Nutritional screening tools for adult cancer patients: A hierarchical Bayesian latent-class meta-analysis

Xiaoli Ruan; Rena Nakyeyune; Yi Shao; Yi Shen; Chen Niu; Zhaoping Zang; Toni Miles; Fen Liu

doi:10.1016/j.clnu.2020.09.033

Nutritional screening tools for adult cancer patients: A hierarchical Bayesian latent-class meta-analysis

Clin Nutr. 2021 Apr;40(4):1733-1743. doi: 10.1016/j.clnu.2020.09.033. Epub 2020 Oct 3.

Authors

Xiaoli Ruan¹, Rena Nakyeyune¹, Yi Shao¹, Yi Shen¹, Chen Niu¹, Zhaoping Zang¹, Toni Miles², Fen Liu³

Affiliations

¹ Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China.
² Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States.
³ Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China. Electronic address: liufen05@ccmu.edu.cn.

PMID: 33041089
DOI: 10.1016/j.clnu.2020.09.033

Abstract

Background & aims: Cancer treatment requires attentiveness to its broader effect on the body. Cancer's effect on appetite, strength, and body composition is contained in the summary term malnutrition. The tools used to detect malnutrition are a critical part of effective cancer care. In clinical care, selection of any specific tool is random. The relative validity of these tools have not been systematically compared. Using hierarchical Bayesian latent-class meta-analysis methods, this report compares three tools used for adult cancer patients - the Mini Nutritional Assessment (MNA), the Nutritional Risk Screening 2002 (NRS-2002) and the Patient Generated Subjective Global Assessment (PG-SGA).

Method: Drawing from English and Chinese language databases, a broad pool of eligible studies were identified for further selection and assessment. Using the hierarchical summary receiver operating characteristic (HSROC) model, pooled sensitivity, specificity, and other measurements the accuracy of the three tools were compared.

Result: A total of 37 eligible studies involving the MNA, NRS-2002 and PG-SGA were included in this meta-analysis. The pooled sensitivity was 0.910 (95% CI: 0.763 to 0.970) for MNA, 0.747 (95% CI: 0.680 to 0.804) for NRS-2002, and 0.964 (95% CI: 0.913 to 0.986) for PG-SGA. The pooled specificity was 0.720 (95% CI: 0.623 to 0.800) for MNA, 0.854 (95% CI: 0.808 to 0.891) for NRS-2002, 0.905 (95% CI: 0.807 to 0.956) for PG-SGA, respectively. The back-calculated likelihood ratio (LR) showed that MNA had a low negative likelihood ratio (LR^-), NRS-2002 corresponded to a high positive likelihood ratio (LR⁺) and PG-SGA represented the best LR⁺ and LR^-.

Conclusions: While there is no standard approach to assessment of malnutrition, the PG-SGA has the best diagnostic performance with cancer patients. Further work is needed to refine the utility of these tools in larger clinical samples.

Keywords: Cancer; Malnutrition; Nutrition; Nutritional screening.

Publication types

Meta-Analysis

MeSH terms

Adult
Aged
Aged, 80 and over
Bayes Theorem
Female
Humans
Male
Malnutrition / complications*
Malnutrition / diagnosis*
Middle Aged
Neoplasms / complications*
Nutrition Assessment*
Nutritional Status
Young Adult