Comparison of Patient-Generated Subjective Global Assessment (PG-SGA) and Mini Nutritional Assessment (MNA) for nutritional assessment in Hepatocellular carcinoma patients

doi:10.21203/rs.3.rs-4190569/v1

Background

Malnutrition affects the prognosis and response to treatment in cancer patients. There is no gold standard for nutritional assessment in patients with hepatocellular carcinoma (HCC). This study aimed to compare Patient-Generated Subjective Global Assessment (PG-SGA) and Mini Nutritional Assessment (MNA) in predicting mortality in HCC patients.

Method

We included HCC patients in outpatient clinic at HRH Maha Chakri Sirindhorn Medical Center. The nutritional status was assessed according to PG-SGA and MNA. Patients were followed up 1 year to verify the incidence of death and complications.

Result

Eighty-nine HCC patients were included. Mean age was 62.2 years. Most of the patients were treated with transarterial chemoembolization. Malnutrition identified by PG-SGA and MNA was 53.9% and 51.7%, respectively. Mortality rate was 2.65 cases per 100 persons. Overall survival rate was 90% and 81.5% at 12 and 24 months, respectively. Patients with malnutrition assessed by PG-SGA and MNA had significantly higher mortality than patients without malnutrition. PG-SGA had a sensitivity of 80% for predicting mortality. PG-SGA had higher accuracy for predicting mortality of HCC patients than MNA (AUROC PG-SGA 0.7148 and MNA 0.7098).

Conclusion

HCC patients with malnutrition were evaluated by PG-SGA and MNA had higher mortality than HCC patients without malnutrition. PG-SGA had higher accuracy in predicting mortality than MNA.

Patient-Generated Subjective Global Assessment

Mini Nutritional Assessment

nutritional assessment

Hepatocellular carcinoma

Malnutrition defines as deficiencies, excesses or imbalance of intake of energy and/or nutrients. As per WHO guidelines, malnutrition encompasses 3 categories; undernutrition, micronutrient malnutrition and overweight/obesity. Malnutrition in cancer patients is reported up to 20–60%.^(1–5) Prevalence of malnutrition depends on type and stage of cancer.^{(6, 7)} Inadequate intake, decrease of muscle protein as well as activated systemic inflammation syndrome are observed in cancer patients.⁽⁸⁾ Mechanical and functional alteration from tumor, catabolic hormones, cytokines, personal habits, physical deterioration, anorexia, psychological factors and side effects of treatment are the potential causes of malnutrition.⁽⁶⁾ Malnutrition is associated with adverse outcomes including increased infection, treatment toxicity, mortality, healthcare costs, and prolonged hospital length of stay.^{(2, 7, 9, 10)} ESPEN guideline recommended Nutrition Risk Screening 2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), Malnutrition Screening Tool (MST) and Mini Nutritional Assessment Short From Revised (MNA-SF) as the nutritional screening tools in cancer patients.^{(8, 11)} In patients identified as at risk for malnutrition, Subjective Global Assessment (SGA), Patient-Generated Subjective Global Assessment (PG-SGA) and Minimal Nutrition Assessment (MNA) were frequently used as the comprehensive nutritional assessment tools.^{(8, 12)} However, nutritional assessment is often overlooked.

Liver cancer is the fifth most frequent cancer death in men and the seventh most frequent cancer death in women.⁽¹³⁾ Hepatocellular carcinoma (HCC) is the most common primary liver cancer. The accompanying cirrhosis in most of the cases and the nutritional requirements derived from cancer, thus patients with HCC are at higher risk of malnutrition.⁽¹⁴⁾ There is no gold standard tool for assessing nutritional status in HCC and cirrhotic patients. The aim of the study is to compare PG-SGA and MNA for predicting mortality in HCC patients.

Study design and participants

Prospective cohort study was conducted at HRH Maha Chakri Sirindhorn Medical Center, an academic tertiary care center in Thailand. Patients were enrolled between May 2019 and May 2020. Informed consent was obtained from all patients and the university ethics committee approved the study protocol. (SWUEC-026/2562E)

The inclusion criteria were patients over the age of 18 years who diagnosed hepatocellular carcinoma (HCC) according to EASL guideline.⁽¹⁵⁾ and who signed the informed consent. The exclusion criteria were (1) incorporated patients (2) grade 3 and 4 hepatic encephalopathy or fulminant hepatitis (3) active cardiac or pulmonary disease, cerebrovascular disease, malabsorption, chronic pancreatitis, HIV infection, and psychiatric disease (4) pregnancy and lactation.

Procedure

Demographics, clinical and laboratory data were collected. Severity of cirrhosis was classified according to the criteria proposed by Child-Pugh score and Model of End Stage Liver disease (MELD). HCC was categorized as the Barcelona Clinic Liver Cancer (BCLC) staging system.⁽¹⁶⁾

Nutritional status was assessed by Patient Generated-Subjective Global Assessment (PG-SGA) and Mini-Nutritional Assessment (MNA).^{(17, 18)} PG-SGA comprised of two compartments; the patient-generated component and the professional compartment. The patient-generated component included weight history, food intake, symptoms, activities and function. The professional compartment includes diagnosis, metabolic demand as well as physical exam. The total numerical score was derived. PG-SGA classified nutritional status into 3 groups; well-nourished (A), moderate or suspected malnutrition (B) and severely malnourished (C). MNA comprised of anthropometric assessment, general assessment, dietary assessment and self-assessment. MNA classified nutritional status into 3 groups; well-nourished, at risk of malnutrition and severely malnourished. In this study, malnutrition defined as PG-SGA B or C or MNA showed a risk of malnutrition or severely malnourished.

They were followed up for 1 year after the last patients included. Data regarding mortality and complications were collected.

Statistical analysis

Categorical variables were presented as frequency and percent; continuous variables were expressed as mean ± SD or median with interquatile range. To evaluate PG-SGA and MNA performance characteristics, likelihood ratios and ROC analysis were used. Overall survival was estimated via the Kaplan-Meier method and compared between groups with a log-rank test (SAS version 9.3; SAS Institute).

From the Martin study⁽¹⁹⁾, hazard ratio of PG-SGA in cancer patients was 2.08. The sample size calculation consisted of 89 patients, considering an error margin of 5%, power of 80% and a dropout rate of 30%.

Eighty-nine HCC patients were recruited. Most of patients were men (83.1%) and mean age was 62.2 years. Two patients had a history of breast cancer and one patient had a history of squamous cell carcinoma of lower lip. Most of patients had cirrhosis and only two patients were chronic hepatitis B without cirrhosis. Hepatitis B was the common cause of cirrhosis. Patients with Child-Pugh score A, B and C were 66, 15 and 5, respectively. Mean MELD score was 10.8±4.8. Median duration of HCC was 16 (4-30) months. Most of the patients were BCLC intermediate (stage 2) and treated with transarterial chemoembolization. (Table 1) Previous history of ascites, gastrointestinal bleeding and hepatic encephalopathy were 18, 12, and 1 patient, respectively. At enrollment, 14 patients had ascites, 2 patients had gastrointestinal bleeding, and 1 patient had hepatic encephalopathy.

Survived patients had higher body mass index, albumin and lower total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), international normalized ratio (INR), creatinine, and alpha-fetoprotein (AFP). (Table 1)

Table 1 Baseline characteristic

Characteristic	Total (n=89)	Survival (n=54)	Death (n=35)
Age, SD	62.2, 11.1	63.4, 11.5	60.3, 10.3
Sex, M/F	74/15	43/11	31/4
BMI, SD	23.6, 3.6	24.1, 3.7	22.9, 3.3
Smoking/Alcohol	48/42	25/21	23/21
Cirrhosis	87	52	35
Cause of cirrhosis
Alcohol	9	5	4
Hepatitis B	33	22	11
Hepatitis C	17	11	6
NAFLD	4	2	2
Cryptogenic	2	0	2
Combined	19	9	10
CTP score A/B/C	66/16/5	48/3/1	18/13/4
MELD, SD	10.8, 4.8	9.0, 2.5	13.4, 6.1
BCLC staging 0/1/2/3/4	5/27/44/11/2	5/21/26/2/0	0/6/18/9/2
Types of treatment
Surgery	5	4	1
Radiofrequency ablation	5	4	1
TACE	53	34	19
Chemotherapy	2	0	2
Best supportive care	3	0	3
Combined	21	12	9
Laboratory results
TB, SD	1.7, 2.8	1.0, 0.6	2.7, 4.2
ALT, SD	45.5, 30.8	44.0, 23.3	47.9, 39.9
AST, SD	75.6, 67.6	55.7, 35.8	106.1, 90.8
Alb, SD	3.6, 0.7	3.9, 0.6	3.2, 0.7
INR, SD	1.2, 0.3	1.1, 0.1	1.3, 0.3
Cr, SD	1.1, 0.6	1.0, 0.4	1.3, 0.8
AFP	10.5 (4.3-91.3)	6.3 (2.6-19.9)	508.5 (6.1-12666)

BMI body mass index; NAFLD nonalcoholic fatty liver disease; CTP Child Turcott Puge; MELD Model of End Stage Liver disease; BCLC Barcelona Clinic Liver Cancer; TACE transarterial chemoembolization; TB total bilirubin; AST aspartate aminotransferase; ALT alanine aminotransferase, alb albumin; INR international normalized ratio; Cr creatinine; AFP alpha-fetoprotein

Mean PG-SGA score was 6.6±5.9. Patients with PG-SGA A, B and C were 46.1, 29.2 and 24.7%, respectively. Mean MNA score was 22.8±3.8. Patients classified as well-nourished, at risk of malnutrition and malnourished by MNA were 48.3, 41.6 and 10.1, respectively. Malnutrition identified by PG-SGA and MNA were 53.9% and 51.7%, respectively. (Table 2)

Table 2 Malnutrition assessed by PG-SGA and MNA

Characteristic	Total (n=89)	Survival (n=35)	Death (n=54)
PG-SGA score, SD	6.6, 5.9	4.0, 3.8	10.5, 6.4
PG-SGA
A	41	34	7
B	26	12	14
C	22	8	14
PG-SGA malnutrition	48	20	28
MNA score, SD	22.8, 3.8	24.1, 3.3	20.8, 3.8
MNA
Well-nourished	43	35	8
At risk of malnutrition	37	17	20
Malnourished	9	2	7
MNA malnutrition	46	19	27

PG-SGA Patient-Generated Subjective Global Assessment; MNA Mini Nutritional Assessment

Thirty patients (85.7%) died over a 1-year period. Thirty-seven (41.6%) patients developed complication and need hospitalization (decompensation 18 patients, spontaneous bacterial peritonitis 6 patients, upper GI bleeding in 6 patients, rupture in 3 patients, and acute kidney injury in 29 patients). The incidence of mortality was 2.65 cases per 100 persons (95%CI 1.87-3.75). Overall survival rate was 68.6% (95%CI 57.66-77.27) and 59.23% (95%CI 46.29-70.03) at 12 and 24 months, respectively. (Fig 1) Patients with malnutrition assessed by PG-SGA and MNA had higher mortality than patients without malnutrition (p<0.001 and 0.001). (Fig 2 and 3)

Compared to MNA, PG-SGA indicated higher sensitivity and higher negative predictive value (NPV) for predicting mortality. (Table 3) PG-SGA had higher accuracy for predicting mortality of HCC patients than MNA (AUROC PG-SGA=0.7148 and MNA=0.7098). (Fig 4)

Table 3 Sensitivity, specificity, positive predictive value and negative predictive value

	Sensitivity	Specificity	PPV	NPV
PG-SGA	80 (71.7-88.3)	63.0 (52.9-73.0)	58.3 (48.1-68.6)	82.9 (75.1-90.7)
MNA	77.1 (68.4-85.9)	64.8 (54.9-74.7)	58.7 (48.5-68.9)	81.4 (73.3-89.5)

PPV positive predictive value; NPV negative predictive value; PG-SGA Patient-Generated Subjective Global Assessment; MNA Mini Nutritional Assessment

Study of prevalence of malnutrition in HCC patients is scarce. Varied prevalence among different nutritional assessment in patients with HCC are ranging from 11.6 to 58.1%.⁽²⁰⁾ In current study, malnutrition of HCC patients assessed by PG-SGA and MNA was 53.9% and 51.7%, respectively. Weight loss is a prognostic indicator for overall survival in cancer patients. Nonetheless, BMI of HCC patients might be masqueraded by an accumulation of excess fluid. Risk of hepatobiliary cancer death increased in patients with higher BMI.⁽²¹⁾ In current study, HCC patients who survived had slightly higher BMI than who died during 1-year follow-up.

PG-SGA has been developed for cancer patients by Ottery.⁽²²⁾ It modified from Subjective Global Assessment (SGA). PG-SGA is perspicuous. Inter-observer agreement between doctor and dietician was 90%.⁽²³⁾ Comparing with SGA, PG-SGA has sensitivity of 98-99.1% and specificity of 82–86% in predicting malnutrition of cancer patients.^{(12, 24)} PG-SGA has higher sensitivity and specificity than abridged version of the PG-SGA and Malnutrition Screening Tool (MST) in patients with cancer.⁽²⁵⁾ The PG-SGA score of ≥ 4 can effectively predict postoperative complications after hepatic resection.⁽²⁶⁾ Adductor pollicis muscle, arm muscle circumference, phase angle and PG-SGA are more sensitive than BMI, TSF and arm circumference for detecting malnutrition in HCC patients.⁽²⁰⁾ PG-SGA correlated with Child-Pugh score and clinical outcomes in HCC patients.⁽²⁷⁾ PG-SGA combined with creatinine height index can assess nutritional status in patients with HCC.⁽²⁷⁾ In the present study, patients with malnutrition assessed by PG-SGA had higher mortality than patients without malnutrition.

MNA is more simply and does not require expertise. MNA has comparable sensitivity to PG-SGA in cancer patients.⁽²⁸⁾ MNA correlates with survival of colorectal and pulmonary cancer patients.^{(29, 30)} MNA can effectively assess nutritional status in patients with liver cancer.⁽³¹⁾ Malnutrition assessed by MNA and Bioelectrical impedance analysis (BIA) measurements significantly influence survival in HCC patients.⁽¹⁴⁾ The MNA score is more predictive of survival than the Nutrition risk screening (NRS).⁽¹⁴⁾ MNA is better than Global Quality of Life and Global Functional Status in predicting quality of life and functional status in patients with HCC.⁽³²⁾ Patients with malnutrition assessed by MNA had higher mortality than well-nourished patients.

Malnutrition has negative effect on prognosis of patients with HCC.^{(14, 27)} In HCC patients who undergoing hepatic resection, malnutrition is associated with higher postoperative complications and longer hospital stay.⁽²⁶⁾ Nutritional status should be assessed in all HCC patients. There is no gold standard of nutritional assessment in HCC patients. The study comparing nutritional assessment in HCC patients is scarce. In our study, sensitivity of PG-SGA and MNA were 80 and 77.1%, respectively. PG-SGA had higher accuracy than MNA in predicting mortality of HCC patients. The specificity of PG-SGA and MNA were fair. There are several possible explanations. PG-SGA requires expertise. Most of cancer patients receive more than three medications and HCC patients with tense ascites are unable to have three full meals which deduct points from MNA.⁽²⁸⁾ They may be misclassified as malnutrition if they take small frequent meals and any form of dietary supplements. Most of HCC patients in this study was Child-Pugh A and B. Malnutrition is not obvious in Child-Pugh A and B cirrhosis, nutritional assessment tools are crucial. In this study, PG-SGA and MNA were only assessed at a single point in time. Serial assessment probably improve reliability and leads to early detection of malnutrition. Further repeated measures study is warranted. Limitation of the study is the lack of available dietary and nutritional supplement data.

Patients with malnutrition identified by PG-SGA or MNA had higher risk of mortality than patients without malnutrition. PG-SGA had higher accuracy than MNA. PG-SGA should be the methods of nutritional assessment in HCC patients.

Acknowledgments

None.

Authors’ contributions

P.C. conceived of the presented idea and developed the theory. P.C., A.S., P.P., N.L. and O.T. collected the data. P.C. and O.T. contributed data. P.C., O.T. and K.K. performed the analysis. All authors discussed the results. P.C. and O.T. contributed to the final manuscript.

Disclosure of interest

The authors report there are no competing interests to declare.

Funding

This present study was supported by a research grant from HRH Princess Mahachakri Sirindhorn Medical Center, Faculty of Medicine, Srinakharinwirot University.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

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No competing interests reported.

Comparison of Patient-Generated Subjective Global Assessment (PG-SGA) and Mini Nutritional Assessment (MNA) for nutritional assessment in Hepatocellular carcinoma patients

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study design and participants

Procedure

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1