Kolling Institute of Medical Research, University of Sydney, St. Leonards, Australia
Joseph Chan , Connie Irene Diakos , David Chan , Anthony J Gill , Nick Pavlakis , Alexander Engel , Stephen John Clarke
Background: Recent evidence suggests that markers of systemic inflammation such as the pre-operative Neutrophil-to-lymphocyte ratio (NLR) and the Lymphocyte-to-monocyte ratio (LMR) are accurate predictors of survival. However, few studies have examined the impact of changes in systemic inflammation post-operatively on patient survival. Our aims were to investigate whether postoperative change in NLR and LMR (NLR/LMR “conversion”) at 21-56 days compared to pre-operative baseline levels confer a survival benefit in colorectal cancer patients undergoing surgical resection. Methods: Data from the Northern Sydney Local Health District from January 1998 to December 2012 were retrospectively collected. Complete data were available for 594 patients. The prognostic role of LMR, NLR and LMR/NLR conversion on overall survival (OS) was assessed using Cox regression models. Results: Pre and post-operatively, both the NLR and LMR were independently associated with OS. Patients who had high pre-operative LMR and converted post-operatively to low LMR had significantly poorer survival, median OS 142.35 vs 63.64 months, HR 0.411 (95% CI 0.236-0.716, p = 0.002). Those with low pre-operative LMR who converted to high LMR had significantly better survival than those who did not convert, median OS 64.74 vs 38.16 months, HR 1.921 (95% CI 1.329-2.775, p < 0.001). For the NLR, conversion from low NLR to high NLR resulted in significantly poorer outcomes, median OS 51.764 vs 115.696 months, HR 0.434 (95% CI 0.290-0.650, p < 0.001). Conversion from high NLR to low NLR conferred marginal survival benefit, median OS 51.39 vs 47.15 months, HR 1.508 (95% CI 1.054-2.157, p = 0.024). Conclusions: LMR andNLR conversion may be early indicators of prognosis in resected colorectal cancer, further stratifying patients for selection of adjuvant therapy and potentially optimizing use of health care resources.
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