Accurate measurement of forced vital capacity (FVC) is important in pulmonary function testing. Since the rate of achieving spirometric end-of-test criteria is usually less than optimal, with resultant under-recording of the FVC, the current analysis proposes a logarithmic model of predicting FVC based on FEV1, FEV2, and FEV3, especially when end of test criteria are not met (i.e., no expiratory plateau or short exhalation time).
Estimated logFVC and exp(Estimated logFVC) were derived after multivariate analysis and construction of a logarithmic regression model from volume measurements within the first 3 seconds of exhalation (based on FEV1, FEV2, and FEV3). We developed the model on a large derivation cohort and subsequently evaluated it on a distinct validation cohort of patients.
The derivation group consisted of 35,885 consecutive spirometric tests performed in the Cleveland Clinic Foundation Pulmonary Function Laboratory. The equation derived was as follows: Estimated logFVC = –0.04 –0.416×logFEV1 –1.612×logFEV2 + 2.991×logFEV3 (R2 = 0.95, p < 0.0001, RMSE = 0.087). The equation was applied to an independent validation set of 61,290 spirometric measurements on as many consecutive, different patients. Based on the above equation, Exp (Estimated logFVC) = 0.10 + 0.963×FVC (R2 = 0.97, p < 0.0001, RMSE = 0.241). In the validation cohort, the prevalence of obstruction was 66% (based on values of the measured FEV1/FVC compared to NHANES III values). In the same cohort, the mean residual, i.e. the difference between estimated and measured FVC (± standard deviation) was 6.9 (± 238) mL.
Our predictive model based on logarithmic values of the spirometric measurements had a good diagnostic performance and behaved reasonably accurate in situations of short exhalation time and/or when no expiratory plateau is achieved.
Since FVC is frequently under-recorded with resultant over-estimation of FEV1/FVC and under-diagnosis of airflow obstruction, we showed that estimating FVC from FEV1, FEV2 and FEV3 using a logarithmic model can improve the precision of the estimation and offer practical diagnostic advantages.
Octavian Ioachimescu, None.