Reduction n TLCO and survival in a clinical population

Background flow best to express the level of transfer factor of the lung for carbon monoxide (T-LCO) has not been properly explored. Methods We used the most recent clinical data from 13 829 patients (54% male; 10% non-European ancestry; median age 60.5 years, range 20-97 years; median survival 3.5 years, range 0-20 years) to determine how best to express T-LCO function in terms of its relationship to survival. Results The proportion of subjects of non-European ancestry with Global Lung Function Initiative (GLI) T-LCO z-scores above predicted was reduced, but was significantly increased between -1.5 and -3.5, suggesting the need for ethnicity-appropriate equations. Applying GLI forced vital capacity (FVC) ethnicity methodology to GLI T-LCO z-scores removed this ethnic bias and was used for all subsequent analysis. T-LCO z-scores using the GLI equations were compared with Miller's USA equations with median T-LCO z-scores being -1.43 and -1.50 for GLI and Miller equations, respectively (interquartile range -2.8 to -0.3 and -2.4 to -0.7, respectively). GLI T-LCO z-scores gave the best Cox regression model for predicting survival. A previously proposed six-tier grading system for level of lung function did not show much separation in survival risk in the less-severe grades. A new four-tier grading based on z-scores of -1.645, -3 and -5 showed better separation of risk with hazard ratio for all-cause mortality of 2.0, 3.4 and 6.6 with increasing severity. Conclusion Applying GLI FVC ethnicity methodology to GLI T-LCO predictions to remove ethnic bias together with a new four-tier z-score grading best relates T-LCO function to survival.

Automated summary

The study explored the best way to express T-LCO function in relation to survival using clinical data from 13,829 patients, finding the need for ethnicity-appropriate equations and a new four-tier z-score grading system. This new grading system showed better separation of risk with increasing severity, providing a more accurate prediction of survival outcomes.