Predicting lung function following lobectomy: A new method to adjust for inherent selection bias

Background: Predictions that overestimate post-lobectomy lung function are more likely than underestimates to lead to lobectomy. Studies of post-lobectomy lung function have included only surgical patients, so overestimates are overrepresented. This selection bias has led to incorrect estimates of prediction bias, which has led to inaccurate threshold values for determining lobectomy eligibility. Objective: The objective of this study was to demonstrate and adjust for this selection bias in order to arrive at correct estimates of prediction bias, the 95% limits of agreement, and adjusted threshold values for determining when exercise testing is warranted. Methods: We conducted a retrospective study of patients evaluated for lobectomy. We used multiple imputations to determine postoperative results for patients who did not have surgery because their predicted postoperative values were low. We combined these results with surgical patients to adjust for selection bias. We used the Bland-Altman method and the bivariate normal distribution to determine threshold values for surgical eligibility. Results: Lobectomy evaluation was performed in 114 patients; 79 had lobectomy while 35 were ineligible based on predicted values. Prediction bias using the Bland-Altman method changed significantly after controlling for selection bias. To achieve a postoperative FEV1 > 30% and DLCO ≥30%, a predicted FEV1 > 46% and DLCO ≥53% were required. Compared to current guidelines, using these thresholds would change management in 17% of cases. Conclusion: The impact of selection bias on estimates of prediction accuracy was significant but can be corrected. Threshold values for determining surgical eligibility should be reassessed.