Using spirometry results in occupational medicine and research: Common errors and good practice in statistical analysis and reporting

Population model of longitudinal FEV1 data in asthmatics: meta-analysis and predictability of placebo response

Asthma is an obstructive lung disease where the mechanism of disease progression is not fully understood hence motivating the use of empirical models to describe the evolution of the patient's health state. With reference to placebo response, measured in terms of FEV1 (Forced Expiratory Volume in 1 s), a range of empirical models taken from the literature were compared at a single trial level. In particular, eleven GSK trials lasting 12 weeks in mild-to-moderate asthma were used for the modelling of longitudinal placebo responses. Then, the chosen exponential model was used to carry out an individual participant data meta-analysis on eleven trials. A covariate analysis was also performed to find relevant covariates in asthma to be accounted for in the meta-analysis model. Age, gender, and height were found statistically significant (e.g. the taller the patients the higher the FEV1, the older the patients the lower the FEV1, and females have lower FEV1). By truncating each trial at week 4, the predictive properties of the meta-analysis model were also investigated, showing its ability to predict long-term FEV1 response from truncated trials. Summarizing, the study suggests that: (i) the exponential model effectively describes the placebo response; (ii) the meta-analysis approach may prove helpful to simulate new trials as well as to reduce trial duration in view of its predictive properties; (iii) the inclusion of available covariates within the meta-analysis model provides a reduction of the inter-individual variability.

Predictive equations for lung function based on a large occupational population in North China

OBJECTIVES

The currently used predictive equations of lung function in North China were derived from early study and have not been updated for nearly two decades.

METHODS

Using American Thoracic Society (ATS) standards, sex-specific spirometric predictive equations for forced vital capacity (FVC), forced expiratory volume in one second (FEV(1)), ratio of FEV(1) to FVC (FEV(1)%) and forced expiratory flow at 25-75% of forced vital capacity (FEF(25-75%)) were derived from 2,897 asymptomatic, lifelong non-smokers (1,208 males, 1,689 females) from a large occupational population in North China. Stepwise multiple regressions were carried out to identify the best predictors of lung function parameters and predictive equations. Independent variables considered for inclusion in predictive equations including age, height, weight and chest circumference were examined.

RESULTS

Age and height were found to be necessary variables for all lung function parameters. Weight was a significant variable in only half of our equations. Chest circumferences (expired or inspired) was excluded as they are not practical in use. Data from 255 apparently healthy non-smokers were used to validate the equations by comparing percentage predicted values and proportion of subjects with normal predicted values with those from the study group, and a high accordance was obtained. Other equations published and used in North China do not appear to offer advantages over these equations.

CONCLUSIONS

These newly developed predictive equations should ideally be applied to calculate lung function for adult individuals and populations as reference values in North China.

Evaluation of flow-volume spirometric test using neural network based prediction and principal component analysis

In this work, an attempt has been made to enhance the diagnostic relevance of spirometric pulmonary function test using neural networks and Principal Component Analysis (PCA). For this study, flow-volume curves (N = 175) using spirometers were generated under standard recording protocol. A method based on neural network is used to predict the most significant parameter, FEV(1). Further, PCA is used to analyze the interdependency of the parameters in the measured and predicted datasets. Results show that the back propagation neural network is able to predict FEV(1) both in normal and abnormal cases. The variation in the magnitude and direction of parameters in the contribution of the principal components shows that FEV(1) is a significant discriminator of normal and abnormal datasets and is further confirmed by the percentage variance in the first few principal components. It appears that this method of prediction and principal component analysis on the measured and predicted datasets could be useful for spirometric pulmonary function test with incomplete data.