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

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Indian lung function prediction models often use basic linear regression. This study highlights the need for more advanced statistical techniques to improve the accuracy of reference values for lung function tests in India.

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Area of Science:

  • Pulmonary Medicine
  • Biostatistics
  • Medical Informatics

Background:

  • Lung function test interpretation relies on reference values from healthy populations.
  • Predicted values are derived using regression models based on anthropometric and ethnic characteristics.
  • Current Indian prediction models for lung function are predominantly based on traditional linear regression.

Purpose of the Study:

  • To statistically evaluate existing Indian prediction models for lung function parameters.
  • To critically assess the suitability of reference values for lung function in the Indian population.
  • To identify limitations in current Indian lung function prediction models.

Main Methods:

  • Systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.
  • Evaluation of prediction models based on modeling approach, regression diagnostics, and methodology.
  • Suitability assessment using an 8-criterion checklist derived from American Thoracic Society (ATS) guidelines.

Main Results:

  • 32 articles and 25,289 subjects were included. Multiple linear regression was common (27 articles).
  • Regression diagnostics were reported in 22 articles, but homoskedasticity of residuals was often unexamined.
  • Only 5 articles met over 7 of 8 suitability criteria; 8 met fewer than 3 criteria.

Conclusions:

  • Traditional linear regression models are prevalent in Indian lung function prediction.
  • There is a need for more robust prediction models utilizing advanced statistical techniques in India.
  • Enhancements in computational power necessitate the development of sophisticated models for accurate lung function assessment.