Prediction bias in spirometry reference equations can arise from combining equations for different age groups, rounding age or height to integers or using self-reported height. To assess the bias arising from these sources, the fit of 13 prediction equations was tested against the Global Lungs Initiative (GLI) dataset using spirometric data from 55,136 healthy Caucasians (54% female). The effects on predicted values of using whole-year age versus decimal age, and of a 1% bias in height, were quantified. In children, the prediction bias relative to GLI ranged from -22% to +17%. Switching equations at 18 yrs of age led to biases of between -846 (-14%) and +1,309 (+38%) mL. Using age in whole years rather than decimal age introduced biases from -8% to +7%, whereas a 1% overestimation of height introduced bias that ranged from +1% to +40%. Bias was greatest in children and adolescents, and in short elderly subjects. Using a single spirometry equation applicable across all ages and populations reduces prediction bias. Measuring and recording age and height accurately are also essential if bias is to be minimised. Copyright©ERS 2012.

Age- and height-based prediction bias in spirometry reference equations

F. Pistelli;
2012-01-01

Abstract

Prediction bias in spirometry reference equations can arise from combining equations for different age groups, rounding age or height to integers or using self-reported height. To assess the bias arising from these sources, the fit of 13 prediction equations was tested against the Global Lungs Initiative (GLI) dataset using spirometric data from 55,136 healthy Caucasians (54% female). The effects on predicted values of using whole-year age versus decimal age, and of a 1% bias in height, were quantified. In children, the prediction bias relative to GLI ranged from -22% to +17%. Switching equations at 18 yrs of age led to biases of between -846 (-14%) and +1,309 (+38%) mL. Using age in whole years rather than decimal age introduced biases from -8% to +7%, whereas a 1% overestimation of height introduced bias that ranged from +1% to +40%. Bias was greatest in children and adolescents, and in short elderly subjects. Using a single spirometry equation applicable across all ages and populations reduces prediction bias. Measuring and recording age and height accurately are also essential if bias is to be minimised. Copyright©ERS 2012.
2012
P. H., Quanjer; G. L., Hall; S., Stanojevic; T. J., Cole; J., Stocks; Hankinson, J. L.; Enright, P. L.; Zheng, J. P.; Arets, H. G. M.; Barbara, C.; Beardsmore, C. S.; Ben Saad, H.; Brunekreef, B.; Burney, P. G. J.; Eigen, H.; Falaschetti, E.; Fallon, B.; Gappa, M.; Gerbase, M. W.; Gislason, T.; Gore, C. J.; Gulsvik, A.; Hall, G. L.; Hankinson, J. L.; Henderson, A. J.; Janson, C.; Jenkins, C.; Karrasch, S.; Kerby, G. S.; Ku hr, J.; Kuster, S.; Lum, S.; Mannino, D. M.; Marks, G.; Nizankowska-Mogilnicka, E.; Nystad, W.; Perez-Padill, R.; Piccioni, P.; Pistelli, F.; Quanjer, P. H.; Schulz, H.; Stanojevic, S.; Soriano, J. B.; Tan, W. C.; Tomalak, W.; Turner, S. W.; Vilozni, D.; Vlachos, H.; West, S.; Wouters, E. F. M.; Zagami, D.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1122451
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