E Raywood

Acceptability, Precision and Accuracy of 3D Photonic Scanning for Measurement of Body Shape in a Multi-Ethnic Sample of Children Aged 5-11 Years: The SLIC Study.

Background Information on body size and shape is used to interpret many aspects of physiology, including nutritional status, cardio-metabolic risk and lung function. Such data have traditionally been obtained through manual anthropometry, which becomes time-consuming when many measurements are required. 3D photonic scanning (3D-PS) of body surface topography represents an alternative digital technique, previously applied successfully in large studies of adults. The acceptability, precision and accuracy of 3D-PS in young children have not been assessed. Methods We attempted to obtain data on girth, width and depth of the chest and waist, and girth of the knee and calf, manually and by 3D-PS in a multi-ethnic sample of 1484 children aged 5–11 years. The rate of 3D-PS success, and reasons for failure, were documented. Precision and accuracy of 3D-PS were assessed relative to manual measurements using the methods of Bland and Altman. Results Manual measurements were successful in all cases. Although 97.4% of children agreed to undergo 3D-PS, successful scans were only obtained in 70.7% of these. Unsuccessful scans were primarily due to body movement, or inability of the software to extract shape outputs. The odds of scan failure, and the underlying reason, differed by age, size and ethnicity. 3D-PS measurements tended to be greater than those obtained manually (p<0.05), however ranking consistency was high (r2>0.90 for most outcomes). Conclusions 3D-PS is acceptable in children aged ≥5 years, though with current hardware/software, and body movement artefacts, approximately one third of scans may be unsuccessful. The technique had poorer technical success than manual measurements, and had poorer precision when the measurements were viable. Compared to manual measurements, 3D-PS showed modest average biases but acceptable limits of agreement for large surveys, and little evidence that bias varied substantially with size. Most of the issues we identified could be addressed through further technological development.

Lung function in children in relation to ethnicity, physique and socioeconomic factors

Can ethnic differences in spirometry be attributed to differences in physique and socioeconomic factors? Assessments were undertaken in 2171 London primary schoolchildren on two occasions 1 year apart, whenever possible, as part of the Size and Lung function In Children (SLIC) study. Measurements included spirometry, detailed anthropometry, three-dimensional photonic scanning for regional body shape, body composition, information on ethnic ancestry, birth and respiratory history, socioeconomic circumstances, and tobacco smoke exposure. Technically acceptable spirometry was obtained from 1901 children (mean (range) age 8.3 (5.2–11.8) years, 46% boys, 35% White, 29% Black-African origin, 24% South-Asian, 12% Other/mixed) on 2767 test occasions. After adjusting for sex, age and height, forced expiratory volume in 1 s was 1.32, 0.89 and 0.51 z-score units lower in Black-African origin, South-Asian and Other/mixed ethnicity children, respectively, when compared with White children, with similar decrements for forced vital capacity (p<0.001 for all). Although further adjustment for sitting height and chest width reduced differences attributable to ethnicity by up to 16%, significant differences persisted after adjusting for all potential determinants, including socioeconomic circumstances. Ethnic differences in spirometric lung function persist despite adjusting for a wide range of potential determinants, including body physique and socioeconomic circumstances, emphasising the need to use ethnic-specific equations when interpreting results. Ethnic differences in spirometry cannot simply be attributed to differences in physique and socioeconomic factors http://ow.ly/R8EaR

P184 Calculation of conductive inhomogeneity in children with severe cf lung disease: which method works?

Introduction Convection Dependent Inhomogeneity (CDI, a measure of ventilation inequality among larger lung units) quantified by Scond cannot be assessed in subjects with severe ventilation inhomogeneity (VI) as assumptions underlying the calculation are invalid; an alternate index that has been suggested is Scond.1 Aim To compare these two methods of CDI assessment in CF children Methods Children with cystic fibrosis (CF; 67) and healthy controls (61) performed multiple breath washout with sulphur hexaflouride measured using mass spectrometry. Scond was calculated from 1.5 to 6 turnovers and Scond* from breath 2 to 3 turnovers. Results All measures of VI were significantly higher for CF vs control, mean difference: LCI 4.0, Scond 0.054, Scond* 0.081. In CF, LCI correlated better with Scond* than Scond (See figure: correlation coefficient LCI vs. Scond* 0.75; LCI vs. Scond 0.42). If children with moderate-severe VI (LCI > 11) were excluded there was an improved correlation for both relationships (correlation coefficient LCI vs. Scond 0.83; LCI vs. Scond* 0.86). An asymptote for the Scond vs LCI relationship was at Scond 0.07 and Scond* 0.13. Conclusion Scond* quantifies the mechanism of VI in moderate to severe lung disease, but it may reach asymptote in very severe VI. Reference Verbanck. Respiratory Physiology & Neurobiology, 2013. Abstract P184 Figure 1 CF triangles, Control circles. A. Scond vs LCI b, Scond* vs LCI c. Scond* vs Scond with line of equivalence

P249 Comparison of physiological versus mathematical methods for quality control in mbw normalised phase iii analysis

Background Breathing pattern cannot be controlled in small children, so multiple breath washout SnIII analysis has to exclude inadequate volume breaths. Aim To compare an existing mathematical breath exclusion algorithm with a physiological method. Methods School age children with CF (30) and controls (30) performed SF6MBW with mass spectrometer, with uncontrolled tidal breathing. Two different breath exclusion methods were compared, with exclusion based on: 1) Expired tidal volume (VT) deviating by >25% of the median VT1 2) VT <3 Langley dead space2 volume or 90% bigger than the median VT Runs with >33% excluded breaths were removed. Volume corrected Scond was calculated from subjects with 3 valid runs. Results Far fewer subjects were excluded by the physiological Langley method, than by the mathematical method (Table). The mean and SD for Scond was identical by both methods, implying that the mathematical algorithm excludes valid data. Conclusion A physiological approach to data cleaning prior to SnIII analysis allows retention of data that would be inappropriately excluded mathematically. References Bigler A, et al. Paediatric Pulmonol 2015;50(8):805–13. Langley FE, et al. Colloques INSERM 1975;51:209–212. Abstrct P249 Table 1

S11 Feasibility of conducting complex physiological measurements in london primary schools: the Size & Lung function in children (SLIC) Study

Despite recognised ethnic differences in lung function, most reference ranges are based on White subjects. Ethnic minorities comprise 40% of the London population, which impacts on healthcare provision. Even when available, selection of appropriate equations is complicated by the increase in admixed populations and complexities of defining ‘ethnicity’. As part of the Wellcome Trust SLIC study (www.ucl.ac.uk/slic) to determine the extent to which body shape, size and composition contributes to ethnic differences in lung function, we examined the feasibility of conducting complex physiological measurements in a multi-ethnic population of London primary school children. Methods 14 London schools participated in the study. Science workshops were presented one week prior to commencing assessments. Consent forms and information packs were distributed to all children. All children with parental consent were eligible and were categorised into 4 broad ethnic groups: White; Black; South-Asian (Indian subcontinent) and Other/mixed. Assessments were performed at school in 5 11 year-old children and included detailed anthropometry, 3D phototonic scan for body shape; body composition; spirometry and saliva samples (cotinine and DNA analysis). Results Parental consent for anthropometry and spirometry was obtained in 54% of those approached. Amongst these, 88% and 96% provided specific consent for DNA samples and access to GP records respectively (Table 1). Assessments were performed in 2175 children (mean (SD)age: 8.22(1.63); 34%White; 29%Black; 25%South-Asian; 12%Other/mixed ethnicities), 1045(48%) of whom had follow-up assessments a year later. Preliminary analysis indicates: 18% had chronic respiratory illness or acute symptoms at time of test. 12% children had a diagnosis of ‘asthma ever’, with 6% having current asthma (Table 1). Acceptable spirometry1 was obtained from 1574(72%) healthy children. Abstract S11 Table 1. Consent, asthma status &spirometry success rates of study population White Black S-Asian Other/mixed Total Tested (% boys) 742 (49.7%) 629 (43.6%) 540 (48.7%) 264 (46.6%) DNA consent 89.3% 84.0% 85.8% 92.2% GP record access consent 97.1% 93.8% 93.6% 97.4% Asthma: ever 11.6% 11.1% 8.9% 19.7% Asthma: current 5.5% 4.6% 5.6% 7.2% Totalspirometrya 533 (71.8%) 435 (69.2%) 411 (76.1%) 195 (73.9%) Data presented as %. Abbreviations: DNA: Deoxyribonucleic acid (for genetic ancestry); GP: General Practitioner; Current asthma: defined as those having symptoms and/or asthma medication over the past 12 months; a based on data from healthy children and after exclusions from poor health and poor performance. Summary Conducting a field study to undertake complex physiological measurements is feasible even in young children. However, the relatively high prevalence of chronic or acute respiratory disease at time of testing in this age group, combined with exclusions due to technically unsatisfactory spirometry means that results from ~30% of children may be excluded if analysis of results is to be based on a ‘healthy’ population. Such factors must be accounted for when designing respiratory field studies to ensure adequate sample size to reach definitive conclusions. Reference Kirkby et al. Pediatr Pulmonol 2008.