OBJECTIVES: (1) To compare the performance of three spirometric algorithms developed to predict whether the total lung capacity (TLC) is reduced vs normal or increased, (2) to determine if forced expiratory volume at 6 s (FEV(6)) can be substituted for FVC in these algorithms, and (3) to determine if ascertainment bias was present in patients referred for the measurement of spirometry and TLC compared to patients referred for spirometry only. METHODS: We analyzed the results of 219 consenting consecutive patients referred to a New Zealand tertiary hospital respiratory laboratory for spirometry and TLC measurements. Spirometry results from 370 patients referred for spirometry but not lung volumes were used to test for potential ascertainment bias. Spirometry results were analyzed using the lower limit of normal (LLN) values from the third National Health and Nutrition Examination Study reference equations. The equations of Goldman and Becklake, and Crapo were used to classify TLC as normal or abnormal. Receiver operator characteristic curves were used to produce an algorithm using the LLN for FVC and FEV(6). The performances of previous algorithms and our own algorithms were analyzed for predicting a reduced lung volume against the "gold standard," plethysmographic TLC. RESULTS: All three algorithms predicted a reduced TLC with an accuracy of approximately 50%. In contrast, all algorithms predicted TLC was either normal or increased with an accuracy of > or = 99% regardless of the reference set used. The algorithms based on FEV(6) performed equally as well as the FVC algorithms. No ascertainment bias was found. CONCLUSIONS: This study provides evidence that spirometry-based algorithms can accurately predict when TLC is either normal or increased, and can also increase the a priori probability that TLC is reduced to approximately 50%. FEV(6) is equivalent to FVC in these predictions.
OBJECTIVES: (1) To compare the performance of three spirometric algorithms developed to predict whether the total lung capacity (TLC) is reduced vs normal or increased, (2) to determine if forced expiratory volume at 6 s (FEV(6)) can be substituted for FVC in these algorithms, and (3) to determine if ascertainment bias was present in patients referred for the measurement of spirometry and TLC compared to patients referred for spirometry only. METHODS: We analyzed the results of 219 consenting consecutive patients referred to a New Zealand tertiary hospital respiratory laboratory for spirometry and TLC measurements. Spirometry results from 370 patients referred for spirometry but not lung volumes were used to test for potential ascertainment bias. Spirometry results were analyzed using the lower limit of normal (LLN) values from the third National Health and Nutrition Examination Study reference equations. The equations of Goldman and Becklake, and Crapo were used to classify TLC as normal or abnormal. Receiver operator characteristic curves were used to produce an algorithm using the LLN for FVC and FEV(6). The performances of previous algorithms and our own algorithms were analyzed for predicting a reduced lung volume against the "gold standard," plethysmographic TLC. RESULTS: All three algorithms predicted a reduced TLC with an accuracy of approximately 50%. In contrast, all algorithms predicted TLC was either normal or increased with an accuracy of > or = 99% regardless of the reference set used. The algorithms based on FEV(6) performed equally as well as the FVC algorithms. No ascertainment bias was found. CONCLUSIONS: This study provides evidence that spirometry-based algorithms can accurately predict when TLC is either normal or increased, and can also increase the a priori probability that TLC is reduced to approximately 50%. FEV(6) is equivalent to FVC in these predictions.
Authors: Emily S Wan; John E Hokanson; James R Murphy; Elizabeth A Regan; Barry J Make; David A Lynch; James D Crapo; Edwin K Silverman Journal: Am J Respir Crit Care Med Date: 2011-04-14 Impact factor: 21.405
Authors: Sytse F Oudkerk; Firdaus A A Mohamed Hoesein; F Cumhur Öner; Jorrit-Jan Verlaan; Pim A de Jong; Jonneke S Kuperus; Michael Cho; Merry-Lynn McDonald; David A Lynch; Edwin K Silverman; James D Crapo; Barry J Make; Katherine E Lowe; Elizabeth A Regan Journal: J Rheumatol Date: 2019-05-01 Impact factor: 4.666
Authors: Emily S Wan; Peter J Castaldi; Michael H Cho; John E Hokanson; Elizabeth A Regan; Barry J Make; Terri H Beaty; MeiLan K Han; Jeffrey L Curtis; Douglas Curran-Everett; David A Lynch; Dawn L DeMeo; James D Crapo; Edwin K Silverman Journal: Respir Res Date: 2014-08-06
Authors: Sooky Lum; Vassiliki Bountziouka; Philip Quanjer; Samatha Sonnappa; Angela Wade; Caroline Beardsmore; Sunil K Chhabra; Rajesh K Chudasama; Derek G Cook; Seeromanie Harding; Claudia E Kuehni; K V V Prasad; Peter H Whincup; Simon Lee; Janet Stocks Journal: PLoS One Date: 2016-04-27 Impact factor: 3.240