Evaluation of Indian Prediction Models for Lung Function Parameters: A Statistical Approach

Interpretation of lung function test parameters is usually based on comparisons of data with reference (predicted) values based on healthy subjects. Predicted values are obtained from studies of "normal" or "healthy" subjects with similar anthropometric and ethnic characteristics. Regression models are generally used to obtain the reference values from measurements observed in a representative sample of healthy subjects.

The study aims to carry out a statistical evaluation of the Indian prediction models of lung function parameters and critically evaluate the reference values for the same in an Indian context.

The screening and inclusion of the articles for the study was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Evaluation of the prediction models has been done with respect to modeling approach, regression diagnostics and methodology protocol. The suitability of the models has also been evaluated using a checklist comprising of 8 criteria developed using the American Thoracic Society (ATS) guidelines.

Using the PRISMA guidelines 32 articles with a total sample size of 25,289 subjects were included in the final synthesis. Multiple linear regression models were used in 27 articles, with one additionally using weighted least squares technique and 4 using step-wise regression method. Regression diagnostics as per the ATS guidelines were performed and reported by 22 articles. The prediction models were traditionally developed using ordinary least squares method (OLS) without examining the homoskedasticity of residuals. The quality assessment using the checklist developed revealed that only 5 articles satisfied more than 7 out of 8 criteria, and a further 8 articles satisfied less than 3 criteria of suitability of prediction models.

Indian prediction models for lung function models are traditionally based on linear regression models, however with more advancement in computational power for sophisticated statistical techniques, more robust prediction models are required in the Indian context.