Revised: June 17, 2026
Accepted: July 10, 2026
Published online: July 27, 2026
Processing time: 86 Days and 4.2 Hours
In patients with cirrhosis and ascites, fluid retention may artificially increase their body weight and body mass index (BMI), potentially masking a low BMI and le
To investigate whether computed tomography (CT)-based correction of the body weight improves malnutrition detection in patients with cirrhosis and ascites.
This retrospective study included 100 hospitalized patients with cirrhosis and ascites. The BMI was evaluated using three approaches: The actual body weight method, the CT-corrected method, and the CT-sensitive corrected method. Low BMI and Global Leadership Initiative on Malnutrition (GLIM)-defined malnutrition were assessed under the three methods, and differences in detection rates were compared.
Compared with the actual body weight method, both the CT-corrected method and the CT-sensitive corrected method resulted in lower BMI values (both P < 0.001), with further reduction observed for the CT-sensitive corrected method (P = 0.009). The detection rates of low BMI increased from 6.0% to 10.0% and 12.0%, respectively. The detection rates of GLIM-defined malnutrition increased from 19.0% to 22.0% and 24.0%, respectively. Reclassification was unidirectional, with patients shifting from a non-low BMI or non-malnourished status under the actual body weight method to a low BMI or malnutrition status under the CT-corrected or CT-sensitive corrected method.
CT-based body weight correction reduced BMI estimates and improved the detection of low BMI and GLIM-defined malnutrition in patients with cirrhosis and ascites. This approach may serve as a useful adjunct to nu
Core Tip: In patients with cirrhosis and ascites, the body mass index (BMI) may be overestimated because of fluid retention. This study showed that computed tomography-based body weight correction reduced BMI estimates and identified additional patients with a low BMI and Global Leadership Initiative on Malnutrition-defined malnutrition. These findings suggest that computed tomography-based correction may serve as a supplementary approach to nutritional assessment in patients with substantial fluid retention.
- Citation: Zhao HJ, Su LH, Lu FH, Gao F, Sai JH, Li P, Shi JL, Wang L, Li SJ, Zhou F, Zhou LM, Wang N, He PY. Computed tomography-based correction of the body mass index improves malnutrition detection in patients with cirrhosis and ascites. World J Hepatol 2026; 18(7): 122692
- URL: https://www.wjgnet.com/1948-5182/full/v18/i7/122692.htm
- DOI: https://dx.doi.org/10.4254/wjh.122692
Cirrhosis represents the common end stage of chronic liver diseases. Ascites is one of the most frequent complications of decompensated cirrhosis and an important marker of disease progression and a poor prognosis[1,2]. In addition to portal hypertension and hypoalbuminemia, sodium and water retention often lead to peritoneal fluid accumulation and peri
Malnutrition is also a common and clinically important problem in those with cirrhosis[5,6]. It is associated with higher rates of infection, prolonged hospitalization, more complications, and increased mortality[7,8]. Early and accurate identification of malnutrition is therefore essential. The body mass index (BMI) remains one of the most widely used indicators in nutritional assessment[9], and a low BMI is included as a phenotypic criterion in the Global Leadership Initiative on Malnutrition (GLIM) framework[10,11]. However, in patients with cirrhosis and ascites, the BMI calculated from the actual body weight may be substantially influenced by fluid retention, leading to overestimation of nutritional body weight and masking of a low BMI or malnutrition[12,13].
Accurate estimation of dry weight is therefore a key challenge in nutritional assessment in this population. Abdominal computed tomography (CT), which is routinely performed in many hospitalized patients with cirrhosis, can provide an objective basis for ascites volume quantification and subsequent body weight correction[14]. Additional consideration of peripheral edema may further improve estimation of the true body weight[15]. However, evidence remains limited regarding how CT-based ascites correction, with or without additional edema adjustment, affects BMI classification and GLIM-defined malnutrition in patients with cirrhosis and ascites.
Therefore, we hypothesized that CT-based correction of body weight for ascites volume, particularly when combined with additional adjustment for severe lower-limb edema, would reduce BMI values and increase the detection of low BMI and GLIM-defined malnutrition compared with assessment based on the actual body weight.
This study used a retrospective design and included 100 hospitalized patients with cirrhosis and ascites who were treated in our hospital between January 2023 and January 2025. The aim of this study was to evaluate the effects of ascites and peripheral edema on body weight, BMI, and malnutrition diagnosis, and to compare the differences among the actual body weight method, CT-corrected method, and CT-sensitive corrected method in low-BMI identification and GLIM-defined malnutrition assessment. This study was approved by the Ethics Committee of the Affiliated Hospital of Chengde Medical University, approval No. CYFYLL2023431; approved on May 19, 2023, and was conducted in accor
Patients were included in this study if they met the clinical diagnostic criteria for cirrhosis with ascites; were aged 18 years or older; had their height and body weight measured and abdominal CT performed after admission; and had relatively complete clinical data available for the BMI calculation and nutritional assessment analysis. Patients were excluded if they had a malignant tumor of the digestive system; had severe infection or severe organ failure; or had missing key data that prevented BMI calculation or nutritional assessment.
Patient data were collected from the electronic medical record system, nursing records, laboratory system, and imaging data, mainly including general information, nutrition-related indicators, and laboratory parameters. Additional clinical variables were collected to evaluate the clinical relevance of malnutrition reclassification, including admission complications, nutritional intervention during hospitalization, readmission after discharge, and one-year mortality.
The general information included sex, age, height, body weight at admission, etiology, length of hospital stay, abdominal circumference, presence or absence of lower-limb edema, percentage of body weight attributable to lower-limb edema, presence or absence of hepatic encephalopathy, Child-Pugh score, liver function grade, and presence or absence of esophagogastric bleeding.
The nutrition-related variables included weight loss within the previous 6 months, arm circumference (AC), triceps skinfold thickness (TSF), arm muscle circumference (AMC), and the actual BMI, CT-corrected BMI, and CT-sensitive corrected BMI calculated using the actual body weight method, CT-corrected method, and CT-sensitive corrected method, respectively.
The laboratory parameters included white blood cell count, lymphocyte count, hemoglobin, platelet count, total bilirubin, prealbumin, albumin, creatinine, blood urea nitrogen, international normalized ratio, prothrombin activity, and prothrombin time. The laboratory parameters were collected from the first available test results after admission.
Actual body weight method: The BMI was calculated based on the actual body weight measured at admission, and the resulting value was defined as the actual BMI. The formula is as follows: BMI (actual) = actual body weight (kg)/height (m)2.
Ascites volume measurement: For all included patients, the abdominal CT imaging data obtained after admission were retrieved and analyzed using 3D Slicer software[16]. An appropriate segmentation plugin was used for preliminary segmentation of the ascites region within the abdominal cavity, after which the researcher reviewed the segmentation results slice by slice and manually corrected mis-segmented areas. After three-dimensional reconstruction, the total ascites volume was calculated. The measured ascites volume was then converted into ascites mass and used for subsequent body weight correction.
To evaluate the reliability of ascites volume measurement, CT images from 30 patients were randomly selected from all included cases, and two researchers independently performed ascites segmentation and volume measurement. The intraclass correlation coefficient (ICC) was used to evaluate inter-observer agreement. In addition, one of the researchers repeated the measurements on the same set of images from 20 cases after a certain time interval to evaluate intra-observer agreement.
CT-corrected method: Based on the ascites volume measured by abdominal CT, the ascites mass was calculated and subtracted from the actual body weight, after which the BMI was recalculated. The resulting value was defined as the CT-corrected BMI. The formula is as follows: BMI (CT) = [actual body weight (kg) – ascites mass (kg)]/height (m)2.
CT-sensitive corrected method: Based on the CT-corrected method, the body weight attributable to peripheral edema was further subtracted before the BMI calculation, and the resulting value was defined as the CT-sensitive corrected BMI. The formula is as follows: BMI (CT sensitive) = [actual body weight (kg) - ascites mass (kg) - peripheral edema correction (kg)]/height (m)2.
The severity of peripheral edema was determined according to the medical records. The European Association for the Study of the Liver Clinical Practice Guidelines on nutrition in chronic liver disease recommend estimating the dry body weight by subtracting 5%, 10%, or 15% of the measured body weight according to the severity of ascites, with an additional 5% subtraction when bilateral pedal edema is present[14]. Because the ascites mass in the present study was directly quantified using CT-based three-dimensional segmentation rather than estimated by a fixed percentage, the CT-sensitive corrected method additionally subtracted 5% of the body weight only in patients documented as having severe lower-limb edema. Mild or moderate lower-limb edema was not adjusted because of the lack of a validated quantitative correction factor in retrospective records. This method was used as a sensitivity correction to further reduce the potential effect of peripheral fluid retention on the BMI assessment.
According to the GLIM BMI criteria for Asian populations, a low BMI was defined by age stratification[10]: (1) Age < 70 years, BMI < 18.5 kg/m2; and (2) Age ≥ 70 years, BMI < 20.0 kg/m2.
Based on the BMI values calculated by the actual body weight method, CT-corrected method, and CT-sensitive corrected method, three low-BMI variables were generated: (1) Low BMI by the actual body weight method; (2) Low BMI by the CT-corrected method; and (3) Low BMI by the CT-sensitive corrected method.
The AMC was calculated using the following formula: AMC = AC - π × TSF, where AC represents the AC and TSF represents the TSF. Reduced muscle mass was defined according to the GLIM criteria: (1) AMC < 22.32 cm in men was considered reduced muscle mass; and (2) AMC < 18.90 cm in women was considered reduced muscle mass. For AMC data that could not be traced back to the original records, the values were treated as missing and were not replaced by estimated values or default normal values.
All patients included in this study had cirrhosis with ascites, which is considered a chronic disease associated with systemic inflammation. Therefore, all patients were considered to meet the disease burden/inflammation criterion in the etiologic domain of the GLIM framework[17,18]. Accordingly, malnutrition was mainly assessed based on the GLIM phenotypic criteria. The GLIM phenotypic criteria used in this study included: (1) Unintentional weight loss > 5% within the previous 6 months; (2) Low BMI; and (3) Reduced muscle mass (represented by reduced AMC). Malnutrition was considered present when any one of the three criteria listed above was met.
Three GLIM classification results were constructed based on the actual body weight method, the CT-corrected method, and the CT-sensitive corrected method, respectively: (1) GLIM_actual, GLIM classification based on the actual body weight method; (2) GLIM_CT, GLIM classification based on the CT-corrected method; and (3) GLIM_CT_sensitive, GLIM classification based on the CT-sensitive corrected method.
For the variable “unintentional weight loss > 5% within the previous 6 months”, documented weight-loss history was coded as 1 when the weight loss was > 5% and as 0 when the weight loss was not reported. In the primary analysis, patients without documented weight-loss information were coded as 0 based on the available medical records. The AMC data that could not be traced back to the original records were treated as missing and were not imputed. To assess the potential influence of missing phenotypic data, a sensitivity analysis was performed after restricting the cohort to patients with complete GLIM phenotypic data, defined as documented weight-loss history, available AMC data, and available BMI values under all three methods.
Three primary analyses were performed in this study: (1) Comparison of the differences among the actual BMI, CT-corrected BMI, and CT-sensitive corrected BMI to evaluate the effects of ascites and peripheral edema correction on BMI values; (2) Comparison of the differences in low BMI identification among the actual body weight method, CT-corrected method, and CT-sensitive corrected method to evaluate the effect of fluid retention on low-BMI classification; and (3) Comparison of the differences in the detection rates of GLIM-defined malnutrition among the three methods to evaluate the impact of BMI correction on malnutrition identification.
Three secondary analyses were performed in this study: (1) Evaluation of the agreement of the GLIM classification results among the different methods; (2) Analysis of the factors associated with low-BMI reclassification; and (3) Analysis of the factors associated with GLIM-defined malnutrition reclassification.
Reclassification was defined as follows: (1) Low-BMI reclassification was defined as inconsistent low-BMI classification for the same patient under different methods. Particular attention was paid to patients classified as having a non-low BMI by the actual body weight method but as having a low BMI by the CT-corrected method or the CT-sensitive corrected method; and (2) GLIM-defined malnutrition reclassification was defined as an inconsistent GLIM-defined malnutrition classification for the same patient under different methods. Particular attention was paid to patients classified as non-malnourished by the actual body weight method but as malnourished by the CT-corrected method or the CT-sensitive corrected method.
Statistical analyses were performed using R software. Continuous variables were first assessed for distributional characteristics. Variables with a normal distribution were expressed as the mean ± SD, whereas variables with a non-normal distribution were expressed as the median (Q1, Q3). Categorical variables were expressed as the number of cases and percentages. All tests were two-sided, and P < 0.05 was considered statistically significant. A sensitivity analysis restricted to patients with complete GLIM phenotypic data was performed to evaluate the robustness of the GLIM-defined malnutrition classification and reclassification. Because the number of reclassified patients was small, analyses of factors associated with reclassification were considered exploratory and hypothesis-generating rather than confirmatory. Paired comparisons among actual BMI, CT-corrected BMI, and CT-sensitive corrected BMI were performed using the Wilcoxon signed-rank test. The detection rates of a low BMI and GLIM-defined malnutrition among the actual body weight method, CT-corrected method, and CT-sensitive corrected method were compared using the McNemar test without continuity correction. Agreement in GLIM-defined malnutrition classification among the different methods was evaluated using the kappa test. Using the occurrence of low-BMI reclassification and GLIM-defined malnutrition reclassification as grouping variables, continuous variables were compared using the Wilcoxon rank-sum test, and categorical variables were compared using Fisher’s exact test in exploratory analyses.
A total of 100 patients with cirrhosis and ascites were included in this study. The general characteristics, laboratory parameters, clinical outcome-related variables, and nutrition-related variables of the study population are shown in Table 1.
| Variable | Value |
| Male | 73 (73.0) |
| Female | 27 (27.0) |
| Age, years | 52.00 (44.00, 63.00) |
| Body weight at admission, kg | 70.00 (60.00, 78.00) |
| Actual BMI, kg/m2 | 24.22 (21.85, 26.15) |
| White blood cell count, × 109/L | 2.73 (2.04, 3.75) |
| Lymphocyte count, × 109/L | 0.57 (0.38, 0.89) |
| Prealbumin, mg/L | 91.60 (62.92, 113.25) |
| Albumin, g/L | 25.94 (23.59, 30.29) |
| Total bilirubin, μmol/L | 18.30 (11.67, 30.56) |
| INR | 1.23 (1.10, 1.42) |
| Ascites volume, mL | 1513.37 (627.29, 2565.20) |
| Child-Pugh score | 8.00 (6.00, 9.00) |
| Length of hospital stay, days | 9.00 (7.00, 13.00) |
| Admission complications | 78 (78.0) |
| Nutritional intervention during hospitalization | 25 (25.0) |
| Readmission | 69 (69.0) |
| One-year mortality | 12/80 (15.0) |
| AMC (n = 85), cm | 23.29 (22.54, 24.17) |
| Reduced AMC | 8/85 (9.4) |
Paired comparisons were performed to evaluate the differences in BMI derived from the actual body weight method, the CT-corrected method, and the CT-sensitive corrected method. The median BMI values were 24.22 (21.85, 26.15), 23.67 (20.73, 25.48), and 23.39 (20.53, 25.48) kg/m2, respectively. Compared with the actual BMI, both the CT-corrected BMI and CT-sensitive corrected BMI were significantly lower (both P < 0.001), and the CT-sensitive corrected BMI was further lower than the CT-corrected BMI (P = 0.009) (Table 2, Figure 1).
| Outcome | Actual body weight method | CT-corrected method | CT-sensitive corrected method |
| BMI (kg/m2) | 24.22 (21.85, 26.15) | 23.67 (20.73, 25.48) | 23.39 (20.53, 25.48) |
| Low BMI | 6 (6.0) | 10 (10.0) | 12 (12.0) |
| GLIM-defined malnutrition | 19 (19.0) | 22 (22.0) | 24 (24.0) |
According to the GLIM BMI criteria for Asian populations with age stratification, the numbers of patients identified as having a low BMI by the actual body weight method, CT-corrected method, and CT-sensitive corrected method were 6 (6.0%), 10 (10.0%), and 12 (12.0%), respectively.
Paired 2 × 2 table analysis showed that, when the actual body weight method was compared with the CT-corrected method, the classification results were concordant in 96 patients, including 6 patients classified as having a low BMI by both methods and 90 patients classified as having a non-low BMI by both methods. In addition, 4 patients were reclassified from a non-low BMI by the actual body weight method to a low BMI by the CT-corrected method, and no reverse reclassification was observed. The McNemar test showed a statistically significant difference in the detection rates of a low BMI between the two methods (P = 0.046).
When the actual body weight method was further compared with the CT-sensitive corrected method, the classification results were concordant in 94 patients, including 6 patients classified as having a low BMI by both methods and 88 patients classified as having a non-low BMI by both methods. In addition, 6 patients were reclassified from a non-low BMI by the actual body weight method to a low BMI by the CT-sensitive corrected method, again with no reverse reclassification. The McNemar test showed a statistically significant difference in the detection rates of a low BMI between the two methods (P = 0.014, Figure 2A).
The numbers of patients classified as having GLIM-defined malnutrition by the actual body weight method, CT-corrected method, and CT-sensitive corrected method were 19 (19.0%), 22 (22.0%), and 24 (24.0%), respectively. Paired comparison showed that the classification results were concordant in 97 patients when the actual body weight method was compared with the CT-corrected method, including 19 patients classified as malnourished by both methods and 78 patients classified as non-malnourished by both methods. In addition, 3 patients were reclassified from non-malnourished by the actual body weight method to malnourished by the CT-corrected method, with no reverse reclassification observed. The McNemar test showed that the difference in the detection rates of GLIM-defined malnutrition between the two methods did not reach statistical significance (P = 0.083).
When the actual body weight method was further compared with the CT-sensitive corrected method, the classification results were concordant in 95 patients, including 19 patients classified as malnourished by both methods and 76 patients classified as non-malnourished by both methods. In addition, 5 patients were reclassified from non-malnourished by the actual body weight method to malnourished by the CT-sensitive corrected method, again with no reverse reclassification. The McNemar test showed that the CT-sensitive corrected method significantly increased the detection rate of GLIM-defined malnutrition compared with the actual body weight method (P = 0.025, Figure 2B).
Because the weight-loss history and AMC data were incomplete in some patients, a sensitivity analysis was performed to assess the potential influence of missing phenotypic data on GLIM-defined malnutrition classification. A documented weight-loss history was available for 72 patients, whereas 28 patients had no documented weight-loss information. AMC data were available for 85 patients, and 60 patients had complete GLIM phenotypic data. In this complete-case sensitivity analysis, the detection rates of GLIM-defined malnutrition using the actual body weight method, CT-corrected method, and CT-sensitive corrected method were 20.0% (12/60), 20.0% (12/60), and 21.7% (13/60), respectively. One patient was reclassified from non-malnourished to malnourished using the CT-sensitive corrected method, and no reverse reclassification was observed. The difference between the actual body weight method and the CT-sensitive corrected method did not reach statistical significance (P = 0.317), likely owing to the reduced sample size and small number of reclassified cases (Supplementary Table 1).
Kappa analysis was performed to evaluate agreement in GLIM-defined malnutrition classification across the different methods. The agreement rates between the actual body weight method and the CT-corrected method, between the actual body weight method and the CT-sensitive corrected method, and between the CT-corrected method and the CT-sensitive corrected method were 97.0%, 95.0%, and 98.0%, respectively, with corresponding kappa values of 0.908, 0.852, and 0.944 (Table 3). Overall, GLIM-defined malnutrition classification showed high agreement among the three methods.
| Comparison | Paired sample size | Observed agreement (%) | Kappa value |
| Actual body weight method vs CT-corrected method | 100 | 97.0 | 0.908 |
| Actual body weight method vs CT-sensitive corrected method | 100 | 95.0 | 0.852 |
| CT-corrected method vs CT-sensitive corrected method | 100 | 98.0 | 0.944 |
Low-BMI reclassification was defined as a discordant low-BMI classification between the actual body weight method and the CT-sensitive corrected method. Compared with the patients without low-BMI reclassification, those with reclassification had a lower actual BMI [19.53 (18.82, 20.68) kg/m2 vs 24.39 (22.77, 26.36) kg/m2, P = 0.002], a lower CT-corrected BMI [17.84 (17.55, 18.93) kg/m2 vs 23.71 (21.62, 25.52) kg/m2, P < 0.001], a lower CT-sensitive corrected BMI [17.84 (17.55, 18.10) kg/m2 vs 23.67 (21.55, 25.52) kg/m2, P < 0.001], a lower AMC [21.23 (20.92, 21.73) cm vs 23.54 (22.56, 24.26) cm, P = 0.025], and a larger ascites volume [5252.04 (3918.76, 6262.42) mL vs 1437.49 (612.18, 2327.16) mL, P = 0.001]. Lower-limb edema was also more common in the reclassification group (P = 0.009) (Table 4, Figure 3A).
| Variable | No reclassification (n = 94) | Reclassification (n = 6) | P value |
| Age, years | 52.00 (44.00, 63.00) | 52.50 (39.50, 62.50) | 0.722 |
| Sex (male/female) | 70/24 | 3/3 | 0.339 |
| Length of hospital stay, days | 11.00 (7.00, 15.00) | 11.50 (6.25, 14.50) | 0.716 |
| Actual BMI, kg/m2 | 24.39 (22.77, 26.36) | 19.53 (18.82, 20.68) | 0.002 |
| CT-corrected BMI, kg/m2 | 23.71 (21.62, 25.52) | 17.84 (17.55, 18.93) | < 0.001 |
| CT-sensitive corrected BMI, kg/m2 | 23.67 (21.55, 25.52) | 17.84 (17.55, 18.10) | < 0.001 |
| AMC, cm | 23.54 (22.56, 24.26) | 21.23 (20.92, 21.73) | 0.025 |
| Albumin, g/L | 33.20 (27.75, 39.10) | 33.55 (32.00, 34.80) | 0.845 |
| Total bilirubin, μmol/L | 17.80 (11.82, 30.40) | 19.10 (11.62, 121.52) | 0.706 |
| INR | 1.21 (1.10, 1.41) | 1.46 (1.17, 1.66) | 0.242 |
| Ascites volume, mL | 1437.49 (612.18, 2327.16) | 5252.04 (3918.76, 6262.42) | 0.001 |
| Liver function grade (A/B/C) | 26/52/16 | 1/3/2 | 0.621 |
| Severe lower-limb edema | 6 (6.4) | 3 (50.0) | 0.009 |
GLIM-defined malnutrition reclassification was defined as a discordant GLIM classification between the actual body weight method and the CT-sensitive corrected method. Compared with patients without GLIM-defined malnutrition reclassification, those with reclassification had a lower actual BMI [20.03 (19.03, 20.90) kg/m2 vs 24.34 (22.56, 26.32) kg/m2, P = 0.006], lower CT-corrected BMI [17.74 (17.49, 19.27) kg/m2 vs 23.70 (21.57, 25.51) kg/m2, P = 0.001], a lower CT-sensitive corrected BMI [17.74 (17.49, 18.16) kg/m2 vs 23.65 (21.44, 25.51) kg/m2, P = 0.001], and a larger ascites volume [6003.70 (4500.38, 6348.66) mL vs 1438.40 (617.12, 2324.45) mL, P = 0.001]. AMC also showed a lower trend in the reclassification group [21.42 (21.01, 21.82) cm vs 23.54 (22.54, 24.23) cm, P = 0.084], and lower-limb edema was more common in this group (P = 0.005) (Table 5, Figure 3B).
| Variable | No reclassification (n = 95) | Reclassification (n = 5) | P value |
| Age, years | 52.00 (44.00, 63.00) | 47.00 (37.00, 58.00) | 0.393 |
| Actual BMI, kg/m2 | 24.34 (22.56, 26.32) | 20.03 (19.03, 20.90) | 0.006 |
| CT-corrected BMI, kg/m2 | 23.70 (21.57, 25.51) | 17.74 (17.49, 19.27) | 0.001 |
| CT-sensitive corrected BMI, kg/m2 | 23.65 (21.44, 25.51) | 17.74 (17.49, 18.16) | 0.001 |
| AMC, cm | 23.54 (22.54, 24.23) | 21.42 (21.01, 21.82) | 0.084 |
| Albumin, g/L | 33.20 (27.80, 38.70) | 36.50 (32.00, 38.30) | 0.840 |
| Total bilirubin, μmol/L | 18.10 (11.77, 30.56) | 17.00 (13.80, 121.52) | 0.929 |
| INR | 1.22 (1.10, 1.41) | 1.46 (1.17, 1.75) | 0.174 |
| Ascites volume, mL | 1438.40 (617.12, 2324.45) | 6003.70 (4500.38, 6348.66) | 0.001 |
| Severe lower-limb edema | 6 (6.3) | 3 (60.0) | 0.005 |
To further assess the clinical relevance of GLIM-defined malnutrition reclassification, additional clinical and nutrition-related variables were compared between patients with and without reclassification. Compared with the non-reclassified patients, the reclassified patients had a significantly longer length of hospital stay [19.00 (16.00, 19.00) days vs 9.00 (6.00, 13.00) days, P = 0.003], lower prealbumin levels [57.00 (53.00, 62.10) mg/L vs 93.50 (63.70, 115.00) mg/L, P = 0.019], and lower albumin levels [20.31 (20.10, 21.02) g/L vs 26.39 (24.19, 30.47) g/L, P = 0.001]. No significant differences were observed in the white blood cell count, lymphocyte count, nutritional intervention during hospitalization, admission complications, readmission, or one-year mortality between the two groups (Table 6).
| Variable | Non-reclassified patients | Reclassified patients | P value |
| Length of hospital stay, days | 9.00 (6.00-13.00) | 19.00 (16.00-19.00) | 0.003 |
| White blood cell count, × 109/L | 2.68 (2.02-3.66) | 3.70 (2.85-3.94) | 0.181 |
| Lymphocyte count, × 109/L | 0.58 (0.38-0.90) | 0.45 (0.42-0.70) | 0.548 |
| Prealbumin, mg/L | 93.50 (63.70-115.00) | 57.00 (53.00-62.10) | 0.019 |
| Albumin, g/L | 26.39 (24.19-30.47) | 20.31 (20.10-21.02) | 0.001 |
| Nutritional intervention during hospitalization | 24/95 (25.3) | 1/5 (20.0) | 1.000 |
| Admission complications | 73/95 (76.8) | 5/5 (100.0) | 0.583 |
| Readmission | 67/95 (70.5) | 2/5 (40.0) | 0.171 |
| One-year mortality | 11/76 (14.5) | 1/4 (25.0) | 0.485 |
Reliability analysis was performed to evaluate the ascites volume measurement method. In 20 randomly selected cases, repeated measurements by the same researcher showed excellent intra-observer agreement, with an ICC of 1.000 (95% confidence interval: 0.999-1.000, P < 0.001). In another 30 randomly selected cases, independent measurements by two researchers showed excellent inter-observer agreement, with an ICC of 0.999 (95% confidence interval: 0.999-1.000, P < 0.001). These findings support the excellent repeatability and agreement of the CT-based three-dimensional segmentation method for ascites volume measurement.
Patients with cirrhosis are at high risk of malnutrition, particularly in the decompensated stage[19,20]. Multiple factors, including insufficient energy intake, impaired absorption, metabolic abnormalities, chronic inflammation, muscle loss, ascites, abdominal distension, early satiety, and recurrent hospitalization, contribute to malnutrition and reduced nutritional intake in this population[21-24]. Because malnutrition is associated with adverse outcomes in patients with advanced cirrhosis, accurate nutritional assessment is clinically important[25-29]. However, nutritional assessment in patients with cirrhosis and ascites remains challenging because the BMI may be influenced by fluid retention[30,31]. Ascites and peripheral edema may cause “non-nutritional” weight gain, leading to overestimation of the BMI and potential underrecognition of a low BMI or malnutrition[32]. Therefore, assessment of ascites burden and appropriate body weight adjustment are important issues in the nutritional evaluation of cirrhosis, although no unified standard for quantitative ascites assessment has been established[33,34].
In the present study, CT-based correction of body weight reduced the BMI estimates and increased the detection of a low BMI in patients with cirrhosis and ascites. The CT-sensitive corrected method, which further accounted for severe lower-limb edema, also increased the detection of GLIM-defined malnutrition compared with the actual body weight method. These findings suggest that the BMI calculated from the actual body weight may mask potential nutritional risk in some patients with fluid retention and that imaging-based correction may help identify a subgroup of patients whose BMI classification is affected by ascites and peripheral edema.
Importantly, these findings should not be interpreted as definitive evidence that CT-corrected classification is more accurate in all patients. Because the ascites mass is mathematically subtracted from the body weight, a reduction in the BMI is expected. The clinical value of this correction lies in identifying patients whose classification may change because of fluid retention, especially those with large-volume ascites, severe lower-limb edema, or BMI values close to the diagnostic threshold. Therefore, CT-based correction should be considered a supplementary approach rather than a replacement for conventional nutritional assessment.
The BMI remains widely used in clinical practice because it is simple and easily available. However, its limitations are evident in patients with cirrhosis and ascites[35]. In this study, all low-BMI reclassification events were unidirectional, from non-low BMI to low BMI, with no reverse reclassification. This suggests that conventional body weight assessment may underrecognize a low BMI in some patients with cirrhosis and fluid retention. In addition, the CT-sensitive corrected BMI was lower than the CT-corrected BMI, indicating that peripheral edema may further influence BMI classification, particularly in patients near the low-BMI threshold.
The GLIM criteria are widely used for malnutrition diagnosis and incorporate both etiologic and phenotypic criteria[36-38]. Patients with cirrhosis and ascites generally meet the etiologic criterion because of their chronic disease burden and inflammation; therefore, accurate assessment of phenotypic criteria is particularly important. Our findings indicate that the low-BMI component of the GLIM framework may be influenced by the presence of ascites and peripheral edema. Although overall agreement among the three GLIM classification methods remained high, a small subgroup of patients was reclassified only after correction, suggesting that relying solely on the actual body weight may underestimate GLIM-defined malnutrition in some cases.
These findings support the concept of “supplementary correction”: CT-based body weight correction may help identify patients close to the diagnostic threshold whose BMI-based nutritional classification may be affected by fluid retention. Therefore, this approach may be most relevant for patients with marked ascites, severe edema, or borderline BMI values, rather than being necessary for all patients with cirrhosis and ascites.
The exploratory analyses of reclassification factors provided preliminary support for this interpretation. Patients with a low BMI or GLIM-defined malnutrition reclassification tended to have lower BMI values, a larger ascites volume, and more frequent severe lower-limb edema. The AMC was also lower or showed a decreasing trend in the reclassification groups. These findings suggest that reclassification was more likely to occur in patients who were already close to the malnutrition threshold and had evident fluid retention. However, because only 6 patients were reclassified by a low BMI and 5 by GLIM-defined malnutrition, these results should be interpreted as exploratory and hypothesis-generating rather than confirmatory.
The additional clinical relevance analysis further suggested that GLIM-defined malnutrition reclassification was not merely a mathematical consequence of subtracting the ascites mass from the body weight. Patients reclassified by the CT-sensitive corrected method had significantly longer hospital stays and lower prealbumin and albumin levels than the non-reclassified patients. These findings suggest that CT-sensitive BMI correction may identify a subgroup of patients with potential nutritional vulnerability whose BMI classification is affected by fluid retention. However, no significant differences were observed in nutritional intervention, readmission, or one-year mortality, which may be related to the small number of reclassified patients and the retrospective study design.
A strength of this study is the use of CT-based three-dimensional segmentation to quantify the ascites volume. The excellent intra-observer and inter-observer agreement supports the repeatability of this measurement workflow and provides a methodological basis for body weight correction based on the ascites volume. Nevertheless, whether CT-based correction improves prognostic prediction or guides nutritional intervention requires further validation in larger prospective studies.
This study has several limitations. First, this was a single-center retrospective study with a relatively small sample size, which may have introduced selection bias and limited the generalizability of the findings. Although several clinical outcome-related variables were additionally analyzed, including length of hospital stay, admission complications, readmission, and one-year mortality, the number of reclassified patients was small and the follow-up information was limited. Therefore, the prognostic value of CT-corrected or CT-sensitive corrected BMI requires further validation in larger multicenter prospective studies with standardized long-term follow-up.
Second, reduced muscle mass was represented by a reduced AMC. Although determination of the AMC is clinically feasible, it is less precise than imaging-based indicators such as the CT-derived skeletal muscle area or the skeletal muscle index. Thus, GLIM classification in this study may still have been influenced by the choice of the phenotypic indicator.
Third, the correction for peripheral edema was relatively crude. Although the additional 5% body-weight subtraction for peripheral edema was based on guideline recommendations for dry-weight estimation, edema severity in this retrospective study was determined from medical record documentation rather than standardized prospective assessment. Therefore, subjective documentation and inter-physician variability in grading lower-limb edema may have affected the reliability of the CT-sensitive correction. Moreover, mild and moderate edema were not quantitatively adjusted because no validated correction factor was available in the retrospective records. Future prospective studies should apply standardized edema grading and evaluate more objective methods for quantifying peripheral fluid retention.
Fourth, this study did not establish clear clinical indications for when CT-based correction should be applied. Although reclassified patients tended to have a larger ascites volume, more frequent severe lower-limb edema, and BMI values close to the low-BMI threshold, the present study was not designed to determine specific cut-off values for ascites volume, edema severity, or baseline BMI that would mandate CT-based correction. Therefore, CT-based BMI correction should currently be considered a supplementary approach for patients with marked fluid retention or borderline BMI values, rather than a universally required procedure. Future studies should further define practical indications and decision thresholds for CT-based correction.
Finally, although the ascites volume measurement showed excellent agreement, the workflow still required slice-by-slice review and manual correction, which may limit reproducibility across centers and operators. More standardized segmentation workflows or automated algorithms may improve clinical applicability in future studies.
In patients with cirrhosis and ascites, BMI based on the actual body weight may be overestimated because of fluid retention, leading to under recognition of a low BMI and malnutrition. CT-based body weight correction, particularly with additional consideration of severe lower-limb edema, may improve nutritional risk identification while maintaining high overall agreement. Imaging-corrected BMI may therefore provide a useful supplementary tool for nutritional assessment in patients with marked fluid retention.
The authors thank the clinical staff and radiology staff of the Affiliated Hospital of Chengde Medical University for their support in data collection and imaging evaluation.
| 1. | Mak LY, Liu K, Chirapongsathorn S, Yew KC, Tamaki N, Rajaram RB, Panlilio MT, Lui R, Lee HW, Lai JC, Kulkarni AV, Premkumar M, Lesmana CRA, Hsu YC, Huang DQ. Liver diseases and hepatocellular carcinoma in the Asia-Pacific region: burden, trends, challenges and future directions. Nat Rev Gastroenterol Hepatol. 2024;21:834-851. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 130] [Cited by in RCA: 126] [Article Influence: 63.0] [Reference Citation Analysis (0)] |
| 2. | Biggins SW, Angeli P, Garcia-Tsao G, Ginès P, Ling SC, Nadim MK, Wong F, Kim WR. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2021;74:1014-1048. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 690] [Cited by in RCA: 596] [Article Influence: 119.2] [Reference Citation Analysis (3)] |
| 3. | Uschner FE, McCoy M, Tyc O, Gu W, Ferstl P, Brol MJ, Masseli J, Peiffer KH, Finkelmeier F, Zeuzem S, Biguenet S, Staufer K, Kabbaj M, Trebicka J. Safety, pharmacokinetics, and preliminary efficacy of VS-01, an intraperitoneal liposomal infusion, in patients with decompensated liver cirrhosis, ascites, and covert hepatic encephalopathy: a phase 1b, first-in-human, open-label, non-randomised, single ascending and multiple dose study. Lancet Gastroenterol Hepatol. 2026;11:299-313. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 1] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
| 4. | Zeng J, Gu C, Wen C, Shen C. The burden of NAFLD (now referred to as MASLD)-related chronic liver disease and cirrhosis from 1990 to 2021 with projections to 2036: a comparative study of global China the United States and India. Lipids Health Dis. 2025;24:298. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 7] [Reference Citation Analysis (0)] |
| 5. | Lai JC, Tandon P, Bernal W, Tapper EB, Ekong U, Dasarathy S, Carey EJ. Malnutrition, Frailty, and Sarcopenia in Patients With Cirrhosis: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2021;74:1611-1644. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 575] [Cited by in RCA: 542] [Article Influence: 108.4] [Reference Citation Analysis (2)] |
| 6. | Romeo M, Dallio M, Cipullo M, Coppola A, Mazzarella C, Mammone S, Iadanza G, Napolitano C, Vaia P, Ventriglia L, Federico A. Nutritional and Psychological Support as a Multidisciplinary Coordinated Approach in the Management of Chronic Liver Disease: A Scoping Review. Nutr Rev. 2025;83:1327-1343. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 4] [Reference Citation Analysis (0)] |
| 7. | Singal AK, Wong RJ, Dasarathy S, Abdelmalek MF, Neuschwander-Tetri BA, Limketkai BN, Petrey J, McClain CJ. ACG Clinical Guideline: Malnutrition and Nutritional Recommendations in Liver Disease. Am J Gastroenterol. 2025;120:950-972. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 40] [Cited by in RCA: 43] [Article Influence: 43.0] [Reference Citation Analysis (2)] |
| 8. | GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2133-2161. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 3925] [Cited by in RCA: 3386] [Article Influence: 1693.0] [Reference Citation Analysis (4)] |
| 9. | Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, Muscaritoli M, Nyulasi I, Ockenga J, Schneider SM, de van der Schueren MA, Singer P. Diagnostic criteria for malnutrition - An ESPEN Consensus Statement. Clin Nutr. 2015;34:335-340. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1401] [Cited by in RCA: 1262] [Article Influence: 114.7] [Reference Citation Analysis (7)] |
| 10. | Jensen GL, Cederholm T, Correia MITD, Gonzalez MC, Fukushima R, Pisprasert V, Blaauw R, Braz DC, Carrasco F, Cruz Jentoft AJ, Cuerda C, Evans DC, Fuchs-Tarlovsky V, Gramlich L, Shi HP, Hasse JM, Hiesmayr M, Hiki N, Jager-Wittenaar H, Jahit S, Jáquez A, Keller H, Klek S, Malone A, Mogensen KM, Mori N, Mundi M, Muscaritoli M, Ng D, Nyulasi I, Pirlich M, Schneider S, de van der Schueren M, Siltharm S, Singer P, Steiber A, Tappenden KA, Yu J, van Gossum A, Wang JY, Winkler MF, Compher C, Barazzoni R. GLIM consensus approach to diagnosis of malnutrition: A 5-year update. JPEN J Parenter Enteral Nutr. 2025;49:414-427. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 23] [Cited by in RCA: 41] [Article Influence: 41.0] [Reference Citation Analysis (0)] |
| 11. | Wang Z, Cao Y, He Y, Hao M, Wu S, Li L, Wang Q, Sun X, Wu L. The global leadership initiative on malnutrition criteria for the diagnosis of malnutrition in patients with chronic liver diseases: a systematic review and meta-analysis. Front Nutr. 2025;12:1612417. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 2] [Reference Citation Analysis (0)] |
| 12. | Haj Ali S, Abu Sneineh A, Hasweh R. Nutritional assessment in patients with liver cirrhosis. World J Hepatol. 2022;14:1694-1703. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 25] [Cited by in RCA: 18] [Article Influence: 4.5] [Reference Citation Analysis (0)] |
| 13. | Bartlett S, Yiu TH, Valaydon Z. Nutritional assessment of patients with liver cirrhosis in the outpatient setting: A narrative review. Nutrition. 2025;132:112675. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 3] [Reference Citation Analysis (0)] |
| 14. | Oriuchi N, Nakajima T, Mochiki E, Takeyoshi I, Kanuma T, Endo K, Sakamoto J. A new, accurate and conventional five-point method for quantitative evaluation of ascites using plain computed tomography in cancer patients. Jpn J Clin Oncol. 2005;35:386-390. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 45] [Cited by in RCA: 44] [Article Influence: 2.1] [Reference Citation Analysis (2)] |
| 15. | European Association for the Study of the Liver. EASL Clinical Practice Guidelines on nutrition in chronic liver disease. J Hepatol. 2019;70:172-193. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 900] [Cited by in RCA: 800] [Article Influence: 114.3] [Reference Citation Analysis (5)] |
| 16. | Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30:1323-1341. [RCA] [PubMed] [DOI] [Full Text] [Reference Citation Analysis (0)] |
| 17. | Schuetz P, Seres D, Lobo DN, Gomes F, Kaegi-Braun N, Stanga Z. Management of disease-related malnutrition for patients being treated in hospital. Lancet. 2021;398:1927-1938. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 114] [Cited by in RCA: 241] [Article Influence: 48.2] [Reference Citation Analysis (1)] |
| 18. | Jensen GL, Cederholm T, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, de Baptista GA, Barazzoni R, Blaauw R, Coats AJS, Crivelli A, Evans DC, Gramlich L, Fuchs-Tarlovsky V, Keller H, Llido L, Malone A, Mogensen KM, Morley JE, Muscaritoli M, Nyulasi I, Pirlich M, Pisprasert V, de van der Schueren M, Siltharm S, Singer P, Tappenden KA, Velasco N, Waitzberg DL, Yamwong P, Yu J, Compher C, Van Gossum A. GLIM Criteria for the Diagnosis of Malnutrition: A Consensus Report From the Global Clinical Nutrition Community. JPEN J Parenter Enteral Nutr. 2019;43:32-40. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 598] [Cited by in RCA: 505] [Article Influence: 72.1] [Reference Citation Analysis (4)] |
| 19. | Plauth M, Bernal W, Dasarathy S, Merli M, Plank LD, Schütz T, Bischoff SC. ESPEN guideline on clinical nutrition in liver disease. Clin Nutr. 2019;38:485-521. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 553] [Cited by in RCA: 459] [Article Influence: 65.6] [Reference Citation Analysis (4)] |
| 20. | Wang S, Limon-Miro AT, Cruz C, Tandon P. CAQ Corner: The practical assessment and management of sarcopenia, frailty, and malnutrition in patients with cirrhosis. Liver Transpl. 2023;29:103-113. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 4] [Cited by in RCA: 6] [Article Influence: 2.0] [Reference Citation Analysis (1)] |
| 21. | Tandon P, Montano-Loza AJ, Lai JC, Dasarathy S, Merli M. Sarcopenia and frailty in decompensated cirrhosis. J Hepatol. 2021;75 Suppl 1:S147-S162. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 333] [Cited by in RCA: 312] [Article Influence: 62.4] [Reference Citation Analysis (3)] |
| 22. | Nishikawa H, Enomoto H, Nishiguchi S, Iijima H. Sarcopenic Obesity in Liver Cirrhosis: Possible Mechanism and Clinical Impact. Int J Mol Sci. 2021;22:1917. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 30] [Cited by in RCA: 37] [Article Influence: 7.4] [Reference Citation Analysis (0)] |
| 23. | Lalama MA, Saloum Y. Nutrition, fluid, and electrolytes in chronic liver disease. Clin Liver Dis (Hoboken). 2016;7:18-20. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 11] [Cited by in RCA: 11] [Article Influence: 1.1] [Reference Citation Analysis (0)] |
| 24. | Elsabaawy M, Alhaddad O. Forgettable in the care of liver cirrhosis: the unseen culprits of progression from bad to worse. Prz Gastroenterol. 2024;19:6-17. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 1] [Reference Citation Analysis (0)] |
| 25. | Yang W, Guo G, Cui B, Li Y, Sun M, Li C, Wang X, Mao L, Hui Y, Fan X, Jiang K, Sun C. Malnutrition according to the Global Leadership Initiative on Malnutrition criteria is associated with in-hospital mortality and prolonged length of stay in patients with cirrhosis. Nutrition. 2023;105:111860. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 12] [Cited by in RCA: 19] [Article Influence: 6.3] [Reference Citation Analysis (0)] |
| 26. | Parola I, Savulescu-Fiedler I, Bucurica S, Maniu I, Cheaib B, Jinga M. Real-Life Challenges in Assessing Nutritional Status and Quality of Life in Patients with Cirrhosis. Diagnostics (Basel). 2025;15:3206. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 1] [Reference Citation Analysis (0)] |
| 27. | Hsu HC, Chow LH, Chen YL, Hung HM, Yen M, Lee HF. Effects of exercise and nutrition in improving sarcopenia in liver cirrhosis patients: a systematic review and meta-analysis. Hepatobiliary Surg Nutr. 2025;14:33-48. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 5] [Cited by in RCA: 13] [Article Influence: 13.0] [Reference Citation Analysis (0)] |
| 28. | He Y, Wang Z, Wu S, Li L, Li J, Zhang Y, Chen B, Sun X, Sun C, Wu L. Screening and assessment of malnutrition in patients with liver cirrhosis. Front Nutr. 2024;11:1398690. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 9] [Reference Citation Analysis (0)] |
| 29. | Dong Y, Chen PY, You C, Li JD, Chen ST. Research progress on nutritional support for patients with liver cirrhosis complicated by upper gastrointestinal bleeding. Front Nutr. 2025;12:1727092. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 1] [Reference Citation Analysis (4)] |
| 30. | Wang R, Huang L, Xu M, Yu X, Wang H. Comparison of different nutritional screening tools in nutritional screening of patients with cirrhosis: A cross-sectional observational study. Heliyon. 2024;10:e30339. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 3] [Reference Citation Analysis (0)] |
| 31. | Zhang P, Wang Q, Zhu M, Li P, Wang Y. Differences in nutritional risk assessment between NRS2002, RFH-NPT and LDUST in cirrhotic patients. Sci Rep. 2023;13:3306. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 17] [Cited by in RCA: 17] [Article Influence: 5.7] [Reference Citation Analysis (2)] |
| 32. | Alves BC, Luchi-Cruz MM, Lopes AB, Saueressig C, Dall'Alba V. Predicting dry weight in patients with cirrhotic ascites undergoing large-volume paracentesis. Clin Nutr ESPEN. 2023;54:34-40. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 4] [Reference Citation Analysis (0)] |
| 33. | Molfino A, Johnson S, Medici V. The Challenges of Nutritional Assessment in Cirrhosis. Curr Nutr Rep. 2017;6:274-280. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 11] [Cited by in RCA: 15] [Article Influence: 1.7] [Reference Citation Analysis (0)] |
| 34. | Tandon P, Raman M, Mourtzakis M, Merli M. A practical approach to nutritional screening and assessment in cirrhosis. Hepatology. 2017;65:1044-1057. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 236] [Cited by in RCA: 199] [Article Influence: 22.1] [Reference Citation Analysis (1)] |
| 35. | Tuo S, Yeo YH, Chang R, Wen Z, Ran Q, Yang L, Fan Q, Kang J, Si J, Liu Y, Shi H, Li Y, Yuan J, Liu N, Dai S, Guo X, Wang J, Ji F, Tantai X. Prevalence of and associated factors for sarcopenia in patients with liver cirrhosis: A systematic review and meta-analysis. Clin Nutr. 2024;43:84-94. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 27] [Cited by in RCA: 25] [Article Influence: 12.5] [Reference Citation Analysis (1)] |
| 36. | Casas Deza D, Betoré Glaria ME, Sanz-París A, Lafuente Blasco M, Fernández Bonilla EM, Bernal Monterde V, Arbonés Mainar JM, Fuentes Olmo J. Mini Nutritional Assessment - Short Form Is a Useful Malnutrition Screening Tool in Patients with Liver Cirrhosis, Using the Global Leadership Initiative for Malnutrition Criteria as the Gold Standard. Nutr Clin Pract. 2021;36:1003-1010. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 5] [Cited by in RCA: 23] [Article Influence: 4.6] [Reference Citation Analysis (0)] |
| 37. | Jiang M, Chen J, Wu M, Wu J, Xu X, Li J, Liu C, Zhao Y, Hua X, Meng Q. Application of Global Leadership Initiative on Malnutrition criteria in patients with liver cirrhosis. Chin Med J (Engl). 2024;137:97-104. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 7] [Reference Citation Analysis (0)] |
| 38. | Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, Baptista G, Barazzoni R, Blaauw R, Coats AJS, Crivelli AN, Evans DC, Gramlich L, Fuchs-Tarlovsky V, Keller H, Llido L, Malone A, Mogensen KM, Morley JE, Muscaritoli M, Nyulasi I, Pirlich M, Pisprasert V, de van der Schueren MAE, Siltharm S, Singer P, Tappenden K, Velasco N, Waitzberg D, Yamwong P, Yu J, Van Gossum A, Compher C; GLIM Core Leadership Committee, GLIM Working Group. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. J Cachexia Sarcopenia Muscle. 2019;10:207-217. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 795] [Cited by in RCA: 761] [Article Influence: 108.7] [Reference Citation Analysis (1)] |