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.
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: Takashi Eguchi; Sarina Bains; Ming-Ching Lee; Kay See Tan; Boris Hristov; Daniel H Buitrago; Manjit S Bains; Robert J Downey; James Huang; James M Isbell; Bernard J Park; Valerie W Rusch; David R Jones; Prasad S Adusumilli Journal: J Clin Oncol Date: 2016-10-31 Impact factor: 44.544
Authors: Chi-Fu Jeffrey Yang; Nicholas R Mayne; Hanghang Wang; Ryan R Meyerhoff; Sameer Hirji; Betty C Tong; Matthew Hartwig; David Harpole; Thomas A D'Amico; Mark Berry Journal: Ann Thorac Surg Date: 2016-05-25 Impact factor: 4.330
Authors: Alex C Asiimwe; Fraser J H Brims; Neil P Andrews; Dave R Prytherch; Bernie R Higgins; Sally A Kilburn; Anoop J Chauhan Journal: Lung Date: 2011-05-10 Impact factor: 2.584
Authors: George D Bablekos; Antonis Analitis; Stylianos A Michaelides; Konstantinos A Charalabopoulos; Anastasia Tzonou Journal: Ann Transl Med Date: 2016-06