Long-term survival after non-small cell lung cancer surgery:: Development and validation of a prognostic model with a preoperative and postoperative mode

Objective: At present, there is no prognostic model that is specific for prediction of survival after non-small cell lung cancer surgery. We aimed to develop a prognostic model that can be used to estimate the postoperative survival of individual patients. Methods: A total of 766 patients underwent resection for primary non-small cell lung cancer. Comorbid conditions were scaled according to the Charlson comorbidity index (CCI). Cox proportional hazard analyses were used to determine risk factors for survival. A prognostic model for survival with a preoperative and postoperative mode was established. Performance of the prognostic model, the CCI, and pathologic tumor stage were quantified by a concordance statistic to indicate discriminative ability. Results: The factors associated with an impaired survival were male sex, age, chronic obstructive pulmonary disease, congestive heart failure, any prior tumor, moderate-to-severe renal disease (preoperative and postoperative mode), clinical tumor stage (preoperative mode), type of resection, and pathologic tumor stage (postoperative mode). The discriminative performance was poor for the CCI (c = 0.55), better for pathologic tumor stage (c = 0.60) and for the preoperative mode (c = 0.61), and best for the postoperative mode (c = 0.65). The discriminative performance of the postoperative mode was better than the discriminative performance of the CCI (P < .0001), the preoperative mode (P < .0002), and pathologic tumor stage (P < .0001). The discriminative performance of the preoperative mode was better than the discriminative performance of the CCI (P < .0001) and similar (P = .90) to a model that only included pathologic tumor stage. Conclusions: The prognostic model, particularly the postoperative mode, successfully estimates long-term survival of individual patients and could help clinicians in clinical decision-making and treatment tailoring.