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New physiological quotients for lung function measures, like FEV1Q, better predict all-cause mortality risk than current standards. These quotients offer improved survival discrimination for patients undergoing lung function testing.

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Area of Science:

  • Pulmonary Medicine
  • Respiratory Physiology
  • Biostatistics

Background:

  • The FEV1Q (Forced Expiratory Volume in 1 second Quotient) is a physiological metric comparing measured FEV1 to a minimal survival threshold.
  • Existing lung function metrics may not optimally predict mortality risk.
  • Developing new physiological quotients could enhance patient outcome assessment.

Purpose of the Study:

  • To derive physiological quotients for key lung function parameters.
  • To evaluate the association of these novel quotients with all-cause mortality.
  • To compare the discriminative performance of physiological quotients against traditional metrics.

Main Methods:

  • Analysis of adult lung function data from Cambridge University Hospital and Royal Papworth Hospital (2016-2024).
  • Investigation of 1st percentile stability for FEV1, FVC, FEV1/FVC, DLCO, KCO, VA, and TLC across age and sex.
  • Calculation of physiological quotients (measured value/1st percentile) and association with all-cause mortality via Cox regression and Harrell's C statistics.

Main Results:

  • Data from 13,771 patients analyzed; mean follow-up ranged from 5.5 to 5.8 years.
  • 1st percentile values showed stability across age for most parameters, but sex differences were noted for FEV1, FVC, VA, and TLC.
  • Physiological quotients demonstrated superior Harrell's C statistics compared to raw values, z-scores, and percent predicted, indicating enhanced survival discrimination.

Conclusions:

  • Physiological quotients offer superior discriminative ability for predicting all-cause mortality.
  • These quotients represent a potentially valuable alternative standard for interpreting lung function test results.
  • The findings support the clinical utility of physiological quotients in risk stratification.