Modeling the overall survival of patients with advanced‐stage non‐small cell lung cancer using data of routine laboratory tests. Issue 2 (5th June 2014) - Zhang, Kejin; Lai, Yinzhi; Axelrod, Rita; Campling, Barbara; Hyslop, Terry; Civan, Jesse; Solomides, Charalambos; Myers, Ronald E.; Lu, Bo; Bar Ad, Voichita; Li, Bingshan; Ye, Zhong; et al.
<x xml:space="preserve">Abstract</x> Cancer patients undergo routine clinical monitoring with an array of blood tests that may carry long‐term prognostic information. We aimed to develop a new prognostic model predicting survival for patients with advanced non‐small cell lung cancer (NSCLC), based on laboratory tests commonly performed in clinical practice. A cohort of 1, 161 stage IIIB or IV NSCLC patients was divided into training (n = 773) and testing (n = 388) cohorts. We analyzed the associations of 32 commonly tested laboratory variables with patient survival in the training cohort. We developed a model based on those significant laboratory variables, together with important clinical variables. The model was then evaluated in the testing cohort. Five variables, including albumin, total protein, alkaline phosphatase, blood urea nitrogen and international normalized ratio, were significantly associated with patient survival after stepwise selection. A model incorporating these variables classified patients into low‐, medium‐ and high‐risk groups with median survival of 16.9, 7.2 and 2.1 months, respectively (p < 0.0001). Compared with low‐risk group, patients in the medium‐ and high‐risk groups had a significantly higher risk of death at 1 year, with hazard ratio (HR) of 1.95 (95% CI 1.62–2.36) and 5.22 (4.30–6.34), respectively. These results were validated in the testing cohort. Overall, we developed a prognostic model relying entirely on readily available variables, with similar predictive power to those which depend on more specialized and expensive molecular assays. Further study is necessary to validate and further refine this model, and compare its performance to models based on more specialized and expensive testing.