A model to predict the immediate postoperative FEV1 following major lung resections

Objective: FEV1 measured on the first postoperative day has shown to be a better predictor of complications than traditional ppoFEV1. Therefore, its estimation before operation may enhance risk stratification. The objective of this study was to develop and validate a model to predict FEV1 on the first postoperative day after major lung resection. Methods: FEV1 was prospectively measured on the first postoperative day in 272 patients submitted for lobectomy or pneumonectomy at two centers. A random sample of 136 patients was used to develop a model estimating the first day FEV1 by using multiple regression analysis including several preoperative and operative factors. The model was then validated by bootstrap analysis and tested on the other sample of 136 patients. Results: Factors reliably associated with postoperative first day FEV1 were age (p = 0.002), preoperative FEV1 (p <0.0001), the presence of epidural analgesia (p <0.0001), and the percentage of non-obstructed segments removed during operation (p = 0.001). The following model estimating the first day postoperative FEV1 was derived: -2.648 + 0.295 × age + 0.371 × FEV1 + 8.216 × epidural analgesia - 0.338 × percentage of non-obstructed segments removed during operation. In the validation set, the mean predicted first day postoperative FEV1 value did not differ from the observed one (42.6 vs 42.0, respectively; p = 0.3) and the plot of the observed versus the predicted first day FEV1 showed a satisfactory calibration. Conclusions: We developed a model predicting the first day postoperative FEV1. If future analyses will prove its role in stratifying the early postoperative risk, it may be integrated in preoperative evaluation algorithms to refine risk stratification.