Prediction of Surgical Outcome by Modeling Based on Risk Factors of Morbidity After Pulmonary Resection for Lung Cancer in Older Adults

Background. Surgical treatment for older patients with lung cancer presents more challenges compared with general population. The aim of the study was to predict surgical outcome after pulmonary resection in older patients with lung cancer by developing a clinical model. Methods. Clinical records of 525 patients who were older than 70 years of age and 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. By 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 the bootstrap method and was 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: American Society of Anesthesiologists 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). The c-statistic of the model was 0.75 (95% confidence interval: 0.69 to 0.80), which was similar to the bootstrapping one (0.75; 95% confidence interval: 0.68 to 0.80). The Hosmer-Lemeshow test showed a strong goodness of fit of the model (p = 0.674). In external validation, the performance of our model was superior to that of the two previous models. Conclusions. Our model displayed an acceptable ability to predict surgical outcome in older patients undergoing pulmonary resection for lung cancer. Use of the model can be helpful in decision making about surgical treatment in this special population. (C) 2016 by The Society of Thoracic SurgeonsBeijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding support [ZYLX201509]; Peking University (PKU) 985 Special Funding for Collaborative Research; National High Technology Research and Development Program of China (863 program) [2014AA020602]; PKU Hospitals [2013-5-05]SCI(E)PubMedARTICLEzlyangyue@bjmu.edu.cn3971-97810