Publication date: February 2018
Source:Cancer Epidemiology, Volume 52
Author(s): Feng Peng, Dan Hu, Xiandong Lin, Gang Chen, Binying Liang, Ying Chen, Chao Li, Hejun Zhang, Yan Xia, Jinxiu Lin, Xiongwei Zheng, Wenquan Niu
BackgroundDyslipidaemia is key to colorectal carcinogenesis, and the prediction of baseline triglycerides (TG), total cholesterol (TC), high- and low-density lipoprotein cholesterol (HDLC and LDLC) for postsurgical colorectal cancer mortality has not been researched.ObjectivesWe attempted to re-analyse the FIESTA database to assess the prognostic value of three informative lipid derivatives − AI (atherogenic index: (TC − HDLC)/HDLC), THR (TG/HDLC) and LHR (LDLC/HDLC) in predicting colorectal cancer mortality.MethodsBased on the FIESTA database, 1318 patients received radical resection from 2000 to 2008, with the latest follow-up completed in December 2015. Median follow-up time was 58.6 months.ResultsTotal 1318 patients were randomly evenly divided into the derivation and validation groups. Overall, baseline AI and LHR were associated with the significantly increased risk of colorectal cancer mortality in both derivation (hazard ratio (HR): 1.41 and 1.35, respectively) and validation (HR: 1.37 and 1.32, respectively) groups (all P < 0.001). The predictive performance of AI and LHR was remarkably enhanced in patients with female gender, former/current smoking, colon cancer, early stage, positive vein tumor embolus, normal weight, preoperative hypertension or diabetes comorbidities. Calibration/discrimination analyses revealed that adding AI or LHR to the traditional model had a better fit in both groups. A prognostic nomogram was finally constructed with good predictive accuracy and discriminative capability (C-index = 0.814, P < 0.001).ConclusionWe consolidated the prognostic superiority of AI and LHR in predicting colorectal cancer mortality over TNM stage.
http://ift.tt/2m8Yfld
Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5 Άγιος Νικόλαος
Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com
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