A Prognostic Model to Predict Mortality among Non–Small-Cell Lung Cancer Patients in the U.S. Military Health System. Issue 12 (December 2015)

• Record Type: Journal Article
• Authors: Lin, Jie; Carter, Corey A.; McGlynn, Katherine A.; Zahm, Shelia H.; Nations, Joel A.; Anderson, William F.; Shriver, Craig D.; Zhu, Kangmin
• Is Part Of: Journal of thoracic oncology. Volume 10:Issue 12(2015)
• Publication Date: 2015-12
• Subjects: Model -- Military health system -- Mortality -- Non–small-cell lung cancer -- Risk prediction; Chest -- Cancer -- Periodicals; Thoracic Neoplasms -- Periodicals; 616.99494005
• DOI: 10.1097/JTO.0000000000000691
• Languages: English
• ISSNs: 1556-0864

Abstract

Abstract : Introduction: Accurate prognosis assessment after non–small-cell lung cancer (NSCLC) diagnosis is an essential step for making effective clinical decisions. This study is aimed to develop a prediction model with routinely available variables to assess prognosis in patients with NSCLC in the U.S. Military Health System. Methods: We used the linked database from the Department of Defense's Central Cancer Registry and the Military Health System Data Repository. The data set was randomly and equally split into a training set to guide model development and a testing set to validate the model prediction. Stepwise Cox regression was used to identify predictors of survival. Model performance was assessed by calculating area under the receiver operating curves and construction of calibration plots. A simple risk scoring system was developed to aid quick risk score calculation and risk estimation for NSCLC clinical management. Results: The study subjects were 5054 patients diagnosed with NSCLC between 1998 and 2007. Age, sex, tobacco use, tumor stage, histology, surgery, chemotherapy, peripheral vascular disease, cerebrovascular disease, and diabetes mellitus were identified as significant predictors of survival. Calibration showed high agreement between predicted and observed event rates. The area under the receiver operating curves reached 0.841, 0.849, 0.848, and 0.838 during 1, 2, 3, and 5 years, respectively. Conclusions: This is the first NSCLC prognosis model for quick risk assessment within the Military Health System. After external validation, the model can be translated into clinical use both as a web-based tool and through mobile applications easily accessible to physicians, patients, and researchers.