ORIGINAL RESEARCH
A Simple Protein–Energy Wasting Score Predicts Survival in Maintenance Hemodialysis Patients Xavier Moreau-Gaudry, MD,* Guillaume Jean, MD,† Leslie Genet, MSc,‡ Dominique Lataillade, MD,§ Eric Legrand, MD,{ Franc¸ois Kuentz, MD,** and Denis Fouque, MD, PhD‡ Objective: Nutritional status is a powerful predictor of survival in maintenance hemodialysis patients but remains challenging to assess. We defined a new Protein Energy Wasting (PEW) score based on the nomenclature proposed by the International Society of Renal Nutrition and Metabolism in 2008. Design and Methods: This score, graded from 0 (worse) to 4 (best) was derived from 4 body nutrition compartments: serum albumin, body mass index, a normalized serum creatinine value, and protein intake as assessed by nPNA. Subjects: We applied this score to 1443 patients from the ARNOS prospective dialysis cohort and provide survival data from 2005 until 2008. Main Outcome Measure: Patients survival at 3.5 year. Results: Survival ranged from 84%-69% according to the protein-energy wasting score. There was a clear-cut reduction in survival (5%-7%; P , 0.01) for each unit decrement in the score grade. There was a 99% survival at 1 year for patients with the score of 4. In addition, the 6-month variation of this PEW score also strongly predicted patients’ survival (P , 0.01). Conclusion: A new simple and easy-to-get PEW score predicts survival in maintenance hemodialysis patients. Furthermore, increase of this nutritional score over time also indicates survival improvement, and may help to better identify subgroups of patients with a high mortality rate, in which nutrition support should be enforced. Ó 2014 by the National Kidney Foundation, Inc. All rights reserved.
Introduction
L
IFE EXPECTANCY OF maintenance hemodialysis patients (MHD) is severely reduced as compared with the general population1 and nutritional status remains a powerful predictor of morbidity and mortality.2-4 Protein–energy wasting (PEW) is the consequence of a combination of insufficient intake, uremic toxins, inflammation, and superimposed catabolism.5,6 Several studies have confirmed the importance of clinical assessment, body composition measurement, and laboratory analyses for diagnosing and quantifying malnutrition,7-9 however routinely, physicians are left without easy-to-use tools. As recently underlined by Kovesdy et al.,10 the lack of a gold standard test to establish PEW renders diagnosis
accuracy of malnutrition impossible, and using a combined score of nutritional parameters that encompasses various aspects of nutrition, an accurate diagnosis of PEW is more likely to be done. The International Society of Renal Nutrition and Metabolism proposed a uniformed nomenclature.11 However, whether this new classification predicts mortality has not yet been validated in MHD. In this study, we developed a new easy-to-use PEW score, based on various clinical and biological values, all being readily available at bedside. We hypothesized that this score will predict survival in MHD patients. The ARNOS prospective dialysis cohort was used to validate this hypothesis.
Subjects and Methods *
Centre de dialyse Porte de Provence, AGDUC, Montelimar, France. Centre du Rein Artificiel – Nephrocare, Ste Foy les Lyon, France. ‡ Department of Nephrology, CarMeN, CENS, Centre Hospitalier LyonSud, Universite de Lyon F-69622, France. § Service de nephrologie et hemodialyse, Centre Hospitalier – Avitum, Sallanches, France. { Service de nephrologie et hemodialyse, Centre hospitalier, Annonay, France. ** Centre de dialyse des Eaux Claires, AGDUC, Grenoble, France. Financial Disclosure: See Acknowledgments on page 399. Address correspondence to Denis Fouque, MD, PhD, Service de Nephrologie, Dialyse, Nutrition, Centre Hospitalier Lyon Sud, 69495 Pierre-Benite, France. †
E-mail: [email protected] Ó
2014 by the National Kidney Foundation, Inc. All rights reserved. 1051-2276/$36.00 http://dx.doi.org/10.1053/j.jrn.2014.06.008
Journal of Renal Nutrition, Vol 24, No 6 (November), 2014: pp 395-400
Study Population and Data Collection The ARNOS prospective study merged 25 dialysis units located in the Rh^ one-Alpes area in France and Switzerland. A total of 2,180 cases were collected during the study from June 1, 2005 to June 1, 2008 (Fig. 1). Each patient was evaluated every 6 months and values recorded until January 1, 2009. A total of 1,349 MHD were enrolled starting June 1, 2005. Starting January 1, 2006 until June 1, 2008, 831 incident patients were additionally enrolled. Exclusion criteria were age less than 18 year, patients receiving a weekly number of dialysis sessions different from 3 (n 5 288, Fig. 1). One patient was excluded because of a lost file. Because of missing data to calculate the score, 458 patients were excluded, leaving 1,433 patients for the 395
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Figure 1. Fluxogram of the ARNOS study. A total of 2,180 patients were recorded. One thousand four hundred thirty three patients were eligible for the score analyses (1,206 prevalent patients and 685 incident patients minus patients with missing data), and of these, 1,068 patients were available for the score variation over time analysis.
final score analysis. Hemodialfiltration was performed in 193 of them, e.g., 13.5%.
380 mmol/L/m2 threshold value was obtained by a receiver operating characteristics (ROC) curve analysis.
Score Values We defined the PEW score as the grading of 1 selected item in each of the 4 categories of the wasting syndrome11: (1) serum albumin (for biochemistry), (2) BMI (for anthropometry), (3) predialysis serum creatinine normalized by body surface area (SCr/BSA; for muscle mass), and (4) normalized protein nitrogen appearance (nPNA; for protein intake). The threshold values were (Table 1) serum albumin: 3.8 g/dL; BMI: 23 kg/m2; SCr/BSA: 380 mmol/L/m2, and nPNA: 0.8 g/kg/day. nPNA was calculated with the Garred formula.12 BSA was estimated by the Boyd formula13,14 as follows:
Score Calculation and Follow-Up If a patient had a value strictly greater than threshold, he received 1; if below threshold, the grade was 0 (see Table 1). The individual score was therefore comprised between 4 (normal nutritional status) and 0 (severe wasting). Because there was only 1 patient with a score of 0, for survival analyses we pooled the 0 and 1 grades in a single group (group 1) and finally analyzed survival for 4 categories of PEW scores from group 1 (severe wasting) to group 4 (normal). We also followed the score change after 6-month of follow-up and analyzed how an improvement/impairment in this score affected patients survival.
Body surface ðcm2 Þ50:00032073ðweightÞ0:728520:01883logðweightÞ 3ðheightÞ0:3
where weight, collected after the HD session, is in gram and height in centimeter. For the SCr/BSA variable, the
Table 1. Definition of the Protein–Energy Wasting Score Serum albumin (g/dL) Body mass index (kg/m2) SCr/BSA (mmol/L/m2) nPNA (g/kg/day)
#3.8 #23 #380 #0.8
nPNA, normalized protein nitrogen appearance; Scr/BSA, predialysis serum creatinine/body surface area (using postdialysis body weight).
Statistical Analyses The different statistical analyses were computed using ‘‘R’’ software version 2.12.1.15 Continuous variables were reported as mean 6 standard deviation or mean (confidence interval [CI], P value) and compared by the Student t-test. Qualitative variables were reported as percentage and compared using chisquared test. Cox proportional hazard models were used to estimate the hazard ratios (HRs). The Kaplan–Meier method was used to plot survival curves that were compared using the log-rank test. To test the robustness of the score in our population, we used a bootstrapping technique to obtain the mean log rank. CIs were obtained by the bootstrap
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PROTEIN-ENERGY WASTING SCORE Table 2. Patients Characteristics According to Their Score at Study Entry Clinical and Laboratory Parameters Number of patients Number of deaths Age (y) Male (%) Dry weight (kg) BSA, m2 Dialysis vintage (y) C-reactive protein (mg/L) Serum albumin (g/dL) SCr (mmol/L) SCr/BSA (mmol/L/m2) sp KT/V nPNA (g protein/kg/d)
Score 0-1
Score 2
Score 3
Score 4
All
Severe Wasting
Moderate Wasting
Slight Wasting
Normal Nutritional Status
1,433 299 66.7 6 14.5 61.7 68.3 6 14.9 1.78 6 0.23 4.2 6 6.0 13.5 6 26.9 36.0 6 5.1 692 6 258 392 6 167 1.60 6 0.52 1.12 6 0.31
276 74 72.1 6 12.1* 63.0† 62.9 6 13.9* 1.71 6 0.21* 4.2 6 7.4* 23.1 6 42.6* 32.5 6 4.7* 509 6 337* 304 6 272* 1.57 6 0.58 0.90 6 0.33*
529 124 69.3 6 13.0* 56.4* 68.3 6 16.1* 1.78 6 0.24* 3.7 6 5.4* 13.7 6 24.8* 34.4 6 4.2* 630 6 172* 361 6 113* 1.63 6 0.55 1.14 6 0.28*
460 81 63.0 6 15.6† 62.6† 68.7 6 13.1* 1.79 6 0.20* 4.7 6 5.7 9.3 6 16.3 38.0 6 4.5* 791 6 207* 446 6 120* 1.59 6 0.49 1.18 6 0.27†
168 20 60.0 6 14.6 74.4 76.2 6 13.7 1.90 6 0.20 4.8 6 6.2 7.5 6 13.5 41.4 6 2.4 918 6 162 484 6 77 1.54 6 0.42 1.23 6 0.26
BSA, body surface area; nPNA, normalized protein nitrogen appearance; SCr, serum creatinine. *P , .001 versus score 4. †P , .05 versus score 4.
1.0 0.9 Survival
0.8
Score = 1 Score = 2 Score = 3 Score = 4
0.7
Results Study duration was 3.5 years. A total of 1,433 patients were retained for analysis (Fig. 1) and their mean follow-up was 2.3 6 4.1 year (Table 2). Mean age was 67 years and dialysis vintage was 4.2 years. The major causes of morbidity remained hypertension and diabetes: hypertension was present in 78% and diabetes was reported in 34%. Chronic cardiac disease, stroke, or transient attacks was present in 23%, 10%, and 6%, respectively. Peripheral vascular disease occurred in 32%. Dialysis access was an arteriovenous fistula (81%) and 19% of patients had a permanent central venous catheter. BMI was 24.9 6 4.9 kg/m2. Residual renal function (diuresis . 500 mL/day) was present in 31% of patients. Mild chronic inflammation was present in more than 50% of patients, with a mean C-reactive protein of 14.0 mg/L (Table 2). Dialysis dose was considered adequate (single pool Kt/V of 1.6) as well as protein intake (1.1 g/kg/day). Table 2 details clinical and biological parameters according to the score grade. The moderately and severely wasted groups (Gr 1 and 2) included 805 patients, e.g., 56% of the cohort. All parameters were significantly more severe or impaired in group 1 compared with group 4, except for Kt/V. Figure 2 shows patients’ survival according to the PEW score. The HR was 0.80 (CI 0.60-1.07, P 5.14) between the severely malnourished group (Gr1) and the moderately malnourished group (Gr2), 0.61 (0.45-0.84, P , .005) between Gr1 and the slightly malnourished group (Gr3) and 0.38 (0.24-0.64, P , .001) between Gr1 and the normal nutritional status group (Gr4). In multivariate analysis, HRs were respectively 0.80 (CI 0.59-1.09, P 5 .16) between Gr1 and Gr2, 0.60 (CI, 0.42-0.85,
P , .005) between Gr1 and Gr3 and 0.33 (CI, 0.19-0.59, P , .001) between Gr1 and Gr4 (Table 3). It is also important to note that in the 4-score subgroup, which indicates a normal nutritional status, only 1 over 168 pts died during the first year of the study (Fig. 2). Finally, to get a score prediction in a shorter time, we tested its power at 1 year and found it also significantly predictive of mortality, with a HR of 0.63 (score 2 vs. 0-1, P , .001), 0.41 (score 3 vs. 0-1, P , .001), and 0.04 (score 4 vs. 0-1, P , .001).
0.6
bias–corrected accelerated method because the likelihood ratio distribution was not gaussian (q-q plot analysis). C-statistics were used to compare scores and variables used by the score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Time (years)
Figure 2. Patients survival according to the protein–energy wasting score. One thousand four hundred thirty three patients were analyzed including prevalent patients followed from June 2005 to December 2008 and incident patients who initiated maintenance hemodialysis from January 2006 to June 2008. Patients with a score of 0 or 1 were pooled together because of limited effective. Group 1 (score of 0 or 1) had severe wasting, group 2 (score of 2) had moderate wasting, group 3 (score of 3) had slight wasting, and group 4 (score of 4) had a normal nutritional status.
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Table 3. Three-and-a-Half Year Survival Predictive Factors in 1,433 Maintenance Hemodialysis Patients Patients Characteristics and Score
HR
Gender (M/F) Diabetes Congestive heart failure Peripheral vascular disease Stroke Fistula C-reactive protein Sp KT/V Score 2 versus 0-1 Score 3 versus 0-1 Score 4 versus 0-1
1.04 1.04 1.91 1.20 1.30 0.83 1.01 0.84 0.80 0.60 0.33
CI min CI max P value 0.80 0.80 1.45 0.97 0.90 0.61 1.00 0.65 0.59 0.42 0.19
1.36 1.36 2.50 1.50 1.89 1.13 1.01 1.08 1.09 0.85 0.59
, .001
, .005 , .005 , .001
CI, confidence interval; HR, hazard ratio; sp KT/V, single pool KT/V. The normal nutritional status group (score of 4) and the slightly protein–energy wasted group (score of 3) show an independent significant protective effect on patients survival (multivariate Cox model).
0.8
Delta Score < 0 Delta Score = 0 Delta Score > 0
0.6
0.7
Survival
0.9
1.0
We also asked the question whether a change in this score after 6 months could predict survival (Fig. 3). This indeed was the case as there was a significant difference in survival between different score changes (P , .01, log-rank test). Compared with the patients who had their nutritional status impaired (D score , 0), patients who maintained their score after 6 months had a 30% improvement in survival (P , .05) at 3.5 years and patients who improved their score (D score . 0) reduced mortality by 53% (P , .005). To validate the robustness of the score in our population, we used a bootstrap analysis at 1 year and 3.5 year. Mean log
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Time (years)
Figure 3. Patients survival according to a 6-month change (D score) in protein–energy wasting score (n 5 1,068, logrank test, P , .01). Patients with no change in 6-month score had a better survival than those with a score impairment (D score , 0), P , .05, and patients who improved their score (D score . 0), P , .005; 47 deaths were recorded in 181 patients (negative D score); 114 deaths in 606 patients (null D score), and 41 deaths in 281 patients (positive D score).
ranks were highly significant, being 31.6 (CI, 15.8-54.1), P , .001 at 1 year and 19.3 (CI, 5.8-35.6), P , .001 at 3.5 year, respectively, confirming that each subgroup significantly differed by survival, and thus, the validity of the score in our population. Finally, to test the superiority of our score, we analyzed the predictive power of each parameter (albumin, creatinine/SC, nPNA, and BMI) compared with the global score by doing a backward stepwise regression analysis. Serum albumin brought the most important predictive power, and when albumin was present in the model, the score was not retained, and creatinine and BMI were also lost. Thus, most nutritional information generally used by clinicians was lost. When albumin was taken out of the model, the score remained significant versus corrected creatinine, versus nPNA and versus BMI taken individually as continuous variables. Thus, even if the weight of each parameter we selected in the score was not strictly equal, the score has by itself a clinical relevance and brings more information than each parameter alone except albumin. We verified this point by performing 2 multivariate Cox analyses, one with serum albumin only, and the other with serum albumin, BMI, corrected creatinine and nPNA. The last model was significantly improved at 1 year and 3.5 year compared with the single albumin model (P 5 .018 and .048, respectively). Thus, these analyses confirm that the present PEW score uses reliable predictive nutritional information and improves survival prediction compared with the use of albumin alone.
Discussion We identified a new nutritional score based on simple readily available parameters and show that it can predict survival in MHD patients with acceptable accuracy. Although an altered nutritional status is frequently reported in MHD, physicians are left without simple tools to identify and treat PEW because there is no single nutrition parameter, which can predict wasting.16-19 It is everyone’s hope to take advantage of a simple nutrition equation to implement recommendations and improve patients’ status.20 The score we developed here includes 1 parameter from each major group generally identified to interfere in CKD patients’ nutritional status: (1) biological parameters, (2) body composition, (3) muscle mass, and (4) nutrient intake. It seems indeed important to add to laboratory information (e.g., serum albumin, prealbumin, cholesterol, and lymphocyte count) to other more clinical information such as BMI, body weight, and fat mass.21,22 Muscle mass, which is the major part of the body strongly associated with survival, is nevertheless difficult to assess. Here, we chose to use predialysis serum creatinine that we normalized by body surface area. Indeed, SCr/BSA gave a better fit in the Cox model than raw serum creatinine. Normalizing serum creatinine seems of interest because it may allow using this score more widely in populations that differ by
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creatinine intake and metabolism (Afro-American, Caucasian, and Hispanic or Asian patients). This may be the reason why creatinine is not used routinely, although many studies have shown its nutritional interest.16,23-26 It should be noted that, creatinine prediction power being linear,25,27 it is arbitrary to set a threshold, however necessary to make discrete statistical analyses and clinical recommendations. Finally, we also added information on nutrient intake by entering in the score the protein intake as estimated by nPNA. This value, although not always recorded in dialysis facilities, belongs to all current international recommendations and can be obtained easily through the dialysis generator software. Added to the model, it improved survival prediction compared with albumin alone. It should be noted that the score by itself was not better than the 4 parameters added into the model as separate variables. This was not unexpected because information given by the score is already brought by all variables included into the score. However, we believe that this is the interest of this score to encourage the dialysis staff to pay attention to all these variables taken together. The present score could be obtained within minutes at bedside, with no additional equipment, at no expense and therefore be added to the arsenal of patient’ general record and follow-up. The final patients classification we obtained (Table 2) corresponds well to the published literature on the prevalence of nutritional disorders in dialysis patients: 37% of patients present with moderate and 19% with severe impairment in their nutritional status.11,28 Is this pragmatic score useful? Patients with a grading of 0 to 1 (severe PEW) presented a mortality of 27% at 3.5 year (Fig. 2). At 2 years, only 6.5% with a normal nutritional status died, whereas 26.5% died if they were severely wasted, which represents a 4 time greater mortality. Of note is the fact that, only 1 patient with a normal nutritional status died during the first year of follow-up (Fig. 2). Interestingly, intermediate score values discriminate from each other to predict survival. This should be underlined because a given score may be obtained with different body composition alterations and not only relies on a single measurement of serum albumin or BMI, as it is often done in routine practice. In addition, as shown in the Cox multivariate analyses, the power of the score appeared greater than the single use of albumin or creatinine taken separately. It is uneasy to predict if an obese dialysis patient, theoretically having a better survival, who presents with a low albumin level will have a PEW: adding serum creatinine, as a muscle information, and protein intake may direct the patient up- or down-ward on the score scale, and allow a better refining of his/her nutritional risk and survival. An example taken from the present cohort highlights the interest of the score at enrollment, 2 patients had the same serum albumin of 3.5 g/dL and same BMI of 23.8 kg/m2; patient A had an adjusted serum creatinine of 300 mmol/L/m2 and
a nPNA of 0.57 g/kg/day, whereas patient B presented with an adjusted serum creatinine of 520 mmol/L/m2 and a protein intake of 1.28 g/kg/day. Patient A had a score of 1 and died during the study, whereas patient B with a score of 3 survived the study. From information of serum albumin and BMI only, the 2 mostly used nutritional indexes, it would not have been possible to predict a different survival for these patients. Looking at serum albumin alone, a common attitude could be even more misleading. Two patients presented with a serum albumin of 3.5 g/dL; the first had a BMI of 33 kg/m2, a corrected serum creatinine of 530 mmol/L/m2 and a nPNA of 1.08 g protein/kg/day (e.g., a score of 3) survived the study; the second one had a BMI of 26 kg/m2, a corrected serum creatinine of 360 mmol/L/m2 and a nPNA of 0.76 g protein/kg/day (e.g., a score of 1) and died during the study. Finally, this score has another interesting property. It allows a longitudinal follow-up of patients to predict an improvement/impairment of survival. Indeed, a positive score increment after 6 months of follow-up was associated with a 35% reduction in mortality at 3.5 year (Fig. 3). Conversely, moving downward the PEW score was associated with a 43% increase in mortality. This observation indicates that an improvement in the wasting syndrome is associated with an increased survival. However, the use of this score as a surrogate for survival studies is questionable, because the present findings are only from epidemiologic nature. Further interventional studies should be designed to test this hypothesis. In conclusion, the routine use of this easy and robust score may help to identify malnutrition, now termed PEW, and therefore may call for aggressive renutrition programs. Follow-up of the score predicts survival, as its improvement is associated with reduced mortality. Further studies may be needed to verify the robustness of the PEW score in non-Western populations because of the differences in body composition and clinical practice.
Practical Application No single parameter allows to define protein energy wasting (PEW) during chronic kidney disease. We present here a simple and easy-to-get PEW scoring system from 0-4, based on 4 indicators of body composition, muscle mass, food intake, and nutritional biology. The lowest is the score, the more severe is the wasting syndrome. The PEW score predicts survival, and improving PEW score at 6-month interval is associated with extended survival. The PEW score may allow identifying patients at highrisk of wasting and target specific nutritional strategies.
Acknowledgments The ARNOS project was partially funded by an educational grant from AMGEN France. None of the authors received honoraria, travel grants, or other financial support for this study.
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References 1. National Institutes of Health Mortality. In: USRDS Annual Data Report. 2011;226-234. Available at http://www.usrds.org/2011/pdf/v2_ch05_11. pdf. Accessed July 30, 2014. 2. Abbott KC, Glanton CW, Trespalacios FC, et al. Body mass index. dialysis modality and survival: analysis of the United States Renal Data System Dialysis Morbidity and Mortality Wave II Study. Kidney Int. 2004;65:597-605. 3. de Ara ujo IC, Kamimura MA, Draibe SA, et al. Nutritional parameters and mortality in incident hemodialysis patients. J Ren Nutr. 2006;16:27-35. 4. Cooper BA, Penne EL, Bartlett LH, Pollock CA. Protein malnutrition and hypoalbuminemia as predictors of vascular events and mortality in ESRD. Am J Kidney Dis. 2004;43:61-66. 5. Kalantar-Zadeh K, Ikizler TA, Block G, Avram MM, Kopple JD. Malnutrition-inflammation complex syndrome in dialysis patients: causes and consequences. Am J Kidney Dis. 2003;42:864-881. 6. Carrero JJ, Nakashima A, Qureshi AR, et al. Protein-energy wasting modifies the association of ghrelin with inflammation, leptin, and mortality in hemodialysis patients. Kidney Int. 2011;79:749-756. 7. Cano NJ, Heng A-E, Pison C. Multimodal approach to malnutrition in malnourished maintenance hemodialysis patients. J Ren Nutr. 2011;21:23-26. 8. Mazairac AHA, de Wit GA, Grooteman MPC, et al. A composite score of protein-energy nutritional status predicts mortality in haemodialysis patients no better than its individual components. Nephrol Dial Transplant. 2011;26:1962-1967. 9. Beberashvili I, Azar A, Sinuani I, et al. Objective Score of Nutrition on Dialysis (OSND) as an alternative for the malnutrition-inflammation score in assessment of nutritional risk of haemodialysis patients. Nephrol Dial Transplant. 2010;25:2662-2671. 10. Kovesdy CP, Kalantar-Zadeh K. Accuracy and limitations of the diagnosis of malnutrition in dialysis patients. Semin Dial. 2012;25:423-427. 11. Fouque D, Kalantar-Zadeh K, Kopple J, et al. A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease. Kidney Int. 2008;73:391-398. 12. Garred LJ, Tang W, Barichello DL, Canaud B. Equations for the calculation of the protein catabolic rate from predialysis and postdialysis urea concentrations and residual renal clearance in stable hemodialysis patients. Blood Purif. 1997;15:157-168. 13. Boyd E. The growth of the surface area of the human body. J Am Stat Assoc. 1936;31:765-766. 14. Sivanandam A, Siva S, Bhandari M, Menon M. Variance inflation in sequential calculations of body surface area, plasma volume, and prostatespecific antigen mass. BJU Int. 2008;102:1573-1580.
15. R Development Core Team. In: R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2010. 16. Fouque D, Vennegoor M, ter Wee P, et al. EBPG guideline on nutrition. Nephrol Dial Transplant. 2007;22(suppl 2):ii45-ii87. 17. Segall L, Mardare N-G, Ungureanu S, et al. Nutritional status evaluation and survival in haemodialysis patients in one centre from Romania. Nephrol Dial Transplant. 2009;24:2536-2540. 18. Rambod M, Bross R, Zitterkoph J, et al. Association of MalnutritionInflammation Score with quality of life and mortality in hemodialysis patients: a 5-year prospective cohort study. Am J Kidney Dis. 2009;53:298-309. 19. Kovesdy CP, George SM, Anderson JE, Kalantar-Zadeh K. Outcome predictability of biomarkers of protein-energy wasting and inflammation in moderate and advanced chronic kidney disease. Am J Clin Nutr. 2009;90:407-414. 20. Fiedler R, Jehle PM, Osten B, Dorligschaw O, Girndt M. Clinical nutrition scores are superior for the prognosis of haemodialysis patients compared to lab markers and bioelectrical impedance. Nephrol Dial Transplant. 2009;24:3812-3817. 21. Pifer TB, McCullough KP, Port FK, et al. Mortality risk in hemodialysis patients and changes in nutritional indicators: DOPPS. Kidney Int. 2002;62:2238-2245. 22. Kakiya R, Shoji T, Tsujimoto Y, et al. Body fat mass and lean mass as predictors of survival in hemodialysis patients. Kidney Int. 2006;70: 549-556. 23. Clinical practice guidelines for nutrition in chronic renal failure. K/DOQI. National Kidney Foundation. Am J Kidney Dis. 2000;35: S1-S140. 24. Desmeules S, Levesque R, Jaussent I, Leray-Moragues H, Chalabi L, Canaud B. Creatinine index and lean body mass are excellent predictors of long-term survival in haemodiafiltration patients. Nephrol Dial Transplant. 2004;19:1182-1189. 25. Moreau-Gaudry X, Guebre-Egziabher F, Jean G, et al. Serum creatinine improves body mass index survival prediction in hemodialysis patients: a 1-year prospective cohort analysis from the ARNOS study. J Ren Nutr. 2011;21:369-375. 26. Lertdumrongluk P, Canaud B. Pre-dialysis creatinine and interdialytic change in creatinine as nutritional markers in haemodialysis patients. Nephrol Dial Transplant. 2012;27:2130. 27. Kalantar-Zadeh K, Streja E, Kovesdy CP, et al. The obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving hemodialysis. Mayo Clin Proc. 2010;85:991-1001. 28. Dukkipati R, Kopple JD. Causes and prevention of protein-energy wasting in chronic kidney failure. Semin Nephrol. 2009;29:39-49.
A Simple Protein–Energy Wasting Score Predicts Survival in Maintenance Hemodialysis Patients Xavier Moreau-Gaudry, MD,* Guillaume Jean, MD,† Leslie Genet, MSc,‡ Dominique Lataillade, MD,§ Eric Legrand, MD,{ Franc¸ois Kuentz, MD,** and Denis Fouque, MD, PhD‡ Objective: Nutritional status is a powerful predictor of survival in maintenance hemodialysis patients but remains challenging to assess. We defined a new Protein Energy Wasting (PEW) score based on the nomenclature proposed by the International Society of Renal Nutrition and Metabolism in 2008. Design and Methods: This score, graded from 0 (worse) to 4 (best) was derived from 4 body nutrition compartments: serum albumin, body mass index, a normalized serum creatinine value, and protein intake as assessed by nPNA. Subjects: We applied this score to 1443 patients from the ARNOS prospective dialysis cohort and provide survival data from 2005 until 2008. Main Outcome Measure: Patients survival at 3.5 year. Results: Survival ranged from 84%-69% according to the protein-energy wasting score. There was a clear-cut reduction in survival (5%-7%; P , 0.01) for each unit decrement in the score grade. There was a 99% survival at 1 year for patients with the score of 4. In addition, the 6-month variation of this PEW score also strongly predicted patients’ survival (P , 0.01). Conclusion: A new simple and easy-to-get PEW score predicts survival in maintenance hemodialysis patients. Furthermore, increase of this nutritional score over time also indicates survival improvement, and may help to better identify subgroups of patients with a high mortality rate, in which nutrition support should be enforced. Ó 2014 by the National Kidney Foundation, Inc. All rights reserved.
Introduction
L
IFE EXPECTANCY OF maintenance hemodialysis patients (MHD) is severely reduced as compared with the general population1 and nutritional status remains a powerful predictor of morbidity and mortality.2-4 Protein–energy wasting (PEW) is the consequence of a combination of insufficient intake, uremic toxins, inflammation, and superimposed catabolism.5,6 Several studies have confirmed the importance of clinical assessment, body composition measurement, and laboratory analyses for diagnosing and quantifying malnutrition,7-9 however routinely, physicians are left without easy-to-use tools. As recently underlined by Kovesdy et al.,10 the lack of a gold standard test to establish PEW renders diagnosis
accuracy of malnutrition impossible, and using a combined score of nutritional parameters that encompasses various aspects of nutrition, an accurate diagnosis of PEW is more likely to be done. The International Society of Renal Nutrition and Metabolism proposed a uniformed nomenclature.11 However, whether this new classification predicts mortality has not yet been validated in MHD. In this study, we developed a new easy-to-use PEW score, based on various clinical and biological values, all being readily available at bedside. We hypothesized that this score will predict survival in MHD patients. The ARNOS prospective dialysis cohort was used to validate this hypothesis.
Subjects and Methods *
Centre de dialyse Porte de Provence, AGDUC, Montelimar, France. Centre du Rein Artificiel – Nephrocare, Ste Foy les Lyon, France. ‡ Department of Nephrology, CarMeN, CENS, Centre Hospitalier LyonSud, Universite de Lyon F-69622, France. § Service de nephrologie et hemodialyse, Centre Hospitalier – Avitum, Sallanches, France. { Service de nephrologie et hemodialyse, Centre hospitalier, Annonay, France. ** Centre de dialyse des Eaux Claires, AGDUC, Grenoble, France. Financial Disclosure: See Acknowledgments on page 399. Address correspondence to Denis Fouque, MD, PhD, Service de Nephrologie, Dialyse, Nutrition, Centre Hospitalier Lyon Sud, 69495 Pierre-Benite, France. †
E-mail: [email protected] Ó
2014 by the National Kidney Foundation, Inc. All rights reserved. 1051-2276/$36.00 http://dx.doi.org/10.1053/j.jrn.2014.06.008
Journal of Renal Nutrition, Vol 24, No 6 (November), 2014: pp 395-400
Study Population and Data Collection The ARNOS prospective study merged 25 dialysis units located in the Rh^ one-Alpes area in France and Switzerland. A total of 2,180 cases were collected during the study from June 1, 2005 to June 1, 2008 (Fig. 1). Each patient was evaluated every 6 months and values recorded until January 1, 2009. A total of 1,349 MHD were enrolled starting June 1, 2005. Starting January 1, 2006 until June 1, 2008, 831 incident patients were additionally enrolled. Exclusion criteria were age less than 18 year, patients receiving a weekly number of dialysis sessions different from 3 (n 5 288, Fig. 1). One patient was excluded because of a lost file. Because of missing data to calculate the score, 458 patients were excluded, leaving 1,433 patients for the 395
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Figure 1. Fluxogram of the ARNOS study. A total of 2,180 patients were recorded. One thousand four hundred thirty three patients were eligible for the score analyses (1,206 prevalent patients and 685 incident patients minus patients with missing data), and of these, 1,068 patients were available for the score variation over time analysis.
final score analysis. Hemodialfiltration was performed in 193 of them, e.g., 13.5%.
380 mmol/L/m2 threshold value was obtained by a receiver operating characteristics (ROC) curve analysis.
Score Values We defined the PEW score as the grading of 1 selected item in each of the 4 categories of the wasting syndrome11: (1) serum albumin (for biochemistry), (2) BMI (for anthropometry), (3) predialysis serum creatinine normalized by body surface area (SCr/BSA; for muscle mass), and (4) normalized protein nitrogen appearance (nPNA; for protein intake). The threshold values were (Table 1) serum albumin: 3.8 g/dL; BMI: 23 kg/m2; SCr/BSA: 380 mmol/L/m2, and nPNA: 0.8 g/kg/day. nPNA was calculated with the Garred formula.12 BSA was estimated by the Boyd formula13,14 as follows:
Score Calculation and Follow-Up If a patient had a value strictly greater than threshold, he received 1; if below threshold, the grade was 0 (see Table 1). The individual score was therefore comprised between 4 (normal nutritional status) and 0 (severe wasting). Because there was only 1 patient with a score of 0, for survival analyses we pooled the 0 and 1 grades in a single group (group 1) and finally analyzed survival for 4 categories of PEW scores from group 1 (severe wasting) to group 4 (normal). We also followed the score change after 6-month of follow-up and analyzed how an improvement/impairment in this score affected patients survival.
Body surface ðcm2 Þ50:00032073ðweightÞ0:728520:01883logðweightÞ 3ðheightÞ0:3
where weight, collected after the HD session, is in gram and height in centimeter. For the SCr/BSA variable, the
Table 1. Definition of the Protein–Energy Wasting Score Serum albumin (g/dL) Body mass index (kg/m2) SCr/BSA (mmol/L/m2) nPNA (g/kg/day)
#3.8 #23 #380 #0.8
nPNA, normalized protein nitrogen appearance; Scr/BSA, predialysis serum creatinine/body surface area (using postdialysis body weight).
Statistical Analyses The different statistical analyses were computed using ‘‘R’’ software version 2.12.1.15 Continuous variables were reported as mean 6 standard deviation or mean (confidence interval [CI], P value) and compared by the Student t-test. Qualitative variables were reported as percentage and compared using chisquared test. Cox proportional hazard models were used to estimate the hazard ratios (HRs). The Kaplan–Meier method was used to plot survival curves that were compared using the log-rank test. To test the robustness of the score in our population, we used a bootstrapping technique to obtain the mean log rank. CIs were obtained by the bootstrap
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PROTEIN-ENERGY WASTING SCORE Table 2. Patients Characteristics According to Their Score at Study Entry Clinical and Laboratory Parameters Number of patients Number of deaths Age (y) Male (%) Dry weight (kg) BSA, m2 Dialysis vintage (y) C-reactive protein (mg/L) Serum albumin (g/dL) SCr (mmol/L) SCr/BSA (mmol/L/m2) sp KT/V nPNA (g protein/kg/d)
Score 0-1
Score 2
Score 3
Score 4
All
Severe Wasting
Moderate Wasting
Slight Wasting
Normal Nutritional Status
1,433 299 66.7 6 14.5 61.7 68.3 6 14.9 1.78 6 0.23 4.2 6 6.0 13.5 6 26.9 36.0 6 5.1 692 6 258 392 6 167 1.60 6 0.52 1.12 6 0.31
276 74 72.1 6 12.1* 63.0† 62.9 6 13.9* 1.71 6 0.21* 4.2 6 7.4* 23.1 6 42.6* 32.5 6 4.7* 509 6 337* 304 6 272* 1.57 6 0.58 0.90 6 0.33*
529 124 69.3 6 13.0* 56.4* 68.3 6 16.1* 1.78 6 0.24* 3.7 6 5.4* 13.7 6 24.8* 34.4 6 4.2* 630 6 172* 361 6 113* 1.63 6 0.55 1.14 6 0.28*
460 81 63.0 6 15.6† 62.6† 68.7 6 13.1* 1.79 6 0.20* 4.7 6 5.7 9.3 6 16.3 38.0 6 4.5* 791 6 207* 446 6 120* 1.59 6 0.49 1.18 6 0.27†
168 20 60.0 6 14.6 74.4 76.2 6 13.7 1.90 6 0.20 4.8 6 6.2 7.5 6 13.5 41.4 6 2.4 918 6 162 484 6 77 1.54 6 0.42 1.23 6 0.26
BSA, body surface area; nPNA, normalized protein nitrogen appearance; SCr, serum creatinine. *P , .001 versus score 4. †P , .05 versus score 4.
1.0 0.9 Survival
0.8
Score = 1 Score = 2 Score = 3 Score = 4
0.7
Results Study duration was 3.5 years. A total of 1,433 patients were retained for analysis (Fig. 1) and their mean follow-up was 2.3 6 4.1 year (Table 2). Mean age was 67 years and dialysis vintage was 4.2 years. The major causes of morbidity remained hypertension and diabetes: hypertension was present in 78% and diabetes was reported in 34%. Chronic cardiac disease, stroke, or transient attacks was present in 23%, 10%, and 6%, respectively. Peripheral vascular disease occurred in 32%. Dialysis access was an arteriovenous fistula (81%) and 19% of patients had a permanent central venous catheter. BMI was 24.9 6 4.9 kg/m2. Residual renal function (diuresis . 500 mL/day) was present in 31% of patients. Mild chronic inflammation was present in more than 50% of patients, with a mean C-reactive protein of 14.0 mg/L (Table 2). Dialysis dose was considered adequate (single pool Kt/V of 1.6) as well as protein intake (1.1 g/kg/day). Table 2 details clinical and biological parameters according to the score grade. The moderately and severely wasted groups (Gr 1 and 2) included 805 patients, e.g., 56% of the cohort. All parameters were significantly more severe or impaired in group 1 compared with group 4, except for Kt/V. Figure 2 shows patients’ survival according to the PEW score. The HR was 0.80 (CI 0.60-1.07, P 5.14) between the severely malnourished group (Gr1) and the moderately malnourished group (Gr2), 0.61 (0.45-0.84, P , .005) between Gr1 and the slightly malnourished group (Gr3) and 0.38 (0.24-0.64, P , .001) between Gr1 and the normal nutritional status group (Gr4). In multivariate analysis, HRs were respectively 0.80 (CI 0.59-1.09, P 5 .16) between Gr1 and Gr2, 0.60 (CI, 0.42-0.85,
P , .005) between Gr1 and Gr3 and 0.33 (CI, 0.19-0.59, P , .001) between Gr1 and Gr4 (Table 3). It is also important to note that in the 4-score subgroup, which indicates a normal nutritional status, only 1 over 168 pts died during the first year of the study (Fig. 2). Finally, to get a score prediction in a shorter time, we tested its power at 1 year and found it also significantly predictive of mortality, with a HR of 0.63 (score 2 vs. 0-1, P , .001), 0.41 (score 3 vs. 0-1, P , .001), and 0.04 (score 4 vs. 0-1, P , .001).
0.6
bias–corrected accelerated method because the likelihood ratio distribution was not gaussian (q-q plot analysis). C-statistics were used to compare scores and variables used by the score.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Time (years)
Figure 2. Patients survival according to the protein–energy wasting score. One thousand four hundred thirty three patients were analyzed including prevalent patients followed from June 2005 to December 2008 and incident patients who initiated maintenance hemodialysis from January 2006 to June 2008. Patients with a score of 0 or 1 were pooled together because of limited effective. Group 1 (score of 0 or 1) had severe wasting, group 2 (score of 2) had moderate wasting, group 3 (score of 3) had slight wasting, and group 4 (score of 4) had a normal nutritional status.
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Table 3. Three-and-a-Half Year Survival Predictive Factors in 1,433 Maintenance Hemodialysis Patients Patients Characteristics and Score
HR
Gender (M/F) Diabetes Congestive heart failure Peripheral vascular disease Stroke Fistula C-reactive protein Sp KT/V Score 2 versus 0-1 Score 3 versus 0-1 Score 4 versus 0-1
1.04 1.04 1.91 1.20 1.30 0.83 1.01 0.84 0.80 0.60 0.33
CI min CI max P value 0.80 0.80 1.45 0.97 0.90 0.61 1.00 0.65 0.59 0.42 0.19
1.36 1.36 2.50 1.50 1.89 1.13 1.01 1.08 1.09 0.85 0.59
, .001
, .005 , .005 , .001
CI, confidence interval; HR, hazard ratio; sp KT/V, single pool KT/V. The normal nutritional status group (score of 4) and the slightly protein–energy wasted group (score of 3) show an independent significant protective effect on patients survival (multivariate Cox model).
0.8
Delta Score < 0 Delta Score = 0 Delta Score > 0
0.6
0.7
Survival
0.9
1.0
We also asked the question whether a change in this score after 6 months could predict survival (Fig. 3). This indeed was the case as there was a significant difference in survival between different score changes (P , .01, log-rank test). Compared with the patients who had their nutritional status impaired (D score , 0), patients who maintained their score after 6 months had a 30% improvement in survival (P , .05) at 3.5 years and patients who improved their score (D score . 0) reduced mortality by 53% (P , .005). To validate the robustness of the score in our population, we used a bootstrap analysis at 1 year and 3.5 year. Mean log
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Time (years)
Figure 3. Patients survival according to a 6-month change (D score) in protein–energy wasting score (n 5 1,068, logrank test, P , .01). Patients with no change in 6-month score had a better survival than those with a score impairment (D score , 0), P , .05, and patients who improved their score (D score . 0), P , .005; 47 deaths were recorded in 181 patients (negative D score); 114 deaths in 606 patients (null D score), and 41 deaths in 281 patients (positive D score).
ranks were highly significant, being 31.6 (CI, 15.8-54.1), P , .001 at 1 year and 19.3 (CI, 5.8-35.6), P , .001 at 3.5 year, respectively, confirming that each subgroup significantly differed by survival, and thus, the validity of the score in our population. Finally, to test the superiority of our score, we analyzed the predictive power of each parameter (albumin, creatinine/SC, nPNA, and BMI) compared with the global score by doing a backward stepwise regression analysis. Serum albumin brought the most important predictive power, and when albumin was present in the model, the score was not retained, and creatinine and BMI were also lost. Thus, most nutritional information generally used by clinicians was lost. When albumin was taken out of the model, the score remained significant versus corrected creatinine, versus nPNA and versus BMI taken individually as continuous variables. Thus, even if the weight of each parameter we selected in the score was not strictly equal, the score has by itself a clinical relevance and brings more information than each parameter alone except albumin. We verified this point by performing 2 multivariate Cox analyses, one with serum albumin only, and the other with serum albumin, BMI, corrected creatinine and nPNA. The last model was significantly improved at 1 year and 3.5 year compared with the single albumin model (P 5 .018 and .048, respectively). Thus, these analyses confirm that the present PEW score uses reliable predictive nutritional information and improves survival prediction compared with the use of albumin alone.
Discussion We identified a new nutritional score based on simple readily available parameters and show that it can predict survival in MHD patients with acceptable accuracy. Although an altered nutritional status is frequently reported in MHD, physicians are left without simple tools to identify and treat PEW because there is no single nutrition parameter, which can predict wasting.16-19 It is everyone’s hope to take advantage of a simple nutrition equation to implement recommendations and improve patients’ status.20 The score we developed here includes 1 parameter from each major group generally identified to interfere in CKD patients’ nutritional status: (1) biological parameters, (2) body composition, (3) muscle mass, and (4) nutrient intake. It seems indeed important to add to laboratory information (e.g., serum albumin, prealbumin, cholesterol, and lymphocyte count) to other more clinical information such as BMI, body weight, and fat mass.21,22 Muscle mass, which is the major part of the body strongly associated with survival, is nevertheless difficult to assess. Here, we chose to use predialysis serum creatinine that we normalized by body surface area. Indeed, SCr/BSA gave a better fit in the Cox model than raw serum creatinine. Normalizing serum creatinine seems of interest because it may allow using this score more widely in populations that differ by
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creatinine intake and metabolism (Afro-American, Caucasian, and Hispanic or Asian patients). This may be the reason why creatinine is not used routinely, although many studies have shown its nutritional interest.16,23-26 It should be noted that, creatinine prediction power being linear,25,27 it is arbitrary to set a threshold, however necessary to make discrete statistical analyses and clinical recommendations. Finally, we also added information on nutrient intake by entering in the score the protein intake as estimated by nPNA. This value, although not always recorded in dialysis facilities, belongs to all current international recommendations and can be obtained easily through the dialysis generator software. Added to the model, it improved survival prediction compared with albumin alone. It should be noted that the score by itself was not better than the 4 parameters added into the model as separate variables. This was not unexpected because information given by the score is already brought by all variables included into the score. However, we believe that this is the interest of this score to encourage the dialysis staff to pay attention to all these variables taken together. The present score could be obtained within minutes at bedside, with no additional equipment, at no expense and therefore be added to the arsenal of patient’ general record and follow-up. The final patients classification we obtained (Table 2) corresponds well to the published literature on the prevalence of nutritional disorders in dialysis patients: 37% of patients present with moderate and 19% with severe impairment in their nutritional status.11,28 Is this pragmatic score useful? Patients with a grading of 0 to 1 (severe PEW) presented a mortality of 27% at 3.5 year (Fig. 2). At 2 years, only 6.5% with a normal nutritional status died, whereas 26.5% died if they were severely wasted, which represents a 4 time greater mortality. Of note is the fact that, only 1 patient with a normal nutritional status died during the first year of follow-up (Fig. 2). Interestingly, intermediate score values discriminate from each other to predict survival. This should be underlined because a given score may be obtained with different body composition alterations and not only relies on a single measurement of serum albumin or BMI, as it is often done in routine practice. In addition, as shown in the Cox multivariate analyses, the power of the score appeared greater than the single use of albumin or creatinine taken separately. It is uneasy to predict if an obese dialysis patient, theoretically having a better survival, who presents with a low albumin level will have a PEW: adding serum creatinine, as a muscle information, and protein intake may direct the patient up- or down-ward on the score scale, and allow a better refining of his/her nutritional risk and survival. An example taken from the present cohort highlights the interest of the score at enrollment, 2 patients had the same serum albumin of 3.5 g/dL and same BMI of 23.8 kg/m2; patient A had an adjusted serum creatinine of 300 mmol/L/m2 and
a nPNA of 0.57 g/kg/day, whereas patient B presented with an adjusted serum creatinine of 520 mmol/L/m2 and a protein intake of 1.28 g/kg/day. Patient A had a score of 1 and died during the study, whereas patient B with a score of 3 survived the study. From information of serum albumin and BMI only, the 2 mostly used nutritional indexes, it would not have been possible to predict a different survival for these patients. Looking at serum albumin alone, a common attitude could be even more misleading. Two patients presented with a serum albumin of 3.5 g/dL; the first had a BMI of 33 kg/m2, a corrected serum creatinine of 530 mmol/L/m2 and a nPNA of 1.08 g protein/kg/day (e.g., a score of 3) survived the study; the second one had a BMI of 26 kg/m2, a corrected serum creatinine of 360 mmol/L/m2 and a nPNA of 0.76 g protein/kg/day (e.g., a score of 1) and died during the study. Finally, this score has another interesting property. It allows a longitudinal follow-up of patients to predict an improvement/impairment of survival. Indeed, a positive score increment after 6 months of follow-up was associated with a 35% reduction in mortality at 3.5 year (Fig. 3). Conversely, moving downward the PEW score was associated with a 43% increase in mortality. This observation indicates that an improvement in the wasting syndrome is associated with an increased survival. However, the use of this score as a surrogate for survival studies is questionable, because the present findings are only from epidemiologic nature. Further interventional studies should be designed to test this hypothesis. In conclusion, the routine use of this easy and robust score may help to identify malnutrition, now termed PEW, and therefore may call for aggressive renutrition programs. Follow-up of the score predicts survival, as its improvement is associated with reduced mortality. Further studies may be needed to verify the robustness of the PEW score in non-Western populations because of the differences in body composition and clinical practice.
Practical Application No single parameter allows to define protein energy wasting (PEW) during chronic kidney disease. We present here a simple and easy-to-get PEW scoring system from 0-4, based on 4 indicators of body composition, muscle mass, food intake, and nutritional biology. The lowest is the score, the more severe is the wasting syndrome. The PEW score predicts survival, and improving PEW score at 6-month interval is associated with extended survival. The PEW score may allow identifying patients at highrisk of wasting and target specific nutritional strategies.
Acknowledgments The ARNOS project was partially funded by an educational grant from AMGEN France. None of the authors received honoraria, travel grants, or other financial support for this study.
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