Sample size calculations for continuous outcomes in clinical nutrition

In nutrition research, sample size calculations for continuous outcomes are important for the planning phase of many randomized trials and could also be relevant for some observational studies such as cohort and cross-sectional studies. However, only little literature dedicated to this topic exists within nutritional science. This article reviews the most common methods for sample size calculations in nutrition research. Approximate formulas are used for explaining concepts and requirements and for working through examples from the literature. Sample size calculations for the various study designs, which are covered, may all be seen as extensions of the sample size calculation for the basic two-group comparison through the application of suitable scaling factors and, possibly, modification of the significance level. The latter is needed for sample size calculations for multi-group designs and designs involving multiple primary outcomes. Like cluster-randomized designs, these types of study designs may be more challenging than standard sample size calculations. In such non-standard scenarios, there may be a need for consulting a biostatistician. Finally, it should be stressed that there may be many ways to plan a study. The final sample size calculation provided for a grant applicant, study protocol, or publication will often not only depend on considerations and input information as described in this article but will also involve restrictions in terms of logistics and/or resources.

Performance of Bioelectrical Impedance and Anthropometric Predictive Equations for Estimation of Muscle Mass in Chronic Kidney Disease Patients

Background: Patients with chronic kidney disease (CKD) are vulnerable to loss of muscle mass due to several metabolic alterations derived from the uremic syndrome. Reference methods for body composition evaluation are usually unfeasible in clinical settings.

Aims: To evaluate the accuracy of predictive equations based on bioelectrical impedance analyses (BIA) and anthropometry parameters for estimating fat free mass (FFM) and appendicular FFM (AFFM), compared to dual energy X-ray absorptiometry (DXA), in CKD patients.

Methods: We performed a longitudinal study with patients in non-dialysis-dependent, hemodialysis, peritoneal dialysis and kidney transplant treatment. FFM and AFFM were evaluated by DXA, BIA (Sergi, Kyle, Janssen and MacDonald equations) and anthropometry (Hume, Lee, Tian, and Noori equations). Low muscle mass was diagnosed by DXA analysis. Intra-class correlation coefficient (ICC), Bland-Altman graphic and multiple regression analysis were used to evaluate equation accuracy, linear regression analysis to evaluate bias, and ROC curve analysis and kappa for reproducibility.

Results: In total sample and in each CKD group, the predictive equation with the best accuracy was AFFM_Sergi (men, n = 137: ICC = 0.91, 95% CI = 0.79–0.96, bias = 1.11 kg; women, n = 129: ICC = 0.94, 95% CI = 0.92–0.96, bias = −0.28 kg). AFFM_Sergi also presented the best performance for low muscle mass diagnosis (men, kappa = 0.68, AUC = 0.83; women, kappa = 0.65, AUC = 0.85). Bias between AFFM_Sergi and AFFM_DXA was mainly affected by total body water and fat mass. None of the predictive equations was able to accurately predict changes in AFFM and FFM, with all ICC lower than 0.5.

Conclusion: The predictive equation with the best performance to asses muscle mass in CKD patients was AFFM_Sergi, including evaluation of low muscle mass diagnosis. However, assessment of changes in body composition was biased, mainly due to variations in fluid status together with adiposity, limiting its applicability for longitudinal evaluations.

Body Composition Tools for Assessment of Adult Malnutrition at the Bedside: A Tutorial on Research Considerations and Clinical Applications

Because of the key role played by the body's lean tissue reserves (of which skeletal muscle is a major component) in the response to injury and illness, its maintenance is of central importance to nutrition status. With the recent development of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition diagnostic framework for malnutrition, the loss of muscle mass has been recognized as one of the defining criteria. Objective methods to evaluate muscle loss in individuals with acute and chronic illness are needed. Bioimpedance and ultrasound techniques are currently the best options for the clinical setting; however, additional research is needed to investigate how best to optimize measurements and minimize error and to establish if these techniques (and which specific approaches) can uniquely contribute to the assessment of malnutrition, beyond more subjective evaluation methods. In this tutorial, key concepts and statistical methods used in the validation of bedside methods to assess lean tissue compartments are discussed. Body composition assessment methods that are most widely available for practice and research in the clinical setting are presented, and clinical cases are used to illustrate how the clinician might use bioimpedance and/or ultrasound as a tool to assess nutrition status at the bedside. Future research needs regarding malnutrition assessment are identified.