4.6 Article

The predictive value of anthropometric parameters on mortality in haemodialysis patients

Journal

NEPHROLOGY DIALYSIS TRANSPLANTATION
Volume 26, Issue 4, Pages 1367-1374

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/ndt/gfq497

Keywords

anthropometry; cohort study; haemodialysis; nutrition; survival analysis

Funding

  1. Ministry of Science and Ecology of Serbia [175089]

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Background. Since protein-calorie malnutrition is a common factor influencing morbidity and mortality of haemodialysis patients, assessing their nutritional status is important. The aim of this study was to investigate the predictive value of anthropometric parameters on mortality and their interrelationship. Methods. The study included a cohort of 242 patients. The analysis involved baseline data obtained during the first calendar year after the patients entered the study (19942001) and repeated measurements for up to 132 months of follow-up (until 2004). Anthropometric measurements were made during the winter season and included skinfolds, mid-arm circumference (MAC), body height and weight. The percentage of body fat (% fat) was calculated from triceps (TSF), biceps, subscapular and suprailiac skinfolds (Disease Outcomes Quality Initiative (DOQI) guidelines) and mid-arm muscle circumference (MAMC) from MAC and TSF. Body mass index (BMI), Kt/V, normalized protein catabolic rate (NPCR) and cardiovascular co-morbidity were also determined and laboratory analyses undertaken. Results. Strong correlations were found among the anthropometric parameters. Extended Cox regression analysis selected % fat, MAC, MAMC and TSF in addition to age, ischaemic heart disease, congestive heart failure, Kt/V, haemoglobin, creatinine, albumin and NPCR as potential predictors of mortality. The same anthropometric parameters were found to be independent mortality predictors in corresponding models. The most predictive anthropometric factor was MAC. BMI was not a risk factor. Conclusion. Percentage of body fat, MAC, MAMC and TSF were independent predictors of mortality of haemodialysis patients, and MAC was the most predictive one.

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