A laboratory prognostic index model for patients with advanced non-small cell lung cancer.

• Main Authors: Arife Ulas, Fatma Paksoy Turkoz, Kamile Silay, Saadet Tokluoglu, Nilufer Avci, Berna Oksuzoglu, Necati Alkis
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2014-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC4256446?pdf=render

We aimed to establish a laboratory prognostic index (LPI) in advanced non-small cell lung cancer (NSCLC) patients based on hematologic and biochemical parameters and to analyze the predictive value of LPI on NSCLC survival.The study retrospectively reviewed 462 patients with advanced NSCLC diagnosed between 2000 and 2010 in a single institution. We developed an LPI that included serum levels of white blood cells (WBC), lactate dehydrogenase (LDH), albumin, calcium, and alkaline phosphatase (ALP), based on the results of a Cox regression analysis. The patients were classified into 3 LPI groups as follows: LPI 0: normal; LPI 1: one abnormal laboratory finding; and LPI 2: at least 2 abnormal laboratory findings.The median follow up period was 44 months; the median overall survival (OS) and median progression-free survival (PFS) were 11 and 6 months, respectively. A multivariate analysis revealed that the following could be used as independent prognostic factors: an Eastern Cooperative Oncology Group performance status score (ECOG PS) ≥2, a high LDH level, serum albumin <3 g/dL, serum calcium>10.5 g/dL, number of metastases>2, presence of liver metastases, malignant pleural effusion, or receiving chemotherapy ≥4 cycles. The 1-year OS rates according to LPI 0, LPI 1, and LPI 2 were 54%, 34%, and 17% (p<0.001), respectively and 6-month PFS rates were 44%, 27%, and 15% (p<0.001), respectively. The LPI was a significant predictor for OS (Hazard Ratio (HR): 1.41; 1.05-1.88, p<0.001) and PFS (HR: 1.48; 1.14-1.93, p<0.001).An LPI is an inexpensive, easily accessible and independent prognostic index for advanced NSCLC and may be helpful in making individualized treatment plans and predicting survival rates when combined with clinical parameters.