title: Long-term survival after non-small cell lung cancer surgery: development and validation of a prognostic model with a preoperative and postoperative mode.
abstract: 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.
authors.affiliation: Department of Cardio-Thoracic Surgery, Erasmus MC, Rotterdam, The Netherlands. o.birim@erasmusmc.nl
authors.forename: Ozcan
authors.initials: O
authors.lastname: Birim
authors.forename: A Pieter
authors.initials: AP
authors.lastname: Kappetein
authors.forename: Marco
authors.initials: M
authors.lastname: Waleboer
authors.forename: John P A
authors.initials: JP
authors.lastname: Puvimanasinghe
authors.forename: Marinus J C
authors.initials: MJ
authors.lastname: Eijkemans
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.forename: Michel I M
authors.initials: MI
authors.lastname: Versteegh
authors.forename: Ad J J C
authors.initials: AJ
authors.lastname: Bogers
mesh_terms: D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000150:complications / Q000401:mortality* / Q000473:pathology / Q000601:surgery*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000150:complications / Q000401:mortality* / Q000473:pathology / Q000601:surgery*; D008297:Male; D015233:Models, Statistical; D011182:Postoperative Care; D011300:Preoperative Care; D011379:Prognosis; D012189:Retrospective Studies; D015996:Survival Rate; D013997:Time Factors