A model to predict the decline of the forced expiratory volume in one second and the carbon monoxide lung diffusion capacity early after major lung resection

Abstract

The objective of the study was to develop regression models for the prediction of the decline of the forced expiratory volume in one second (FEV1) and the carbon monoxide lung diffusion capacity (DLCO) early after major lung resection. One hundred and ninety patients submitted to pulmonary lobectomy or pneumonectomy for lung cancer performed preoperative and early postoperative (mean 10.9 after operation) pulmonary function tests. One hundred and fifty of these patients also underwent DLCO measurements by the single breath method. The decline of FEV1 and DLCO were expressed as percentage losses from preoperative values. Stepwise multiple regression analyses were performed to develop two models estimating the percent reduction of FEV1 and DLCO from preoperative values. The multivariate procedures were then validated by bootstrap analyses. The following regression equations were derived: estimated percent reduction in FEV1 = 21.34 - (0.47 x age) + (0.49 x percentage of functioning parenchyma removed during operation) + (17.91 x COPD-index); estimated percent reduction in DLCO = 35.99 - (0.31 x age) - (36.47 x FEV1/FVC ratio) + (0.33 x DLCO) + (0.54 x percentage of functioning parenchyma removed during operation). The comparison between observed and estimated losses of FEV1 and DLCO (by using these regression equations) was not significantly different. We think the regression models generated in this study may be reliably used for risk stratification purposes.