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Recommendations Summary

CKD: Nutrition Assessment: Composite Nutritional Indices (2020)

Click here to see the explanation of recommendation ratings (Strong, Fair, Weak, Consensus, Insufficient Evidence) and labels (Imperative or Conditional). To see more detail on the evidence from which the following recommendations were drawn, use the hyperlinks in the Supporting Evidence Section below.

  • Recommendation(s)

    CKD: 7-Point Subjective Global Assessment (SGA)

    In adults with CKD 5D, we recommend the use of the 7-point Subjective Global Assessment as a valid and reliable tool for assessing nutritional status (1B).

    Rating: Strong

    CKD: Malnutrition Inflammation Score (MIS)

    In adults with CKD 5D on MHD or posttransplantation, Malnutrition Inflammation Score may be used to assess nutritional status (2C).

    Rating: Weak

    • Risks/Harms of Implementing This Recommendation

      There are no risks of harms associated with the implementation of these recommendations.

    • Conditions of Application

      The large body of literature on nutritional assessment and composite nutritional indices have been completed in CKD 5D. While some of these tools may be relevant and can be translated to earlier stages (1-4) CKD, there is a need for the practitioner to conduct a comprehensive nutritional assessment comprising the main domains of the Nutrition Care Process.

      Protein energy wasting (PEW), a term supported by the International Society of Renal Nutrition and Metabolism, describes the complexity of nutritional and metabolic alterations that exist in CKD.  While PEW definition is useful in identify patients with overt nutritional abnormalities, its sensitivity is low given its strict criteria.  While comprehensive nutritional indices have been validated for the recognition of a poor nutritional status (e.g., malnutrition), it is unclear how well some of these same tools may be applied in the early identification of PEW.

      Implementation Considerations
      Routine nutrition screening of adults diagnosed with CKD stages 1-5D should occur to allow for the identification and further assessment and treatment of nutritional concerns.

      A comprehensive nutrition assessment, using a composite nutritional index, should be conducted at the initial visit and completed whenever there is a change in health status or as per institutional or regulatory policies.

      Monitoring and Evaluation
      The comprehensive nutrition assessment will guide the nutrition intervention prescribed. The clinician should monitor key nutrition care outcomes based on the treatment plan prescribed and re-assess and change the plan accordingly to achieve the goals established.

    • Potential Costs Associated with Application

      There are no significant costs associated with implementation of these recommedations.

    • Recommendation Narrative

      Assessment of nutritional status in adults diagnosed with CKD stages 1-5 must occur on a routine basis in order to prevent and/or treat malnutrition and wasting. The Nutrition Care Process (NCP) begins with a nutrition screening, whereby key nutritional indicators may trigger further assessment and intervention.  There are several nutrition screening mechanisms in clinical practice, but few are specific to CKD, and there are limited data on their validity and reliability. Most of the existing tools focus on identification of malnutrition risk; only one currently screens for PEW. Regardless of the mechanism used, the nutritional assessment conducted subsequent to the screening should be comprehensive and include the routine monitoring of nutrition care outcomes. The main components of the comprehensive nutrition assessment comprise anthropometric measurements, biomarkers, clinical symptoms exhibited on physical exam, dietary intake assessment, and medical/psychosocial history. The availability of composite nutritional indices [e.g., the Subjective Global Assessment (SGA) or Malnutrition Inflammation Score (MIS)], collect such data and therefore assist the clinician in deciding about the individual’s nutritional status and eventual plan of care, and are specific to the unique nutritional requirements of this patient population. 

      Detailed Justification

      Composite Nutritional Indices: Screening Tools

      Geriatric Nutrition Risk Index (GNRI)
      Three studies reported on the use of GNRI to assess nutritional status, including two validity/reliability studies (Beberashvili et al 2013, Yamada et al 2013) and one prediction study in MHD patients (.In one study, GNRI had the greatest area under curve  (using MIS as a reference) of the nutrition screening tools (Yamada et al 2013). GNRI showed a significantly negative correlation with the MIS (r=-0.67, P 0.0001), and the most accurate GNRI cutoff to identify a malnourished patient according to the MIS was 91.2. The GNRI’s sensitivity, specificity, and accuracy of a score of 91.2 in predicting malnutrition according to the MIS were 73%, 82%, and 79% respectively. Another study reported that GNRI had high inter-observer agreement score (k=0.98) and high intra-observer reproducibility (k=0.82) (Beberashvili et al 2013).

      In another study, GNRI was a significant predictor for mortality at 2.97 years (p<0.001) but had lower predictive value for all-cause mortality compared to MIS and albumin levels.

      Malnutrition Universal Screening Tool/Malnutrition Screening Tool (MUST/MST)
      Two validity/reliability study reported on the use of MUST and MST tools to assess nutritional status in MHD patients (Yamada et al 2013, Lawson et al 2012). A study by Lawson et al, reported on the validity and reliability of both MUST and MST tool in MHD patients (Lawson et al 2012). The sensitivity of both the MUST and MST tool was low (53.8% for MUST; 48.7% for MST), indicating that they are not particularly sensitive at identifying individuals with malnutrition in this group, compared to SGA. Both tools have a high specificity (MUST=78.3%; MST=85.5%), so they are good at excluding individuals who are not malnourished. Reliability assessed by kappa was 0.58 for MUST (95% CI, 0.20 to 0.80) and 0.33 for MST (95% CI, 20.03 to 0.54). Both tools had an NPV or 60% and PPV for MUST was 73.7% and for MST was 78.7%.  Though these tools are not sensitive enough to identify all malnourished renal in-patients, they are still fairly reliable and related to other nutrition status markers. In Yamada, et al., the authors compared results from various malnutrition assessment tools to the reference standard of MIS. MUST and MST scores were both significantly associated with MIS (p<0.0001 for each). The ROC curves of the MUST and MST compared to MIS were the smallest of the tools measured, and sensitivity, specificity and accuracy to detect hypoalbuminemia were among the lowest of all tools considered, indicating these may not be the best tools to discriminate nutritional risk in patients on MHD.

      Mini-Nutrition Assessment (MNA)
      Four studies reported on the use of MNA to assess nutritional status in MHD patients, three were validity/reliability studies (Yamada et al 2013, Afsar et l 2006, SAntin et al 2016) and one was a correlational study (Yamada et al 2013, Afsar et l 2006, SAntin et al 2016, Erdoga et al 2013). Afsar et al. reported on the reliability of MNA tool compared to SGA 3-point scale. The reliability coeffcients (alpha) for MNA was 0.93 (good degree of reproducibility). MNA might underestimate the nutritional status of patients on MHD who are not in an in?ammatory state. Hence, MNA may not be as reliable as SGA in detecting PEM in the MHD population. Erdogan et al. compared MNA to Bio-electrical Impedance Analysis (BIA), reported a significant correlation between MNA score and single frequency-BIA (r=0.2, p=0.045), muscle mass (r=0.382; p<0.001) and visceral fat ratio (r=0.270; p=0.007). Authors concluded BIA is not as sensitive as MNA to detect early effects of secondary causes for malnutrition.  Santin et al. 2015, compared SGA (7-point), MIS, MNA-Short Form (MNA-SF) to handgrip strength (HGS), albumin, c-reactive protein (CRP), and skinfolds. SGA and MNA-SF had fair agreement (kappa=0.24; p<0.001). The worst agreement was found between MIS and MNA-SF (kappa=0.14, none to slight; p<0.004). Again, both SGA and MIS had good concurrent and predictive validity for CKD population, whereas MNA-SF validity results were more comparable to non-CKD elderly individuals. Yamada et al, compared MNA to other nutritional tools and reported that MNA had lower area under curve (0.73) than GNRI and Nutritional Risk Score but higher than MUST and MST.

      Nutrition Impact Symptoms (NIS)
      One validity study reported on the use of NIS score for identifying those at risk of malnutrition in patients on HD and concluded that NIS score is a useful nutrition screening tool for identifying who are at risk of malnutrition (Campbell et al. 2013). NIS score >2 had the strongest predictive value for mortality and for predicting poor nutritional outcomes, behind the rating of malnourished by SGA. Concurrent validity indicated similar agreement between each of the malnutrition risk tools (patient-generated subjective global assessment (PG-SGA), an abbreviated PG-SGA and NIS). Serum albumin was negatively correlated with NIS (Spearman Rho= -0.161; p=0.018).

      Nutrition Screening Tool (NST)
      One validity study reported on the use of NST to assess nutritional status in PD patients. In this study, NST had a sensitivity of 0.84 (range: 0.74 to 0.94; p<0.05) and speci?city of 0.9 (range: 0.82 to 0.99; p<0.05) which is clinically acceptable (Bennett et al 2006).

      Renal Nutrition Screening Tool (R-NST)
      In another study by Xia et al in PD patients, the R-NST was compared to SGA-7 point scale. Authors determined that the R-NST tool when compared to SGA- 7 point scale is valid to detect risk of malnutrition (sensitivity=97.3% (95% CI 90.7-99.7), specificity=74.4% (95% CI 57.9-87.0), PPV=88.0% (95% CI 79.0-94.1), NPV=93.6% (95% CI 78.6-99.2). These results indicate that R-NST is a good tool for identifying renal in-patients at risk of undernutrition.

      Protein Energy Wasting (PEW) Score
      Two predictive studies reported on the use of PEW score to assess nutritional status. Leining and colleagues identified that SGA and albumin were significant predictors of mortality, but BMI, mid-arm muscle circumference (MAMC) and PEW score did not predict mortality at 24 months in PD patients. However, Moreau-Gaudry et al, a study conducted in patients on MHD recorded that PEW predicts survival. Each unit decrease in score was related with a 5-7% reduction in survival (p<0.01). This score can be helpful in identifying subgroups of patients with a high mortality rate and recommend nutrition support.

      Composite Nutritional Indices: Assessment Tools

      Subjective Global Assessment (SGA)
      Eleven studies examined the relationship between the 7-point SGA score and comparative measures, including three validity/reliability studies (Steiber et al 2007, Visser et al 1999, Santin et al 2016)  and six additional prediction and/or correlation studies (de Roij van Zuijdewijn et al 2015, Malgorzewicz et al 2008, Vannini et al 2009, Jones et al 2004, Perez et al 2016, Tapiawala et al 2006).

      Three studies examined the validity and/or reliability of the 7-point SGA score in MHD patients. In Visser, et al , 7-point SGA score demonstrated fair inter-observer reliability [intra-class correlation (ICC) = 0.72] and good intra-observer reliability (ICC=0.88) in MHD patients. In Santin, et al,   7-point SGA score had good agreement with MIS (κ=0.43; p<0.001) and MNA-SF (κ=0.24; p<0.001). In a study by Steiber, et al, SGA had fair interrater reliability (κ=0.5, Spearman’s Rho=0.7), substantial intra-rater reliability (κ=0.7, spearman’s Rho=0.8) (p<0001).

      Three cohort studies examined whether the 7-point SGA score was predictive of hard outcomes in patients on MHD. In Perez, et al., SGA was a significant predictor of mortality at 2 years after adjustments for significant confounders (Perez et al 2016). In a study by de Roij van Zuijedewijn, et al., SGA was a significant predictor (p<0.001) for mortality at 2.97 years, but had lower predictive value for all-cause mortality compared to MIS and albumin levels. de Mutsert and others reported that hazard of mortality increased with SGA in a dose-dependent manner among patients on dialysis. Compared to normal nutritional status, persons who had a SGA of 4-5 had an increased HR (95% CI) at 7 year mortality of 1.6 (1.3, 1.9) and SGA of 1–3 had an HR of 2.1 (1.5, 2.8) at 7-year mortality. The strength of association increased in time-dependent models. Finally, in a study with PD patients, every one unit increase in the 7-point SGA adapted for end-stage renal disease (ESRD)/continuous ambulatory PD patients, there was a 25% decreased 2 year mortality risk (p<0.05) (Churchill et al 1996).

      Six studies examined correlations between the 7-point SGA score and other measures of nutritional status. In Visser, et al., there was a strong correlation between the 7-point SGA score and BMI (r=0.79), % fat (r=0.77), and mid arm circumference (r=0.71) (all p<0.001)in MHD patients. In a study by Steiber, et al, there were statistically significant differences in mean BMI and serum albumin according to SGA score in MHD patients (p<0.05). Tapiawala, et al. assessed the 7-point SGA score in patients with CKD, ESRD and those on all types of dialysis. SGA scores were not correlated with dietary protein and energy intake or serum albumin levels, but anthropometric measures correlated with the SGA scores (skinfolds r=0.2, MAC r=0.5 and MAMC r=0.5). Authors concluded 7-point SGA is a reliable method of assessing nutritional status. Malgorzewicz, et al. compared near-infrared measurements and albumin levels to the SGA 7-point score in MHD patients. LBM measured by near-infrared was significantly decreased in malnourished patients (p<0.05) and there was a correlation between SGA score and LBM(r=0.5; p<0.05) as well as SGA score and albumin concentration (r=0.7; p<.05). In Vannini, et al, SGA were associated with traditional nutritional markers, reinforcing validity for use among patients on MHD. SGA score was not associated with CRP level. Jones, et al examined the relationship between 3-point SGA score and a composite nutritional score that included SGA (3 point and 7 point), BMI, % reference weight, skinfold and MAMC measurements and albumin levels in patients treated by MHD. Compared to the composite score, the SGA score misclassified a “large number of subjects” and score was not associated with many nutrition parameters such as dietary intake, BMI or albumin levels.

      In one study (Garagarza et al 2013), the authors utilized a version of the SGA that was adapted for patients on MHD, and in two studies (Leinig et al 2011, Passadakis et al 1999),  the version of the SGA tool used was unclear.  Garagarza et al, compared bioimpedance spectroscopy measurements to SGA scores from a version modified for MHD that included a 5-point score comprising weight changes, eating habits, gastrointestinal symptoms, functional activity and comorbidities. PEW measured by BIS extracellular weight(ECW)/body weight (BW) was positively associated with CRP (p=0.009) and SGA score (p=0.03).  Leinig, et al. examined the relationship between SGA score and mortality risk at 24 months in PD patients, but version of the SGA employed was unclear. SGA score was a significant predictor of mortality in PD patients (Leinig et al 2011). Passadakis, et al. compared BIA measurements to SGA score in CAPD patients, but the version of SGA utilized was uncertain. SGA score was significantly correlated with impedance index (r=0.48; p=0.0038) and phase angle (r=0.43; p=0.0048).

      Malnutrition Inflammation Score (MIS)
      Eight studies reported on the use of MIS to assess nutritional status, including two validity/reliability studies (Beberashvili et al 2013, Santin et al 2016), four prediction studies (Fiedler et al 2009, de Roij van Zuijdewijn et al 2015, Perez et al 2016) and three correlation studies (Molnar et al 2010, Amparo et al 2013, Hou et al 2012).

      One study by Bebershavili et al reported that MIS had moderate inter-observer agreement (k=0.62) and inter-observer reproducibility (k=0.77) and is a valid tool for longitudinal assessment of nutritional status of patients on MHD. Another study by Santin et al, indicated that MIS had good agreement with SGA (k=0.43, p<0.001) and worse agreement with MNA-SF (k= 0.14, p<0.004). MIS also had good concurrent and predictive validity for the MHD population.

      Four studies reported on the use of MIS as a predictor of mortality (Fiedler et al 2009, de Roij van Zuijdewijn et al 2015, Perez et al 2016). All three studies reported that in patients on MHD, MIS is a significant predictor of mortality. In one study, MIS was a significant predictor for mortality at 2.97 years (p<0.001), and best predictive tool for all-cause mortality and secondary end-points like cardiovascular events in patients on MHD (de Roij van Zuijdewijn et al 2015). Another study by Fiedler et al also reported that MIS was predictive of both mortality and hospitalizations in patients treated by MHD with survival analysis indicated that MIS was one of the best predictors of mortality [HR 6.25 (2.82 – 13.87), p<0.001].  Perez et al, also indicated that MIS was a significant predictor for 2 year mortality in MHD patients. Finally, in Santin, et al., while mild MIS did not predict mortality, severe MIS was a significant predictor of mortality in adjusted analysis [HR (95% CI): 5.13 (1.19, 13.7).

      Three studies reported on the use of MIS and correlation with other tools. Amparo et al, indicated that there was a significant negative correlation between hand grip strength and MIS (r= -0.42, p<0.001) in predialysis subjects. Hou et al, indicated that MIS was strongly correlated with modified quantitative subjective global assessment (r=0.924) and inversely correlated with BIA (r= -0.213) in MHD patients. Molnar et al, reported that MIS showed significant negative correlations with abdominal circumference (p= -0.144; p<0.001) and pre-albumin level (p= -0.165; P<0.001), whereas significant positive correlation was seen with IL-6 (p=0.231; p<0.001), TNF-a (p=0.102; p<0.001), and CRP levels (p=0.094; p=0.003) in kidney transplant recipients. All studies show that MIS is a useful tool to assess nutritional status in CKD patients.

      Other Composite Nutritional Indices

      Nutrition Risk Score (NRS)
      A prediction study reported that NRS was a good predictor of mortality (HR 4.24 (1.92-9.38), p<0.001) in patients on MHD and was superior when compared to lab markers and BIA in predicting mortality (Fiedler et al 2009).

      Protein Nutrition Index (PNI)
      A reliability study investigated PNI as a predictor of survival in PD patients. Compared to the reference standard (nPNA (nPCR) ≤0.91 as malnutrition), the sensitivity, specificity, positive and negative predictive value of PNI were 0.4, 0.978, 0.901 and 0.783, respectively (Chen et al 2010). This study indicated that PNI is a good predictor of mortality (even after adjusting for age and comorbidities). An increase in PNI score by 1 led to a 16% decrease in mortality risk.

      Composite Score of Protein Energy Nutrition Status (cPENS)
      de Roij van Zuijdewin et al studied eight nutrition assessment tools used to predict all-cause mortality. cPENS had a Harrell’s C statistics of 0.63 (0.61 – 0.66) for predicting mortality. However, the study indicated that it had inadequate discrimination and calibration or a lower predictive value for mortality.   

      Other Measures
      Blumberg et al, compared the integrative score with the SGA-7-point scale in HD patients. Integrative clinical nutrition dialysis score is based on biochemical measures of albumin, creatinine, urea, cholesterol, CRP, dialysis adequacy, and weight change. With every unit increase in integrative score, the odds of death were significantly decreased (HR=0.929, 95% CI 0.885-0.974,  p<0.002). SGA and integrative score were significantly correlated (n=69, r=0.853,  p<0.01) and according to the author this is a useful prognostic tool to detect early nutrition deterioration.

      A prediction study investigated which nutritional composed scoring system best predicts all-cause mortality in MHD patients (Perez et al 2016). This study indicated that SGA and MIS are better predictors of all-cause mortality at 15.5 months in this study and International Society of Renal Nutrition and Metabolism criteria was not able to predict mortality in this sample.

      One correlation study investigated the relationship between body adiposity index (BAI), BIA, anthropometrics, and DEXA (Silva et al 2013). The correlation coefficient was higher between DEXA vs. anthropometric measurements (r=0.76) and BAI (r=0.61) when compared to BIA (r=0.57) in the adjusted analysis (p<0.0001). Results suggest BIA estimates body fat with high accuracy in non-dialyzed CKD patients.

    • Recommendation Strength Rationale

      The evidence supporting the above recommenations is based on Grade II/Grade B and Grade III/Grade C evidence.

    • Minority Opinions

      Consensus reached.