points. The patient is initially evaluated for a low BMI (< 20.5 kg/m²), weight loss during the past three months, decreased food consumption in the preceding week, and the existence of a severe illness. A patient with one or more of these conditions undergoes screening. Patients receive a nutritional score (0–3 points) during the screening phase, which is based on their BMI, weight loss, and reduced food intake. They are also given a disease severity score (0–3 points) based on their current clinical condition and chronic diseases with acute complications (for example, a cerebrovascular event, traumatic brain injury, major abdominal surgery, or bone marrow transplant); patients aged ≥ 70 years receive an extra point. An NRS-2002 score below three implies no malnutrition risk, while a score of three or more signifies a risk of malnutrition. 14 The MNA-SF is an abbreviation for the MNA and is a commonly utilized screening tool specifically developed for older populations. This instrument comprises six questions, with responses evaluated on a scale of 0–2 or 0–3. These inquiries evaluate weight reduction over the past three months, appetite levels, physical mobility, psychological stress, neuropsychological issues, and body mass index (BMI). We classify patients into three categories based on their total score: "normal nutritional status" (12–14 points), "nutritional risk" (8–11 points), or "malnourishment" (0–7 points). We categorize the patient as "at risk for malnutrition" if their overall score is less than 11 points. 15 The GLIM diagnostic criteria for malnutrition served as the benchmark norm. Three phenotypic criteria and two etiological criteria comprise the GLIM criteria. At least one phenotypic criterion and one etiological criterion must be present to diagnose malnutrition. A person must have lost at least 5% of their body weight in the past six months or at least 10% of their body weight in seven or more months, have a low body mass index (< 20 kg/m² for people younger than 70 years or < 22 kg/m² for people older than 70 years), and have less muscle mass, as shown by a valid body composition analysis, such as bioelectrical impedance analysis (BIA). The causes are less food intake or assimilation (eating less than 50% of your daily nutritional needs for one to two weeks, or any reduction lasting longer than two weeks, or any long-term gastrointestinal condition that makes it difficult to assimilate or absorb food) and inflammation and disease burden from an injury or illness, whether it's short-term or long-term. Malnutrition severity is classified as moderate or severe, determined by factors like the extent of unintended weight loss, BMI, and diminished muscle mass. 16 The MUST is a proven, fast, and reproducible screening instrument designed for all adult patients across various healthcare environments. It takes into account BMI, unintentional weight loss, and the likelihood of future weight loss resulting from acute illnesses that may hinder food consumption for over five days. Each criterion is assigned a score ranging from 0 to 2 points. The Body Mass Index (BMI) was categorized as 0 for values exceeding 20 kg/m², 1 for values between 18.5 and 20 kg/m², and 2 for values below 18.5 kg/m². We classified weight loss as 0 for less than 5%, 1 for 5–10%, and 2 for greater than 10%. The presence of acute disease and its possible effect on food intake during the following five days was scored as 0 if absent and 2 if present. We classified the patients' malnutrition risk as low (0), medium (1), or high (2). 18 The European Society for Clinical Nutrition and Metabolism (ESPEN) defined malnutrition as having one or more of these signs: a low body mass index (BMI); losing weight without trying while also having a lower BMI; and losing weight without trying, having a lower BMI, and a low fat-free mass index (FFMI). 19 Body weight and height were recorded at recruitment, specifically upon admittance, utilizing a calibrated weighing scale and a wall-mounted stadiometer, accurate to the closest 0.5 kg and 0.5 cm, respectively. We calculated BMI by dividing the weight (in kilos) by the square of the height (in meters). We evaluated frailty utilizing the FRAIL scale. The FRAIL scale is a commonly utilized instrument for evaluating frailty, which denotes a condition of susceptibility to negative health consequences. It consists of five elements: tiredness, resistance, ambulation, sickness, and weight loss. The scale spans from 0 to 5 points, allocating one point for each component, where 0 signifies optimal health status and 5 denotes the most adverse condition. A score of 3–5 points categorizes an individual as "frail," 1–2 points as "pre-frail," and 0 points as "non-frail." 20 The Turkish adaptation of the Mini-Mental State Examination (MMSE) evaluated cognitive function. 21 Cognitive impairment was defined as an MMSE score of less than 24/30 for the illiterate and less than 25/30 for the literate. We used the Geriatric Depression Scale (GDS) to evaluate depressive mood, setting a cut-off score of 14 for the Turkish version. 22 Results A total of 202 hospitalized older adults were included in the study, with a median age of 70.6 years (IQR: 64–76), and 60.9% (n = 123) were female. The mean BMI was 28.8 ± 5.9 kg/m². The median length of hospital stay was 8 days (IQR: 6–13). Comorbidities were prevalent, with hypertension (73.3%), diabetes mellitus (61.9%), and chronic kidney disease (22.3%) being the most common. We observed frailty, cognitive impairment, and depressive symptoms in 76.7%, 15.8%, and 10% of the participants, respectively. According to the GLIM criteria, 33.2% (n = 67) of patients were diagnosed with malnutrition. Malnourished patients were significantly older (72.8 ± 7.1 vs. 69 ± 7 years, p < 0.01) and had lower BMI values (24.8 vs. 30 kg/m², p < 0.01) compared to their well-nourished counterparts. Serum albumin and hemoglobin levels were also significantly lower in the malnourished group ( p = 0.013 and p = 0.02, respectively). Frailty was significantly more prevalent among malnourished patients compared to well-nourished individuals (86.6% vs. 71%, p = 0.02). Cognitive impairment was also more common in the malnourished group (26.9% vs. 10%, p < 0.01), while the presence of depressive symptoms did not differ significantly ( p = 0.13). No significant differences were found between groups regarding hospital stay duration or number of comorbidities. The prevalence of malnutrition risk according to screening tools was as follows: MNA-SF (38.4%), NRS-2002 (25.7%), MUST (41.6%), and ESPEN (25.4%). When evaluated against GLIM-defined malnutrition, MNA-SF and MUST demonstrated the highest sensitivity (both 94%), while ESPEN showed the highest specificity (100%) but had the lowest sensitivity (25.4%). The MNA-SF also had the highest overall agreement with GLIM, with a Cohen’s kappa value of 0.700, indicating substantial agreement. The diagnostic performance of each tool was assessed by ROC analysis. The area under the curve (AUC) was highest for MUST (AUC = 0.973), followed by NRS-2002 (AUC = 0.948), MNA-SF (AUC = 0.908), and ESPEN (AUC = 0.627). The MNA-SF had the highest negative predictive value (96.5%) and the lowest negative likelihood ratio (0.07), making it a reliable tool for ruling out malnutrition in this population. Discussion This study looked at how common malnutrition is among older patients in the hospital using the GLIM criteria and evaluated how well four popular nutritional screening tools—MNA-SF, MUST, NRS-2002, and ESPEN—work. The GLIM framework classified one-third (33.2%) of the study cohort as malnourished. This data matches earlier studies that found hospitalized patients diagnosed with malnutrition using GLIM; the prevalence ranged from 18.9–80.0%. The differences in prevalence rates may be due to whether a nutritional screening was done, which tool was used, and how the etiological criterion of "presence of inflammation" and the phenotypic criterion of "low muscle mass" were evaluated. 23 – 26 The biggest limitation in validating the effectiveness of malnutrition screening tools is the absence of a single objective measure or "gold standard" for diagnosing malnutrition. 27 GLIM is a prevalent and validated nutritional assessment instrument that utilizes various indicators to identify malnutrition and commence treatment. Clinical studies have endorsed it, and it forecasts health outcomes in hospitalized older individuals. 28 In our study, unlike previous research, we did not use any approved screening tool at the beginning to find people at risk of malnutrition before applying the GLIM criteria to diagnose malnutrition. Van Dronkelaar et al. found that the prevalence of malnutrition according to the GLIM criteria without prior screening was 42% in their study, whereas in our study, this rate was lower (33.2%). In this study, unlike ours, there were patients from the acute admission ward, neurology, and geriatrics departments, and the number of patients was higher than in our study. 29 Brito et al. evaluated all patients using the GLIM criteria without conducting a prior nutritional risk screening, as our study did. The GLIM criteria are said to be a viable option for diagnosing malnutrition in hospitalized patients, as they independently affect the length of stay, the risk of dying in the hospital, and the risk of dying within six months of discharge. 28 The ESPEN criteria demonstrated perfect specificity (100%) but exhibited the lowest sensitivity (25.4%) and the least concordance with GLIM. The ESPEN screening tool has high PPVs, meaning most patients identified as malnourished by it are also defined as malnourished by GLIM criteria. However, the low sensitivity (25.4%) suggests that ESPEN does not identify half of the malnourished patients. The results show that using ESPEN alone may miss many malnourished patients, making it a poor choice for screening in this case. This outcome aligns with the conclusions of prior research. 30 As in our study, according to the GLIM criteria, half of the geriatric rehabilitation patients were malnourished, while the prevalence was much lower when the ESPEN definition was applied. The GLIM criteria indicate that 52.0% of the participants in this study are malnourished overall. The ESPEN definition diagnosed malnutrition in 12% of the patients. The difference between the diagnostic tools is clear because ESPEN requires weight loss to be linked to either a low BMI or an FFMI malnutrition diagnosis, while the GLIM criteria only need one of these conditions. GLIM's incorporation of etiological criteria elucidates the increased prevalence relative to ESPEN. 31 Ren et al. discovered a higher prevalence of malnutrition among older patients in the hospital, based on both the SGA scale and the ESPEN 2015 criteria, as reported by GLIM. Based on the GLIM standards, 37.9% were identified as malnourished; according to the SGA criteria, 32.8% were classified as malnourished; and per the ESPEN 2015 criteria, 17.0% were deemed malnourished. 32 The MNA-SF and MUST screening methods exhibited the highest sensitivity, both at 94%, highlighting their efficacy in detecting patients at risk for malnutrition. In previous studies, MNA-SF was not as specific as nutritional assessments or expert evaluations of nutritional status in hospitalized older adults, which is not the case in our study. 33 , 34 Neelemaat and colleagues demonstrated that the MST, MUST, NRS-2002, and SNAQ are effective tools for assessing malnutrition in hospitalized patients. However, they found the MNA-SF to be inadequate for use in hospitalized older adults, as it failed to reliably identify individuals with nutritional deficiencies. 34 Slee and colleagues found a significant difference between MUST and MNA-SF scores in frail older patients, raising concerns about whether MUST is the best tool for assessing malnutrition risk in this group. They contended that the failure to utilize the MUST as the benchmark for evaluating malnutrition risk in frail older inpatients was a significant issue. 35 All hospitals and care facilities employ the MUST. The MUST establishes a malnutrition risk score based on the current body mass index (BMI), documented weight loss, and the occurrence of acute illness or lack of nutritional intake for five days. The MNA-SF poses identical inquiries to the MUST, supplemented by additional questions regarding neuropsychological functional state, physical mobility, and dietary intake. The MNA employs a more elevated grading scale for BMI in comparison to the MUST. The participants' BMI in this study was primarily among the normal and overweight categories. In another study, researchers compared different tools like MUST, SGA, and NRS-2002 for managing nutrition in older patients in the hospital, and they found that the GLIM framework showed a higher malnutrition rate (46%) than what we found in our study. Compared to MUST, SGA, and NRS-2002, the authors found that MUST had the highest concordance with GLIM, which is consistent with our study. They suggested that we MUST determine the nutritional status of older patients upon hospital admission and then conduct a GLIM assessment. 36 In addition to nutritional status, our findings revealed significant correlations between malnutrition and frailty, cognitive decline, and diminished blood albumin concentrations. Frailty was much more common in malnourished patients, supporting the growing evidence that malnutrition and frailty often occur together and can affect each other. 37 – 38 Cognitive impairment was notably more prevalent in malnourished patients, aligning with other research that highlights the adverse impact of inadequate nutrition on cognitive performance. 39 – 40 In contrast to the literature, although depressive symptoms were more common in the group, the difference lacked statistical significance. 41 This difference might be due to the small number of patients with high GDS scores or the fact that symptoms of malnutrition and depression, like tiredness and weight loss, can make it hard to assess. This study has several important aspects, such as its forward-looking design, the use of the new GLIM criteria for diagnosis, and the comparison of different screening methods. However, we must acknowledge certain limitations. A single center conducted the investigation, potentially limiting its generalizability. Survey participation is voluntary, and written informed consent is required from all patients, which may lead to selection bias and exclude patients in poor health. These patients are at a heightened risk for malnutrition and would thus be particularly relevant to the study. The omission of SGA in our investigation may be considered a drawback, despite its prevalent usage in most studies evaluating screening techniques. Since its introduction, many groups have extensively utilized SGA, verified it, and linked it to clinical results. A further drawback is that the cross-sectional nature of the current investigation precludes the establishment of causal relationships. In conclusion, there is significant variation in malnutrition prevalence among hospitals, primarily attributable to the differing diagnostic equipment employed. The alignment with prior studies underscores the dependability of the GLIM criteria in detecting malnutrition in hospitalized older patients. There was a strong agreement between the GLIM and MNA-SF (k = 0.70), but there was little to no agreement among MUST, NRS-2002, and ESPEN (κ = 0.17, κ = 0.13, κ = 0.05); this difference shows that these nutritional assessments find different groups of people at risk. We emphasize the importance of finding out how common malnutrition is by using thorough nutritional evaluations instead of just screening tools, which often mistakenly identify too many people as malnourished because they are not very accurate. Healthcare personnel may overlook malnutrition in the older adults if they do not conduct nutritional screening. Neglecting to recognize malnutrition will result in inadequate treatment, which can have serious health implications. We must conduct a comprehensive nutritional examination to ascertain malnutrition or its risk. Nonetheless, doing such an exhaustive evaluation on all patients in a hospital environment is impractical due to temporal and cost limitations. It emphasizes the necessity of employing standardized instruments for precise nutritional evaluations in hospital environments. Table 4 Statistical assesment of nutritional screening techniques in comparison to the diagnostic criteria for malnutrition: NRS 2002, MUST, MNA-SF, ESPEN NRS- 2002 MUST ESPEN MNA-SF Sensitivity(%) 77.6 94 25.4 94 Specificity(%) 91.9 54 100 81.5 Positive predictive value(%) 82.5 50 100 71.6 Negative predictive value(%) 89.2 94 73 96.5 Positive Likelihood ratio(LR+) 9.7 2 - 5 Negative likelihood ratio(LR -) 0.24 0.11 0.74 0.07 Κ value (p) 0.172 0.134 0.058 0.700 AUC 0.948 0,973 0,627 0,908 Declarations Author Contribution S.S: Data collection, interpretation, manuscript draftingS.A: Study design, Conceptualization, supervision, manuscript review, correspondence, manuscript drafting, literature searchN.Ö.D: Critical manuscript revision, literature search, manuscript draftingK.D: Data analysis, patient recruitment, data acquisition, clinical interpretation, manuscript draftingAll authors reviewed the manuscript References Kaiser MJ, Bauer JM, Rämsch C, et al. 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JPEN J Parenter Enteral Nutr. 2020;44(8):1492-1500. doi:10.1002/jpen.1781 Alves LF, de Jesus JDS, Britto VNM, de Jesus SA, Santos GS, de Oliveira CC. GLIM criteria to identify malnutrition in patients in hospital settings: A systematic review. JPEN J Parenter Enteral Nutr. 2023;47(6):702-709. doi:10.1002/jpen.2533 Hirose S, Matsue Y, Kamiya K, et al. Prevalence and prognostic implications of malnutrition as defined by GLIM criteria in elderly patients with heart failure. Clin Nutr. 2021;40(6):4334-4340. doi:10.1016/j.clnu.2021.01.014 Meijers JM, van Bokhorst-de van der Schueren MA, Schols JM, Soeters PB, Halfens RJ. Defining malnutrition: mission or mission impossible?. Nutrition. 2010;26(4):432-440. doi:10.1016/j.nut.2009.06.012 Brito JE, Burgel CF, Lima J, et al. GLIM criteria for malnutrition diagnosis of hospitalized patients presents satisfactory criterion validity: A prospective cohort study. Clin Nutr. 2021;40(6):4366-4372. doi:10.1016/j.clnu.2021.01.009 van Dronkelaar C, Tieland M, Cederholm T, Reijnierse EM, Weijs PJM, Kruizenga H. Malnutrition Screening Tools Are Not Sensitive Enough to Identify Older Hospital Patients with Malnutrition. Nutrients. 2023;15(24):5126. Published 2023 Dec 17. doi:10.3390/nu15245126 Cortes R, Yañez AM, Capitán-Moyano L, Millán-Pons A, Bennasar-Veny M. Evaluation of different screening tools for detection of malnutrition in hospitalised patients. J Clin Nurs. 2024;33(12):4759-4771. doi:10.1111/jocn.17170 Clark AB, Reijnierse EM, Lim WK, Maier AB. Prevalence of malnutrition comparing the GLIM criteria, ESPEN definition and MST malnutrition risk in geriatric rehabilitation patients: RESORT. Clin Nutr. 2020;39(11):3504-3511. doi:10.1016/j.clnu.2020.03.015 Ren SS, Zhu MW, Zhang KW, et al. Machine Learning-Based Prediction of In-Hospital Complications in Elderly Patients Using GLIM-, SGA-, and ESPEN 2015-Diagnosed Malnutrition as a Factor. Nutrients. 2022;14(15):3035. Published 2022 Jul 24. doi:10.3390/nu14153035 Ranhoff AH, Gjøen AU, Mowé M. Screening for malnutrition in elderly acute medical patients: the usefulness of MNA-SF. J Nutr Health Aging. 2005;9(4):221-225. Neelemaat F, Meijers J, Kruizenga H, van Ballegooijen H, van Bokhorst-de van der Schueren M. Comparison of five malnutrition screening tools in one hospital inpatient sample. J Clin Nurs. 2011;20(15-16):2144-2152. doi:10.1111/j.1365-2702.2010.03667.x Slee A, Birch D, Stokoe D. A comparison of the malnutrition screening tools, MUST, MNA and bioelectrical impedance assessment in frail older hospital patients. Clin Nutr. 2015;34(2):296-301. doi:10.1016/j.clnu.2014.04.013 Bellanti F, Lo Buglio A, Quiete S, et al. Comparison of Three Nutritional Screening Tools with the New Glim Criteria for Malnutrition and Association with Sarcopenia in Hospitalized Older Patients. J Clin Med. 2020;9(6):1898. Published 2020 Jun 17. doi:10.3390/jcm9061898 Dent E, Hoogendijk EO. Psychosocial factors modify the association of frailty with adverse outcomes: a prospective study of hospitalised older people. BMC Geriatr. 2014;14:108. Published 2014 Sep 28. doi:10.1186/1471-2318-14-108 Gingrich A, Volkert D, Kiesswetter E, et al. Prevalence and overlap of sarcopenia, frailty, cachexia and malnutrition in older medical inpatients. BMC Geriatr. 2019;19(1):120. Published 2019 Apr 27. doi:10.1186/s12877-019-1115-1 Orsitto G, Fulvio F, Tria D, Turi V, Venezia A, Manca C. Nutritional status in hospitalized elderly patients with mild cognitive impairment. Clin Nutr. 2009;28(1):100-102. doi:10.1016/j.clnu.2008.12.001 Yu W, Yu W, Liu X, et al. Associations between malnutrition and cognitive impairment in an elderly Chinese population: an analysis based on a 7-year database. Psychogeriatrics. 2021;21(1):80-88. doi:10.1111/psyg.12631 German L, Feldblum I, Bilenko N, Castel H, Harman-Boehm I, Shahar DR. Depressive symptoms and risk for malnutrition among hospitalized elderly people. J Nutr Health Aging. 2008;12(5):313-318. doi:10.1007/BF02982661 Table 1 to 3 Table 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1to3.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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6770752","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470112340,"identity":"6ccaa358-299f-4a0e-a4ce-7db857d07996","order_by":0,"name":"Sevinj Sadigova","email":"","orcid":"","institution":"Erciyes Üniversitesi Tıp Fakültesi Hastaneleri","correspondingAuthor":false,"prefix":"","firstName":"Sevinj","middleName":"","lastName":"Sadigova","suffix":""},{"id":470112342,"identity":"4731fa3b-62cd-4f78-922b-4fd0e6505054","order_by":1,"name":"Sibel Akın","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACZh4GhgcQJiOI5uEjSksClGkA0sJG2BqEFjYJMElIg3w778EHiW12ebrtx59Vfs2xk2FjYH746AYeLQaH+ZINEtuSi83OJKTdlt2WDHQYm7FxDj4tzDxmEoltzInbDiQcuy25jRmohYdNGp8W+WYe8x+JbfWJ284/bCuW3FZPWAvDYR4zhsS2w4nbbiSzMX7cdpiwFpBfJBLOHQdqecYszbjtOA8bMwG/yPefPfjhQ1k10GHpDz/+3FZtz8/e/PAxXoeBACM0LkDRCiQJKQeDP1CtP4hSPQpGwSgYBSMNAAApEUeDXxg6fgAAAABJRU5ErkJggg==","orcid":"","institution":"Erciyes Üniversitesi Tıp Fakültesi Hastaneleri","correspondingAuthor":true,"prefix":"","firstName":"Sibel","middleName":"","lastName":"Akın","suffix":""},{"id":470112343,"identity":"fa62a9aa-c887-4c0d-935e-0231d6b5ee59","order_by":2,"name":"Neziha Özlem Deveci","email":"","orcid":"","institution":"Erciyes Üniversitesi Tıp Fakültesi Hastaneleri","correspondingAuthor":false,"prefix":"","firstName":"Neziha","middleName":"Özlem","lastName":"Deveci","suffix":""},{"id":470112344,"identity":"afc3c45a-1396-445a-92de-976b61186778","order_by":3,"name":"Kamil Deveci","email":"","orcid":"","institution":"Kayseri City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kamil","middleName":"","lastName":"Deveci","suffix":""}],"badges":[],"createdAt":"2025-05-28 19:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6770752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6770752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91265973,"identity":"d5448321-55f5-4e67-9aca-15da2fb4d472","added_by":"auto","created_at":"2025-09-14 07:53:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":396859,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6770752/v1/0202aa00-d4f5-4773-bc1d-f1e1a31eeaa6.pdf"},{"id":84555016,"identity":"8db3cf9c-5b3e-4bb2-b534-9d92c94bbcce","added_by":"auto","created_at":"2025-06-13 11:22:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19309,"visible":true,"origin":"","legend":"","description":"","filename":"Table1to3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6770752/v1/958d37a2ef0bc4b78257dbab.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Which Tool Tells the Truth? A Diagnostic Accuracy Study of Malnutrition Screening in Older Hospitalized Adults Using GLIM as the Gold Standard","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalnutrition, which impacts 20\u0026ndash;50% of patients in hospitals and varies based on the population, underlying health issues, and tests used, greatly affects health and quality of life, especially in older adults.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e It can happen because of medical treatments, long periods without food for tests, side effects of drugs, not enough nutritional support, and complicated changes in the body related to inflammation that disrupt normal nutrient use and increase the breakdown of body tissues and metabolism.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Hospital malnutrition can lead to marked weight loss, muscle wasting, and impaired immune function, thereby prolonging hospital stays and elevating the risk of complications, morbidity, and mortality.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEffectively addressing hospital malnutrition necessitates a comprehensive approach, including early screening, personalized nutritional interventions, and interdisciplinary collaboration to ensure optimal patient recovery. Despite its high prevalence and well-documented adverse health outcomes, malnutrition and the risk of it among hospitalized older adults remain underrecognized and inadequately managed. Although routine nutritional screening is recommended, only 10\u0026ndash;20% of hospitalized patients\u0026mdash;even in centers with established clinical nutrition departments\u0026mdash;undergo such assessments.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e This gap may stem from insufficient awareness regarding the validity and reliability of screening tools, uncertainty in selecting the most appropriate instrument, and the lack of standardized protocols for screening at hospital admission.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMalnutrition screening is considered the essential first step in nutritional care, as it enables early identification and timely intervention. An ideal screening tool should be quick and easy to administer while accurately identifying patients at risk and thereby facilitating the efficient allocation of resources for further nutritional evaluation. Such a tool should detect all malnourished individuals without incorrectly classifying well-nourished patients as at risk. Hospitalized and geriatric populations have recommended several validated screening instruments for use. However, one of the primary challenges in validating these tools is the absence of a universally accepted objective criterion\u0026mdash;or \u0026ldquo;gold standard\u0026rdquo;\u0026mdash;for diagnosing malnutrition.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Some of the most commonly used and trusted tools are the Nutrition Risk Screening (NRS 2002)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and the Mini Nutritional Assessment\u0026ndash;Short Form (MNA-SF),\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e which use several signs to help diagnose nutrition issues and plan further actions. Clinical trials have also used these tools as outcome measures, potentially predicting the health status of hospitalized older adults.\u003c/p\u003e \u003cp\u003eIn 2019, we established the Global Leadership Initiative on Malnutrition (GLIM) to promote standardization in clinical practice.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The GLIM framework brings together signs of malnutrition, such as unexpected weight loss, low body mass index (BMI), and muscle loss, with causes such as not eating enough, problems with digestion, inflammation, and illness, to create a shared method for diagnosing malnutrition.\u003c/p\u003e \u003cp\u003eA recent study that looked at 22 approved screening tools found that 28.0% of older adults in hospitals in Europe are at risk of protein-energy malnutrition, and a similar rate of 27.7% was seen in hospitals in Turkey.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The study was conducted in a region with a population of approximately 1.46\u0026nbsp;million and three hospitals with a total capacity of 3,688 beds. Older adults comprise about 10% of the population, and the area has been identified as having a high risk for malnutrition. The population primarily consists of individuals from middle- and low-income backgrounds, and traditional dietary habits\u0026mdash;often rich in carbohydrates due to cultural and economic factors\u0026mdash;may further contribute to nutritional inadequacy.\u003c/p\u003e \u003cp\u003eGiven the available data, it is necessary to conduct studies to assess the accuracy of various screening tools within a specific population. This study aims to find out how common malnutrition is among older adults in the hospital by using different nutritional screening tools (MNA-SF, NRS-2002, MUST, and ESPEN) and to see how closely they align with the GLIM criteria. The study also wants to see how well these tools can find malnutrition, aiming to identify the best and most trustworthy tool for treating this at-risk group.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis prospective cross-sectional study included adult patients (age\u0026thinsp;\u0026gt;\u0026thinsp;60 years) admitted to the internal medicine wards at Erciyes University Hospital (Kayseri, Turkey) from August 2024 to November 2024. A solitary qualified internal medicine physician offered all newly admitted patients the opportunity to participate, and those who consented and gave written informed consent within 48 hours of admission were included. The Medical Research Ethics Committee of Erciyes University's Medical School approved the study. Patients were excluded if they were unable to communicate, had eating disorders, had cancer, experienced severe cognitive impairment or delirium, or were receiving end-of-life palliative care. We gathered sociodemographic and medical information during a structured interview, along with the results of other questionnaires. The data encompassed admission diagnoses, health-related behaviors, and sociodemographic factors (gender, date of birth, reason for hospitalization, and medications utilized). Within 48 hours of admission, we conducted nutritional screening using the following instruments: ESPEN, GLIM, MUST, NRS-2002, and MNA-SF. The ESPEN recommendations endorse the NRS-2002 as a nutritional screening instrument for assessing the nutritional status of hospitalized patients. More than just checking for malnutrition, the NRS-2002 has proven its usefulness. You can also use it to identify patients who would benefit from various forms of nutritional support. The overall NRS-2002 score varies from 0 to 7 points. The patient is initially evaluated for a low BMI (\u0026lt;\u0026thinsp;20.5 kg/m\u0026sup2;), weight loss during the past three months, decreased food consumption in the preceding week, and the existence of a severe illness. A patient with one or more of these conditions undergoes screening. Patients receive a nutritional score (0\u0026ndash;3 points) during the screening phase, which is based on their BMI, weight loss, and reduced food intake. They are also given a disease severity score (0\u0026ndash;3 points) based on their current clinical condition and chronic diseases with acute complications (for example, a cerebrovascular event, traumatic brain injury, major abdominal surgery, or bone marrow transplant); patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years receive an extra point. An NRS-2002 score below three implies no malnutrition risk, while a score of three or more signifies a risk of malnutrition.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The MNA-SF is an abbreviation for the MNA and is a commonly utilized screening tool specifically developed for older populations. This instrument comprises six questions, with responses evaluated on a scale of 0\u0026ndash;2 or 0\u0026ndash;3. These inquiries evaluate weight reduction over the past three months, appetite levels, physical mobility, psychological stress, neuropsychological issues, and body mass index (BMI). We classify patients into three categories based on their total score: \"normal nutritional status\" (12\u0026ndash;14 points), \"nutritional risk\" (8\u0026ndash;11 points), or \"malnourishment\" (0\u0026ndash;7 points). We categorize the patient as \"at risk for malnutrition\" if their overall score is less than 11 points.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The GLIM diagnostic criteria for malnutrition served as the benchmark norm. Three phenotypic criteria and two etiological criteria comprise the GLIM criteria. At least one phenotypic criterion and one etiological criterion must be present to diagnose malnutrition. A person must have lost at least 5% of their body weight in the past six months or at least 10% of their body weight in seven or more months, have a low body mass index (\u0026lt;\u0026thinsp;20 kg/m\u0026sup2; for people younger than 70 years or \u0026lt;\u0026thinsp;22 kg/m\u0026sup2; for people older than 70 years), and have less muscle mass, as shown by a valid body composition analysis, such as bioelectrical impedance analysis (BIA). The causes are less food intake or assimilation (eating less than 50% of your daily nutritional needs for one to two weeks, or any reduction lasting longer than two weeks, or any long-term gastrointestinal condition that makes it difficult to assimilate or absorb food) and inflammation and disease burden from an injury or illness, whether it's short-term or long-term. Malnutrition severity is classified as moderate or severe, determined by factors like the extent of unintended weight loss, BMI, and diminished muscle mass.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The MUST is a proven, fast, and reproducible screening instrument designed for all adult patients across various healthcare environments. It takes into account BMI, unintentional weight loss, and the likelihood of future weight loss resulting from acute illnesses that may hinder food consumption for over five days. Each criterion is assigned a score ranging from 0 to 2 points. The Body Mass Index (BMI) was categorized as 0 for values exceeding 20 kg/m\u0026sup2;, 1 for values between 18.5 and 20 kg/m\u0026sup2;, and 2 for values below 18.5 kg/m\u0026sup2;. We classified weight loss as 0 for less than 5%, 1 for 5\u0026ndash;10%, and 2 for greater than 10%. The presence of acute disease and its possible effect on food intake during the following five days was scored as 0 if absent and 2 if present. We classified the patients' malnutrition risk as low (0), medium (1), or high (2).\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The European Society for Clinical Nutrition and Metabolism (ESPEN) defined malnutrition as having one or more of these signs: a low body mass index (BMI); losing weight without trying while also having a lower BMI; and losing weight without trying, having a lower BMI, and a low fat-free mass index (FFMI).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Body weight and height were recorded at recruitment, specifically upon admittance, utilizing a calibrated weighing scale and a wall-mounted stadiometer, accurate to the closest 0.5 kg and 0.5 cm, respectively. We calculated BMI by dividing the weight (in kilos) by the square of the height (in meters). We evaluated frailty utilizing the FRAIL scale. The FRAIL scale is a commonly utilized instrument for evaluating frailty, which denotes a condition of susceptibility to negative health consequences. It consists of five elements: tiredness, resistance, ambulation, sickness, and weight loss. The scale spans from 0 to 5 points, allocating one point for each component, where 0 signifies optimal health status and 5 denotes the most adverse condition. A score of 3\u0026ndash;5 points categorizes an individual as \"frail,\" 1\u0026ndash;2 points as \"pre-frail,\" and 0 points as \"non-frail.\"\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The Turkish adaptation of the Mini-Mental State Examination (MMSE) evaluated cognitive function.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Cognitive impairment was defined as an MMSE score of less than 24/30 for the illiterate and less than 25/30 for the literate. We used the Geriatric Depression Scale (GDS) to evaluate depressive mood, setting a cut-off score of 14 for the Turkish version.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 202 hospitalized older adults were included in the study, with a median age of 70.6 years (IQR: 64\u0026ndash;76), and 60.9% (n\u0026thinsp;=\u0026thinsp;123) were female. The mean BMI was 28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 kg/m\u0026sup2;. The median length of hospital stay was 8 days (IQR: 6\u0026ndash;13). Comorbidities were prevalent, with hypertension (73.3%), diabetes mellitus (61.9%), and chronic kidney disease (22.3%) being the most common. We observed frailty, cognitive impairment, and depressive symptoms in 76.7%, 15.8%, and 10% of the participants, respectively.\u003c/p\u003e \u003cp\u003eAccording to the GLIM criteria, 33.2% (n\u0026thinsp;=\u0026thinsp;67) of patients were diagnosed with malnutrition. Malnourished patients were significantly older (72.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 vs. 69\u0026thinsp;\u0026plusmn;\u0026thinsp;7 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and had lower BMI values (24.8 vs. 30 kg/m\u0026sup2;, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to their well-nourished counterparts. Serum albumin and hemoglobin levels were also significantly lower in the malnourished group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, respectively).\u003c/p\u003e \u003cp\u003eFrailty was significantly more prevalent among malnourished patients compared to well-nourished individuals (86.6% vs. 71%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Cognitive impairment was also more common in the malnourished group (26.9% vs. 10%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the presence of depressive symptoms did not differ significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13). No significant differences were found between groups regarding hospital stay duration or number of comorbidities.\u003c/p\u003e \u003cp\u003eThe prevalence of malnutrition risk according to screening tools was as follows: MNA-SF (38.4%), NRS-2002 (25.7%), MUST (41.6%), and ESPEN (25.4%). When evaluated against GLIM-defined malnutrition, MNA-SF and MUST demonstrated the highest sensitivity (both 94%), while ESPEN showed the highest specificity (100%) but had the lowest sensitivity (25.4%). The MNA-SF also had the highest overall agreement with GLIM, with a Cohen\u0026rsquo;s kappa value of 0.700, indicating substantial agreement.\u003c/p\u003e \u003cp\u003eThe diagnostic performance of each tool was assessed by ROC analysis. The area under the curve (AUC) was highest for MUST (AUC\u0026thinsp;=\u0026thinsp;0.973), followed by NRS-2002 (AUC\u0026thinsp;=\u0026thinsp;0.948), MNA-SF (AUC\u0026thinsp;=\u0026thinsp;0.908), and ESPEN (AUC\u0026thinsp;=\u0026thinsp;0.627). The MNA-SF had the highest negative predictive value (96.5%) and the lowest negative likelihood ratio (0.07), making it a reliable tool for ruling out malnutrition in this population.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study looked at how common malnutrition is among older patients in the hospital using the GLIM criteria and evaluated how well four popular nutritional screening tools\u0026mdash;MNA-SF, MUST, NRS-2002, and ESPEN\u0026mdash;work. The GLIM framework classified one-third (33.2%) of the study cohort as malnourished. This data matches earlier studies that found hospitalized patients diagnosed with malnutrition using GLIM; the prevalence ranged from 18.9\u0026ndash;80.0%. The differences in prevalence rates may be due to whether a nutritional screening was done, which tool was used, and how the etiological criterion of \"presence of inflammation\" and the phenotypic criterion of \"low muscle mass\" were evaluated.\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe biggest limitation in validating the effectiveness of malnutrition screening tools is the absence of a single objective measure or \"gold standard\" for diagnosing malnutrition.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e GLIM is a prevalent and validated nutritional assessment instrument that utilizes various indicators to identify malnutrition and commence treatment. Clinical studies have endorsed it, and it forecasts health outcomes in hospitalized older individuals.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn our study, unlike previous research, we did not use any approved screening tool at the beginning to find people at risk of malnutrition before applying the GLIM criteria to diagnose malnutrition. Van Dronkelaar et al. found that the prevalence of malnutrition according to the GLIM criteria without prior screening was 42% in their study, whereas in our study, this rate was lower (33.2%). In this study, unlike ours, there were patients from the acute admission ward, neurology, and geriatrics departments, and the number of patients was higher than in our study.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBrito et al. evaluated all patients using the GLIM criteria without conducting a prior nutritional risk screening, as our study did. The GLIM criteria are said to be a viable option for diagnosing malnutrition in hospitalized patients, as they independently affect the length of stay, the risk of dying in the hospital, and the risk of dying within six months of discharge.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe ESPEN criteria demonstrated perfect specificity (100%) but exhibited the lowest sensitivity (25.4%) and the least concordance with GLIM. The ESPEN screening tool has high PPVs, meaning most patients identified as malnourished by it are also defined as malnourished by GLIM criteria. However, the low sensitivity (25.4%) suggests that ESPEN does not identify half of the malnourished patients. The results show that using ESPEN alone may miss many malnourished patients, making it a poor choice for screening in this case. This outcome aligns with the conclusions of prior research.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e As in our study, according to the GLIM criteria, half of the geriatric rehabilitation patients were malnourished, while the prevalence was much lower when the ESPEN definition was applied. The GLIM criteria indicate that 52.0% of the participants in this study are malnourished overall. The ESPEN definition diagnosed malnutrition in 12% of the patients. The difference between the diagnostic tools is clear because ESPEN requires weight loss to be linked to either a low BMI or an FFMI malnutrition diagnosis, while the GLIM criteria only need one of these conditions. GLIM's incorporation of etiological criteria elucidates the increased prevalence relative to ESPEN.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Ren et al. discovered a higher prevalence of malnutrition among older patients in the hospital, based on both the SGA scale and the ESPEN 2015 criteria, as reported by GLIM. Based on the GLIM standards, 37.9% were identified as malnourished; according to the SGA criteria, 32.8% were classified as malnourished; and per the ESPEN 2015 criteria, 17.0% were deemed malnourished.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe MNA-SF and MUST screening methods exhibited the highest sensitivity, both at 94%, highlighting their efficacy in detecting patients at risk for malnutrition. In previous studies, MNA-SF was not as specific as nutritional assessments or expert evaluations of nutritional status in hospitalized older adults, which is not the case in our study.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Neelemaat and colleagues demonstrated that the MST, MUST, NRS-2002, and SNAQ are effective tools for assessing malnutrition in hospitalized patients. However, they found the MNA-SF to be inadequate for use in hospitalized older adults, as it failed to reliably identify individuals with nutritional deficiencies.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Slee and colleagues found a significant difference between MUST and MNA-SF scores in frail older patients, raising concerns about whether MUST is the best tool for assessing malnutrition risk in this group. They contended that the failure to utilize the MUST as the benchmark for evaluating malnutrition risk in frail older inpatients was a significant issue.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e All hospitals and care facilities employ the MUST. The MUST establishes a malnutrition risk score based on the current body mass index (BMI), documented weight loss, and the occurrence of acute illness or lack of nutritional intake for five days. The MNA-SF poses identical inquiries to the MUST, supplemented by additional questions regarding neuropsychological functional state, physical mobility, and dietary intake. The MNA employs a more elevated grading scale for BMI in comparison to the MUST. The participants' BMI in this study was primarily among the normal and overweight categories.\u003c/p\u003e \u003cp\u003eIn another study, researchers compared different tools like MUST, SGA, and NRS-2002 for managing nutrition in older patients in the hospital, and they found that the GLIM framework showed a higher malnutrition rate (46%) than what we found in our study. Compared to MUST, SGA, and NRS-2002, the authors found that MUST had the highest concordance with GLIM, which is consistent with our study. They suggested that we MUST determine the nutritional status of older patients upon hospital admission and then conduct a GLIM assessment.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition to nutritional status, our findings revealed significant correlations between malnutrition and frailty, cognitive decline, and diminished blood albumin concentrations. Frailty was much more common in malnourished patients, supporting the growing evidence that malnutrition and frailty often occur together and can affect each other.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Cognitive impairment was notably more prevalent in malnourished patients, aligning with other research that highlights the adverse impact of inadequate nutrition on cognitive performance.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e In contrast to the literature, although depressive symptoms were more common in the group, the difference lacked statistical significance.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e This difference might be due to the small number of patients with high GDS scores or the fact that symptoms of malnutrition and depression, like tiredness and weight loss, can make it hard to assess.\u003c/p\u003e \u003cp\u003eThis study has several important aspects, such as its forward-looking design, the use of the new GLIM criteria for diagnosis, and the comparison of different screening methods. However, we must acknowledge certain limitations. A single center conducted the investigation, potentially limiting its generalizability. Survey participation is voluntary, and written informed consent is required from all patients, which may lead to selection bias and exclude patients in poor health. These patients are at a heightened risk for malnutrition and would thus be particularly relevant to the study. The omission of SGA in our investigation may be considered a drawback, despite its prevalent usage in most studies evaluating screening techniques. Since its introduction, many groups have extensively utilized SGA, verified it, and linked it to clinical results. A further drawback is that the cross-sectional nature of the current investigation precludes the establishment of causal relationships.\u003c/p\u003e \u003cp\u003eIn conclusion, there is significant variation in malnutrition prevalence among hospitals, primarily attributable to the differing diagnostic equipment employed. The alignment with prior studies underscores the dependability of the GLIM criteria in detecting malnutrition in hospitalized older patients. There was a strong agreement between the GLIM and MNA-SF (k\u0026thinsp;=\u0026thinsp;0.70), but there was little to no agreement among MUST, NRS-2002, and ESPEN (κ\u0026thinsp;=\u0026thinsp;0.17, κ\u0026thinsp;=\u0026thinsp;0.13, κ\u0026thinsp;=\u0026thinsp;0.05); this difference shows that these nutritional assessments find different groups of people at risk. We emphasize the importance of finding out how common malnutrition is by using thorough nutritional evaluations instead of just screening tools, which often mistakenly identify too many people as malnourished because they are not very accurate. Healthcare personnel may overlook malnutrition in the older adults if they do not conduct nutritional screening. Neglecting to recognize malnutrition will result in inadequate treatment, which can have serious health implications. We must conduct a comprehensive nutritional examination to ascertain malnutrition or its risk. Nonetheless, doing such an exhaustive evaluation on all patients in a hospital environment is impractical due to temporal and cost limitations. It emphasizes the necessity of employing standardized instruments for precise nutritional evaluations in hospital environments.\u003c/p\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistical assesment of nutritional screening techniques in comparison to the diagnostic criteria for malnutrition: NRS 2002, MUST, MNA-SF, ESPEN\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\u003eNRS- 2002\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMUST\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eESPEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMNA-SF\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\u003eSensitivity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\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=\"char\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.5\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=\"char\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.6\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=\"char\"\u003e\n \u003cp\u003e89.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive Likelihood ratio(LR+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative likelihood ratio(LR -)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Kappa; value (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.948\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,973\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,908\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.S: Data collection, interpretation, manuscript draftingS.A: Study design, Conceptualization, supervision, manuscript review, correspondence, manuscript drafting, literature searchN.\u0026Ouml;.D: Critical manuscript revision, literature search, manuscript draftingK.D: Data analysis, patient recruitment, data acquisition, clinical interpretation, manuscript draftingAll authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaiser MJ, Bauer JM, R\u0026auml;msch C, et al. 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Clin Nutr. 2021;40(6):4366-4372. doi:10.1016/j.clnu.2021.01.009\u003c/li\u003e\n\u003cli\u003evan Dronkelaar C, Tieland M, Cederholm T, Reijnierse EM, Weijs PJM, Kruizenga H. Malnutrition Screening Tools Are Not Sensitive Enough to Identify Older Hospital Patients with Malnutrition. Nutrients. 2023;15(24):5126. Published 2023 Dec 17. doi:10.3390/nu15245126\u003c/li\u003e\n\u003cli\u003eCortes R, Ya\u0026ntilde;ez AM, Capit\u0026aacute;n-Moyano L, Mill\u0026aacute;n-Pons A, Bennasar-Veny M. Evaluation of different screening tools for detection of malnutrition in hospitalised patients. J Clin Nurs. 2024;33(12):4759-4771. doi:10.1111/jocn.17170\u003c/li\u003e\n\u003cli\u003eClark AB, Reijnierse EM, Lim WK, Maier AB. Prevalence of malnutrition comparing the GLIM criteria, ESPEN definition and MST malnutrition risk in geriatric rehabilitation patients: RESORT. Clin Nutr. 2020;39(11):3504-3511. doi:10.1016/j.clnu.2020.03.015\u003c/li\u003e\n\u003cli\u003eRen SS, Zhu MW, Zhang KW, et al. 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J Nutr Health Aging. 2008;12(5):313-318. doi:10.1007/BF02982661\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1 to 3","content":"\u003cp\u003eTable 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GLIM, Nutritional assessment, Nutrition screening tools, Malnutrition, Older adults, Hospital","lastPublishedDoi":"10.21203/rs.3.rs-6770752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6770752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo analyze the prevalence of malnutrition in hospitalized older patients and to assess the efficacy of the most frequently utilized nutritional screening instruments in identifying individuals at risk of malnutrition.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMethods: A prospective cross-sectional study was conducted on 202 older inpatients (mean age: 70.6 years; 60.9% female) in internal medicine wards. Within 48 hours of admission, patients were screened using NRS-2002, MNA-SF, MUST, and ESPEN criteria. The GLIM framework was used as the reference standard. Sensitivity, specificity, predictive values, and Cohen\u0026rsquo;s kappa were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on GLIM, 33.2% (n\u0026thinsp;=\u0026thinsp;67) of patients were malnourished. These patients were older (72.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 vs. 69\u0026thinsp;\u0026plusmn;\u0026thinsp;7 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and had lower BMI (24.8 vs. 30 kg/m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). They also had reduced serum albumin and hemoglobin levels (p\u0026thinsp;=\u0026thinsp;0.013 and p\u0026thinsp;=\u0026thinsp;0.02). The prevalence of malnutrition risk was: MUST (41.6%), MNA-SF (38.4%), NRS-2002 (25.7%), and ESPEN (25.4%). MNA-SF and MUST had the highest sensitivity (94%), while ESPEN showed perfect specificity (100%) but low sensitivity (25.4%). MNA-SF had the strongest agreement with GLIM (κ\u0026thinsp;=\u0026thinsp;0.700).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe MNA-SF and MUST exhibited the greatest sensitivity, while ESPEN displayed the best specificity but possessed the lowest sensitivity. The MNA-SF had the highest overall concordance with GLIM, as evidenced by Cohen\u0026rsquo;s kappa value reflecting considerable agreement.\u003c/p\u003e","manuscriptTitle":"Which Tool Tells the Truth? A Diagnostic Accuracy Study of Malnutrition Screening in Older Hospitalized Adults Using GLIM as the Gold Standard","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 11:22:48","doi":"10.21203/rs.3.rs-6770752/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed7f0562-2865-4d25-b55c-a09000f436c7","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-14T07:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 11:22:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6770752","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6770752","identity":"rs-6770752","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}