Abstract
Introduction
The Global Leadership Initiative on Malnutrition (GLIM) lacks reliable blood tests for evaluating the nutrition status. We retrospectively compared the GLIM criteria, Controlling Nutrition Status (CONUT) score, and Subjective Global Assessment (SGA) to establish effective malnutrition screening and provide appropriate nutritional interventions according to severity.
Methods
We classified 177 patients into 3 malnutrition categories (normal/mild, moderate, and severe) according to the GLIM criteria, CONUT score, and SGA. We investigated the malnutrition prevalence, concordance of malnutrition severity, predictability of clinical outcome, concordance by etiology, and clinical outcome by inflammation.
Results
The highest prevalence of malnutrition was found using the GLIM criteria (87.6%). Concordance of malnutrition severity was low between the GLIM criteria and CONUT score. Concordance by etiology was low in all groups but was the highest in the “acute disease” group. The area under the curve of clinical outcome and that of the “with inflammation group” were significantly higher when using the CONUT score versus using the other tools (0.679 and 0.683, respectively).
Conclusion
The GLIM criteria have high sensitivity, while the CONUT score can effectively predict the clinical outcome of malnutrition. Their combined use can efficiently screen for malnutrition and patient severity in acute care hospitals.
Keywords: Global Leadership Initiative on Malnutrition criteria, Controlling Nutrition Status score, Subjective Global Assessment, Malnutrition, Clinical outcome
Introduction
Malnutrition ensues 15–60% of hospitalized patients, negatively impacting the clinical course of their disease [1]. Therefore, screening for and assessing malnutrition is crucial, but a gold standard method is lacking. The most used and well-validated tool for assessing malnutrition is the Subjective Global Assessment (SGA) [2]. The SGA is a simple, noninvasive, economical, and reliable predictor of hospital-related outcome; hence, it has been considered a semi-gold standard method [3]. However, the SGA has some drawbacks; it is time-consuming and subjective, limiting its use.
Nutrition screening tools that are based on laboratory parameters are objective, reproducible, simple, and time-saving, allowing for an automatic assessment. Serum albumin (ALB) is the simplest and most used, but its usefulness is limited because ALB has a long half-life (20 days) and is susceptible to inflammation [4]. To address this problem, the Controlling Nutrition Status (CONUT) score, a composite method based on the assessment of serum ALB, total cholesterol (T-cho), and total lymphocyte count (TLC), was developed [5]. It is used to predict the prognosis of patients with malnutrition [5, 6, 7, 8]. Furthermore, there is a substantial agreement between the CONUT score and SGA [6].
In 2018, consensus diagnostic criteria for malnutrition, the Global Leadership Initiative on Malnutrition (GLIM) criteria, were adopted [9]. It considers phenotypic and etiologic criteria and is preceded by a validated screening tool for malnutrition [9]. Its use has recently increased [10, 11] and a comparison with pre-existing nutrition screening tools, including the SGA, suggests that it has a higher sensitivity [12, 13, 14]. However, the GLIM criteria and the CONUT score have not been compared.
This study aimed to compare the GLIM criteria, CONUT score, and SGA to establish an efficient malnutrition screening tool among patients receiving nutritional intervention from the Nutritional Support Team (NST) of a university hospital dealing with patients in the acute phase. Moreover, the usefulness of these assessment tools for predicting the clinical outcome of the patients was compared.
Materials and Methods
Study Design
This retrospective observational study was conducted at the Hokkaido University Hospital, an acute care hospital with 924 beds. Subjects included 190 consecutive patients who were admitted from January 2016 to December 2018 and who required intervention by the NST. Patients were screened for malnutrition by the attending physicians and subsequently reassessed using 3 tools by a physician certified by the Japanese Society for Clinical Nutrition and Metabolism after the NST intervention.
Patient Characteristics
Patients aged ≥20 were included, and 13 patients who lacked usual body weight or skeletal muscle index (SMI) data were excluded. Patients in whom the SMI could not be analyzed were with those such as cardiac pacemaker implants, in whom the use of bioelectrical impedance analysis (BIA) was contraindicated. The remaining 177 patients were enrolled in this study which was approved by the Institutional Review Board of Hokkaido University (No. 018-0443). Information about the aim of the study was posted on the Hokkaido University website, and potential participants could decline to participate or opt out at any time.
Subjective Global Assessment
The SGA questionnaire included information on patient history (weight loss, changes in dietary intake, gastrointestinal symptoms, and functional capacity), a physical examination (muscle, subcutaneous fat, sacral and ankle edema, and ascites), and the clinician's assessment of the patient's status. A patient is classified as normal or mild, moderate, and severe malnourished, depending on the combination of assessment results [2].
GLIM Criteria
The GLIM criteria are composed of 3 phenotypic (used for grading malnutrition severity as normal or mild, moderate, or severe) and 2 etiologic components; the patient is required to meet ≥1 criterion from each component for a malnutrition diagnosis [9].
Phenotypic Criteria
Nonvolitional Weight Loss
Weight loss of 5–10% and >10% within the previous 6 months, or 10–20% and >20% after 6 months, was classified as moderate and severe malnutrition, respectively [9].
Low Body Mass Index
Body mass index (BMI) was calculated as body weight (kg) divided by the square of the patient's height (m2). A BMI <18.5 kg/m2 and <17.0 kg/m2 for participants aged <70, or <20.0 kg/m2 and <17.8 kg/m2 for those aged >70, was classified as moderate and severe malnutrition, respectively [9, 10].
Reduced Muscle Mass
The SMI assesses muscle reduction. Body composition was determined by BIA using body composition analyzers (InBody 770 and InBody S20 analyzers, InBody Japan, Tokyo, Japan) [15]. The SMI was calculated by dividing the appendicular skeletal muscle mass (kg) by the square of the patient's height (m2). The appendicular skeletal muscle was defined as the sum of the muscle masses of the 4 limbs, which was automatically calculated by the InBody analyzer software. According to the GLIM criteria, the reduced muscle phenotype should be the same one for diagnosing sarcopenia [16]. An SMI <7.0 kg/m2 and <6.1 kg/m2 in men and <5.7 kg/m2 and <5.3 kg/m2 in women was classified as moderate and severe malnutrition, respectively.
Etiologic Criteria
Reduced Food Intake or Assimilation
This criterion was considered present when food intake provided ≤50% of the energy requirement for ≥1 week or when the decrease in food intake continued for ≥2 weeks. As a supportive indicator of this etiology, a clinical judgment was made regarding the presence of gastrointestinal symptoms, short bowel syndrome, pancreatic dysfunction, esophageal stricture after weight loss surgery, and gastroparesis.
Disease Burden or Inflammation
According to the GLIM criteria and based on the clinical judgment, we classified disease burden or inflammation into 4 groups: “chronic disease with inflammation,” “chronic disease with minimal or no perceived inflammation,” “acute disease or injury with severe inflammation,” and “starvation” that included being hungry or having a food shortage associated with socioeconomic or environmental factors [9], and a final group that could not be classified, the “unclassifiable” group.
C-reactive protein (CRP) concentration was used as a measure of inflammation. The “chronic disease with inflammation” group included patients with protein catabolism who had a CRP concentration of ≥0.5 mg/dL [17]. The “chronic disease with minimal or no perceived inflammation” group included patients with nonprotein catabolic disorders. The “acute disease or injury with severe inflammation” group comprised patients with severe infections, burns, trauma, and those undergoing operative intervention, while patients with complicated pathological conditions in whom the presence or absence of inflammation could not be determined comprised the “unclassifiable” group.
CONUT Score
The CONUT score was calculated using serum ALB, TLC, and T-cho. Point values were assigned to different ranges: ALB (0–6), TLC (0–3), and T-cho (0–3). Patients were classified into 3 malnutrition groups − normal or mild (0–4), moderate (5–8), or severe (9–12) − and scored accordingly [5].
Clinical Outcome
The clinical outcomes for all patients were classified into 3 groups: nonsurvivors, hospital transfer for further chronic care, and home discharged. Except for the “unclassifiable” group, patients in the remaining groups were classified into 2 groups: “with inflammation” and “without inflammation.”
Statistical Analyses
We used Venn diagrams to illustrate the relationship between the prevalence of malnutrition as assessed using the GLIM criteria, CONUT score, and SGA. At this time, “moderate” and “severe” malnutrition groups were defined as “malnutrition.”
Concordance between the GLIM criteria and CONUT score was evaluated by agreement (%), the kappa coefficient (weighted κ), and Kendall's coefficient of concordance (Kendall's W). The weighted κ and Kendall's W were interpreted as follows: 0.00–0.20: slight; 0.21–0.40: fair; 0.41–0.60: moderate; 0.61–0.80: substantial; and 0.81–1.00: almost perfect agreement [18].
To determine the accuracy of the malnutrition severity classification against clinical outcomes according to the 3 malnutrition tools, the area under the curve (AUC) was calculated. The receiver operating characteristic curve of the 3 tools was also evaluated to confirm their ability to predict clinical outcomes in patients with malnutrition. Patient background (age, sex, days from intervention to discharge, and diagnosis upon admission) in the no-event group (home discharge) and event group (nonsurvivors and hospital transfer) were compared using the Mann-Whitney U test and χ2 test. Moreover, we examined clinical outcomes by group classified by the presence or absence of inflammation. The AUC was classified as poor if <0.60, fair if 0.60–0.80, and good if >0.80 [19]. A p value <0.05 was considered statistically significant. The data were analyzed using JMP Version 14.0.0 (SAS Institute Inc., Cary, NC, USA) and Bell Curve for Excel Version 3.20 (Social Survey Research Information Corp., Tokyo, Japan).
Results
Online suppl Table 1 (for all online suppl. material, see www.karger.com/doi/10.1159/000516994) shows the characteristics of the 177 included patients. The prevalence of moderate and severe malnutrition was 155/177 (87.6%) according to the GLIM criteria, 107/177 (60.4%) according to the CONUT score, and 122/177 (68.9%) according to the SGA. The relationship between the GLIM criteria, CONUT score, and SGA in detecting moderate or higher levels of malnutrition is shown in Figure 1. When comparing the classifications obtained by the 3 tools, all 3 classified patients as malnourished in 71/177 (40%) cases and as nonmalnourished in 1/177 (1%) cases; therefore, the agreement rate was 72/177 (41%). The agreement regarding the classification of malnutrition between 2 tools was approximately the same: 52% for the GLIM criteria and CONUT score, 64% for the GLIM criteria and SGA, and 67% for the CONUT score and SGA. However, 32/177 (18%) patients were classified as nonmalnourished when using the CONUT score and SGA, while the same patients were classified as malnourished when using the GLIM criteria. Thus, there was a discrepancy in the assessment of malnutrition between the GLIM criteria and the other 2 tools. In all patients, the GLIM criteria and CONUT score had slight concordance (agreement: 20.9%, weighted κ: −0.16, Kendall's W: 0.11).
Fig. 1.
Malnutrition assessment of 177 patients. Venn diagrams showing the relation among the GLIM criteria, CONUT score, and SGA in detecting moderate or higher levels of malnutrition. GLIM, Global Leadership Initiative on Malnutrition; CONUT, Controlling Nutrition Status; SGA, Subjective Global Assessment.
The etiological classification of the 177 patients is shown in Table 1. In this study, the “starvation” group was not applicable to any patients. The agreement of the GLIM criteria and the CONUT score was the highest for the “acute disease” group (52.9 vs. <25.3% in other groups). The weighted κ was below the fair agreement or not available for all groups. Kendall's W depicted almost perfect agreement in the “acute disease” group, fair agreement in the “chronic disease with minimal or no perceived inflammation,” and “unclassifiable” groups, and slight agreement in the “chronic disease with inflammation” group. Considering the no-event group (home discharge) and the event group (nonsurvivors and hospital transfer), no significant difference was found in age, sex, or diagnosis upon admission, excluding that of cardiovascular diseases, whereas the number of days from intervention to discharge was significantly more in the event group (Online suppl. Table 2).
Table 1.
Etiology classification (n = 177): the concordance in malnutrition classification using between the GLIM criteria and CONUT score
| Malnutrition etiology* | N (%) | Malnutrition severity by GLIM criteria |
Malnutrition severity by CONUT score |
Agreement, n (%) | Weighted κ | Kendall's W |
|---|---|---|---|---|---|---|
| normal or mild/moderate/severe | ||||||
| Total | 177 | 22/26/129 | 70/53/54 | 37 (20.9) | −0.16 | 0.11 |
| Chronic disease with minimal or no perceived inflammation | 69 (39.0) | 4/9/56 | 45/15/9 | 5 (7.2) | −0.12 | 0.23 |
| Chronic disease with inflammation | 79 (44.6) | 13/13/53 | 22/28/29 | 20 (25.3) | −0.15 | 0.11 |
| Acute disease or injury with severe inflammation | 17 (9.6) | 4/3/10 | 0/5/12 | 9 (52.9) | 0.12 | 0.94 |
| Unclassifiable | 12 (6.8) | 1/1/10 | 3/5/4 | 3 (25.0) | −0.13 | 0.33 |
GLIM, Global Leadership Initiative on Malnutrition; CONUT, Controlling Nutrition Status; weighted κ, kappa coefficient; Kendall's W, Kendall's coefficient of concordance. *There was duplicate pathology for each patient. *Starvation group was not applied to any patients.
The AUC of the clinical outcome was significantly superior when using the CONUT score (0.679) and SGA (0.611) than when using the GLIM criteria (0.569, p = 0.014). Excluding the “unclassifiable” group, among the remaining 165 patients, 96 were classified as “with inflammation” and 69 as “without inflammation.” When compared according to the presence or absence of inflammation, the AUC of “with inflammation” group was significantly better when using the CONUT score (0.683) and SGA (0.586) than when using the GLIM criteria (0.518, p < 0.01). In “without inflammation” group, no significant difference was found in the AUC (shown in Fig. 2; Online suppl. Table 3).
Fig. 2.
Accuracy of the assessment of malnutrition severity against the clinical outcome in (a) all the patients (n = 177), patients with inflammation (n = 96) (b), and patients without inflammation (n = 69) (c). The chart shows the ROC curve for the prediction of clinical outcomes based on the malnutrition severity obtained by the GLIM criteria, CONUT score, and SGA. In this analysis, nonsurvivors and hospital transfers were calculated as events and moderate, or higher levels of malnutrition defined as “malnutrition.” GLIM, Global Leadership Initiative on Malnutrition; CONUT, Controlling Nutrition Status; SGA, Subjective Global Assessment; ROC, receiver operating characteristic.
Discussion
This study was conducted among acute care patients who were admitted to a university hospital and required intervention from the NST. We evaluated the concordance of 3 malnutrition assessment tools, the GLIM criteria, CONUT score, and SGA. The concordance between the GLIM criteria and CONUT score was significantly lower than previously reported [12, 13, 14] because the CONUT score solely comprised blood tests [6], while the GLIM criteria's evaluation items did not coincide. The GLIM criteria are extremely sensitive and false positives may occur; however, it is the most useful malnutrition screening tool [12, 13, 20].
To investigate more detailed agreement trends, we compared concordance by etiology; better concordance was obtained for the “acute disease” group than that obtained for the other groups. The GLIM criteria are more sensitive, reflecting an earlier nutritional status [14], but in the “acute disease” group, patients had more inflammation leading to cytokine activation. Serum ALB decreases due to synthesis suppression and increased degradation, whereas CRP increases [21]. In this group, the CONUT score may have been higher and overestimated, causing improved concordance with the sensitive GLIM criteria [12, 20]. Based on the above and compared to the GLIM criteria, the CONUT score was considered less sensitive and the malnutrition screening performance inferior in patients with less inflammation.
Of the 59 patients who were classified as “severe” by the GLIM criteria and “normal or mild” by the CONUT score, 40 were in the “chronic disease with minimal or no inflammation” group, and half of them had neuropathic anorexia. Like the previous reports, serum ALB was retained in these patients regardless of their body composition [22], but the mechanism is not clear.
In all 12 patients graded as “normal or mild” by the GLIM criteria and “severe” by the CONUT score, the extracellular water/total body water obtained using BIA was ≥0.400, and edema was observed. Edema may have caused overestimation of the BMI and SMI, affecting the GLIM criteria [20].
The discrepancy between the GLIM criteria and SGA indicates that further research is needed to elucidate their relationship. Therefore, we compared the clinical outcomes. The AUC of the clinical outcomes showed that the CONUT score and SGA grading of malnutrition severity were useful to predict clinical outcomes compared to the GLIM criteria. Clinical outcomes were also examined based on the presence or absence of inflammation; the CONUT score and SGA were useful for predicting the former.
In severe cases, inflammation is common and may lead to poor outcomes. The presence of inflammation in patients with malnutrition often limits the effectiveness of nutritional therapy and may impair the clinical response to drug therapy, rendering its analysis essential [11, 23]. The CONUT score evaluated inflammation in the “with inflammation” group and predicted the effect of nutrition therapy and clinical outcome. Since there was no significant difference in admission diagnosis that would affect clinical outcomes between the good and poor clinical outcome groups, the factors affecting clinical outcomes might involve a complex pathology of inflammatory response and nutritional status rather than the disease itself.
In the “without inflammation” group, severe malnutrition was often seen in patients with reduced food intake and improved in many cases using nutrition therapy [23]. It was one of the reasons why both GLIM criteria and CONUT score were poor in the AUC assessment of clinical outcomes.
This study had some limitations: first, the only consideration was clinical outcome [13, 24]; the prognosis for death could not be assessed. Hospital duration, which is most often used for nutritional evaluation, could not be assessed [25]. Second, clinical outcomes could not be compared with a group not receiving NST intervention. Third, it was a single-center study with a small sample size. In particular, the sample size of each of the 4 groups in the etiological classification of patients was small. In this regard, it is necessary to increase the sample size and further study the usefulness of these malnutrition assessment tools. Despite these limitations, this study covered all patients requiring NST intervention, regardless of their condition. In the clinical setting, the combined use of the highly sensitive GLIM criteria and indicators such as the CONUT score which can simultaneously evaluate the degree of inflammation assessing malnutrition leads to a more accurate assessment of patient status. Our findings are valuable as we present the applicability of the GLIM criteria in acute care hospitals, which has not been reported previously.
Conclusions
The concordance between the GLIM criteria and CONUT score was low, suggesting that these 2 tools evaluate the nutritional status of patients from different perspectives. On comparing these 2 malnutrition assessment tools with SGA, the most validated tool, the GLIM criteria were found to exhibit high sensitivity, while the CONUT score may be useful in predicting the clinical outcomes of malnutrition. Incorporation of blood test into the GLIM criteria, such as for CONUT score, will improve the prediction of patient prognosis, nutritional assessment, and therapy.
Statement of Ethics
This study was approved by the Review Board of Hokkaido University (Approval no. 018-0443). Information about the aim of the study was posted on the Hokkaido University website, and potential participants could decline to participate or opt out at any time.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author Contributions
A.M., T.I., T.S., and H.T. participated in the research design, execution, data analysis, and drafting of the manuscript. S.K. participated in performing the research. M.N., J.S., and T.T. participated in the review of the manuscript.
Supplementary Material
Supplementary data
Supplementary data
Supplementary data
Acknowledgements
We are grateful to all the patients and health professionals who contributed to the data collection at Hokkaido University Hospital.
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