%A Chhabra, S
%T Using arm span to derive height: Impact of three estimates of height on interpretation of spirometry
%9 Original Article
%D 2008
%J Annals of Thoracic Medicine
%R 10.4103/1817-1737.39574
%P 94-99
%V 3
%N 3
%U https://www.thoracicmedicine.org/article.asp?issn=1817-1737;year=2008;volume=3;issue=3;spage=94;epage=99;aulast=Chhabra
%8 July 1, 2008
%X **Background:** When standing height required to calculate forced vital capacity (FVC) cannot be measured, it can be derived from arm span using different methods.
** Objectives:** To compare three different estimates of height derived from arm span and investigate their impact on interpretation of spirometric data.
** Methods:** In a cross-sectional study, 517 subjects aged 7 to 76 years, with various respiratory diseases underwent spirometry. Three estimates of height were obtained from arm span: (a) by direct substitution (Ht_{ AS} ); (b) estimated height (Ht_{ est} ), obtained from the mean arm span:standing height ratio; and (c) predicted height (Ht_{ pred} ), obtained from arm span by linear regression analysis. Predicted values of forced vital capacity (FVC) obtained from these estimates were compared with those obtained from actual standing height (Ht_{ act} ), followed by Bland Altman analysis of agreement in the patterns of ventilatory impairment.
** Results:** The arm span was 5%-6% greater than the height. The difference increased with increasing height. Ht_{ AS} and the FVC predicted from it were significantly greater than the other measures of height and the related predicted FVCs respectively. Compared to Ht_{ act} , Ht_{ AS} gave a misclassification rate of 23.7% in taller subjects (Ht_{ act} > 150 cm) and 14.2% in shorter subjects in the patterns of ventilatory impairment. Misclassification rates were 6%-8% with Ht_{ est} and Ht_{ pred} . Agreement analysis showed that FVCs predicted from Ht_{ pred} had the best agreement with the FVC predicted from Ht_{ act} .
**Conclusions:** Among several methods of estimating height from the arm span, prediction by regression is most appropriate as it gives least errors in interpretation of spirometric data
%0 Journal Article
%I Wolters Kluwer Medknow Publications
%@ 1817-1737