br composition assessments in lung cancer and cancers
composition assessments in lung cancer [18,20] and cancers of the head and neck . Our previous research used the BIVA method to examine associations between hydration status, symptoms and survival in advanced cancer patients .
BIVA RXc z-score analysis facilitates comparisons between populations
Statistical conversion of BIVA measurements to z-scores enables researchers to compare body composition of different study pop-ulations . Piccoli et al.  used this method to compare BIVA data for a variety of disease groups. To date, no studies have used the BIVA RXc z-score method to synthesise cancer populations evaluated with BIVA. Consequently, there is potential to use the BIVA RXc z-score method to evaluate body composition by cancer type and severity. Such information will potentially help support nutritional assessment and management in cancer.
To determine the feasibility of the BIVA RXc z-score method to compare body composition in cancer populations using published bioimpedance data.
Materials and methods
A systematic review reporting the use of BIVA in advanced cancer (published by Nwosu et al., 2013 ) was used to identify previous studies using BIVA to evaluate body composition in advanced cancer. Further, an electronic search of the literature using MEDLINE, EMBASE and Pubmed (combining keywords of “bioelectrical impedance vector analysis” and “Neoplasms [MesH]”, limited to English language and humans) was conducted to identify relevant studies.
Inclusion criteria for studies
Articles were eligible for review provided that they involved the use of BIVA in adult humans with cancer. The following data was required for the RXc z-score analysis: (i) R/H (Ohm/m) and Xc/H (Ohm/m) mean for the studied population, (ii) studied MitomycinC size, (iii) sex-specific bioimpedance data and (iv) details of the reference population used for the analysis. Minimum standards for the reference population were as follows: the total sample size n 100, the R/H (Ohm/m) mean, R/H (Ohm/m) standard deviation (SD), Xc/H (Ohm/m) mean and Xc/H (Ohm/m) SD. The Piccoli 1995 reference population (Caucasian Europeans, males (n ¼ 354) and females (n ¼ 372) aged 18e85 years, body mass index (BMI) 16e31 kg/m2, Italy, analyser ¼ Akern-RJL systems ) was used for studies which did not meet the minimum reference standard. We selected the Piccoli data as it was the most commonly selected reference population for studies evaluating the BIVA method.
Exclusion criteria for studies
The following articles were excluded: Non English studies; those reporting paediatric populations; absent data to facilitate the BIVA RXc z-score analysis (see inclusion criteria).
BIVA software and RXc z-score analysis
BIVA was conducted using software developed by Professor Antonio Piccoli, University of Padova . The mean vector imped-ance measures for study populations were transformed into standard deviates with respect to the mean and standard deviation and
compared against their reference population . The z-score is the number of standard deviations away from the mean value of the reference group . Z-scores can provide information about an individual measured score, relative to others in the distribution . Transformation of the BIVA measurements to z-scores facilitates comparison between different conditions and diseases (Fig. 2). Using the RXc z-score graph, individuals within the 50% tolerance ellipse are considered to have normal body composition, whereas those in the 75% and 95% tolerance ellipses are abnormal .
Vectors were plotted on the RXc z-score graph to facilitate data comparison. Vectors plotted within the 50% tolerance ellipse were considered normal. Based on data from the Piccoli study , the BIVA RXc z-score graph was divided into four quadrants to classify body composition of populations within the 75% and 95% (i.e. abnormal) tolerance ellipses. These quadrants were (i) Athletic (high cell mass), top left, (ii) Cachexia (low cell mass), bottom right;
(iii) Oedema, bottom left and (iv) Dehydrated, top right (Fig. 2). Body composition was determined according to the plotted vector po-sition. Further details on the equations used to calculate the RXc z-score graph analysis are available in the appendix.
This study was a secondary analysis of previously published research. Therefore, ethical approval was not required.
The literature search returned 15 full text articles using BIVA in people with cancer (Fig. 3). Two of these articles were rejected as they were not specific to patients with advanced cancer. Two studies (Lundberg et al.  and Gnagnarella et al. ) were excluded as insufficient data was available to facilitate the RXc z-score analysis. Of the remaining eleven studies, some presented the same BIVA data. These included two different studies, which both reported data for the same breast cancer sample [30,31]. Similarly, two studies reported data for the same head and neck cancer sample [21,32]. We grouped the relevant studies together to avoid confusion. Consequently, nine of the eleven eligible studies were included. These nine studies provided data for seven male and three female populations (Table 1). The studies described different cancer types and stages, which included advanced cancer of different origin ; lung cancer (including a sample of patients in remission) [18,20], breast cancer , head and neck cancer [21,33] and gynaecological cancer . Details of patient demographics, type of analyser and BIVA z-score analysis are presented in Table 1.