Protein energy wasting and long-term outcomes in nondialysis dependent chronic kidney disease

An Exploratory Study on a New Method for Nutritional Status Assessment in Patients with Chronic Kidney Disease

Malnutrition is a risk factor for disease progression and poor prognosis in chronic kidney disease (CKD). However, the complexity of nutritional status assessment limits its clinical application. This study explored a new method of nutritional assessment in CKD (stage 1-5) patients using the Subjective Global Assessment (SGA) as the gold standard and evaluated its applicability. The kappa test was used to analyze the consistency of the Renal Inpatient Nutrition Screening Tool (Renal iNUT) with SGA and protein-energy wasting. Logistic regression analysis was used to analyze the risk factors of CKD malnutrition and calculate the prediction probability of multiple indicators combined for the diagnosis of CKD malnutrition. The receiver operating characteristic curve of the prediction probability was drawn to evaluate its diagnostic efficiency. A total of 161 CKD patients were included in this study. The prevalence of malnutrition according to SGA was 19.9%. The results showed that Renal iNUT had a moderate consistency with SGA and a general consistency with protein-energy wasting. Age > 60 years (odds ratio, OR = 6.78), neutrophil-lymphocyte ratio > 2.62 (OR = 3.862), transferrin < 200 mg/dL (OR = 4.222), phase angle < 4.5° (OR = 7.478), and body fat percentage < 10% (OR = 19.119) were risk factors for malnutrition in patients with CKD. The area under the receiver operating characteristic curve of multiple indicators for the diagnosis of CKD malnutrition was 0.89 (95% confidence interval: 0.834-0.946, p < 0.001). This study demonstrated that Renal iNUT has good specificity as a new tool for the nutrition screening of CKD patients, but its sensitivity needs to be optimized. Advanced age, high neutrophil-lymphocyte ratio, low transferrin level, low phase angle, and low body fat percentage are risk factors for malnutrition in patients with CKD. The combination of the above indicators has high diagnostic efficiency in the diagnosis of CKD malnutrition, which may be an objective, simple, and reliable method to evaluate the nutritional status of patients with CKD.

An updated clinical prediction model of protein-energy wasting for hemodialysis patients

Background and aim

Protein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectively. We performed a cross-sectional study in hemodialysis patients to propose a novel PEW prediction model.

Materials and methods

A total of 380 patients who underwent maintenance hemodialysis were enrolled in this cross-sectional study. The data were analyzed with univariate and multivariable logistic regression to identify influencing factors of PEW. The PEW prediction model was presented as a nomogram by using the results of logistic regression. Furthermore, receiver operating characteristic (ROC) and decision curve analysis (DCA) were used to test the prediction and discrimination ability of the novel model.

Results

Binary logistic regression was used to identify four independent influencing factors, namely, sex (P = 0.03), triglycerides (P = 0.009), vitamin D (P = 0.029), and NT-proBNP (P = 0.029). The nomogram was applied to display the value of each influencing factor contributed to PEW. Then, we built a novel prediction model of PEW (model 3) by combining these four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and BMI, while the ISRNM diagnostic criteria served as model 1 and model 2. ROC analysis of model 3 showed that the area under the curve was 0.851 (95%CI: 0.799-0.904), and there was no significant difference between model 3 and model 1 or model 2 (all P > 0.05). DCA revealed that the novel prediction model resulted in clinical net benefit as well as the other two models.

Conclusion

In this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objective than traditional diagnostic criteria.

Association of Different Malnutrition Parameters and Clinical Outcomes among COVID-19 Patients: An Observational Study

Background: Malnutrition is highly prevalent in medical inpatients and may also negatively influence clinical outcomes of patients hospitalized with COVID-19. We analyzed the prognostic implication of different malnutrition parameters with respect to adverse clinical outcomes in patients hospitalized with COVID-19. Methods: In this observational study, consecutively hospitalized adult patients with confirmed COVID-19 at the Cantonal Hospital Aarau (Switzerland) were included between February and December 2020. The association between Nutritional Risk Screening 2002 (NRS 2002) on admission, body mass index, and admission albumin levels with in-hospital mortality and secondary endpoints was studied by using multivariable regression analyses. Results: Our analysis included 305 patients (median age of 66 years, 66.6% male) with a median NRS 2002-score of 2.0 (IQR 1.0, 3.0) points. Overall, 44 patients (14.4%) died during hospitalization. A step-wise increase in mortality risk with a higher nutritional risk was observed. When compared to patients with no risk for malnutrition (NRS 2002 < 3 points), patients with a moderate (NRS 2002 3−4 points) or high risk for malnutrition (NRS 2002 ≥ 5 points) had a two-fold and five-fold increase in risk, respectively (10.5% vs. 22.7% vs. 50.0%, p < 0.001). The increased risk for mortality was also confirmed in a regression analysis adjusted for gender, age, and comorbidities (odds ratio for high risk for malnutrition 4.68, 95% CI 1.18 to 18.64, p = 0.029 compared to patients with no risk for malnutrition). Conclusions: In patients with COVID-19, the risk for malnutrition was a risk factor for in-hospital mortality. Future studies should investigate the role of nutritional treatment in this patient population.