Figure 1

Flow chart for diagnostic algorithm. In clinical practice the diagnosis of COPD is based on multiple variables. As the simplest model we chose a three PFT parameters approach in which an initial COPD YES/NO diagnosis based on FEV/FVC levels was corrected if FEV1 and RV/TLC levels were altered counterintuitively*. * As thresholds for FEV1 and RV/TLC levels different cut-off levels were used and kappa statistics calculated for all alternatives. Each change in COPD diagnosis only materializes if both parameters deviate by ≤ 5/7.5/10/12.5/15/20% from 100% of the predicted value. Example: If deviations of 10% (from 100%) are chosen as thresholds for both FEV1 and RV/TLC (as % of predicted) in order to change the GOLD-COPD diagnosis from ¹⁾ 'yes' into 'no' (i.e., FEV1 ≥ 90% and RV/TLC ≤ 110%; [²⁾ or vice versa, from 'no' into 'yes', FEV1 < 90% and RV/TLC > 110%]), then the number of misclassified patients (false positives + false negatives) is reduced from 69 to 33, and κ- statistics improve from 0.64 to 0.83. Abbreviations: as in table 1.