Author of

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  P1.08 - Poster Session with Presenters Present (ID 460)

  • Event: WCLC 2016
  • Type: Poster Presenters Present
  • Track: Surgery
  • Presentations: 1
  • Moderators:
  • Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)

  • 16939

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    P1.08-004 - Prediction of Surgical Outcome by Modeling Based on Risk Factors of Morbidity Following Pulmonary Resection for Lung Cancer in the Elderly (ID 5394)

    14:30 - 14:30  |  Author(s): N. Wu
    • Abstract
    • Slides

    Background:
    Surgical treatment for elderly patients with lung cancer presents more challenges compared with general population. The aim of the study was to predict surgical outcome following pulmonary resection in the elderly with lung cancer by developing a clinical model.
    
    Methods:
    Clinical records of 525 patients aged over 70 years who underwent pulmonary resection for lung cancer in a single center were reviewed. Patients were divided into three ordered categories of surgical outcome according to the Clavien–Dindo classification. Using a development cohort of 401 patients, an ordinal logistic regression was performed to develop a prediction model for surgical outcome. The model was internally validated by bootstrap method and externally validated by another cohort of 124 patients. Two previous models were tested as benchmarks of our model.
    
    Results:
    The model was developed based on five risk factors of morbidity: ASA classification (p<0.001), pulmonary disease (p=0.001), tumor size (p=0.011), tumor location (p=0.015) and surgical approach (p=0.036). C-statistics of the model was similar to bootstrapping one. Hosmer-Lemeshow test showed a good goodness-of-fit. In external validation the performance of our model was superior to the two previous models. Figure 1

    Prediction Performance of the Present and Previous Models

    Models	c-statistic (95%CI)	Hosmer-Lemeshow test
    The present model	0.75 (0.69-0.80)	0.674
    After bootstrapping	0.75 (0.68-0.80)	0.671
    External validation	0.70 (0.64-0.75)	0.382
    Kates M, et al (2009)	0.63 (0.57-0.69)	0.115
    Poullis M, et al (2013)	0.61 (0.54-0.67)	0.091

    
    
    
    
    Conclusion:
    Our model displayed an acceptable ability to predict surgical outcome in elderly patients undergoing pulmonary resection for lung cancer.
    
    

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