A nomogram based on serum bilirubin and albumin levels predicts survival in gastric cancer patients
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Huiling Sun1,*, Bangshun He1,*, Zhenlin Nie2,*, Yuqin Pan1, Kang Lin1, Hongxin Peng3, Tao Xu1, Xiaoxiang Chen3, Xiuxiu Hu3, Zijuan Wu4, Di Wu5 and Shukui Wang1
1General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China
2Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, China
3School of Medicine, Southeast University, Nanjing, 210009, China
4First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
5School of Medicine, Yangzhou Occupational University, Yangzhou, 225009, China
*These authors have contributed equally to this work
Shukui Wang, email: [email protected]
Keywords: gastric cancer, bilirubin, albumin, prognosis, nomogram
Received: December 06, 2016 Accepted: March 21, 2017 Published: April 18, 2017
Decreases in serum bilirubin and albumin levels are associated with poorer prognoses in some types of cancer. Here, we examined the predictive value of serum bilirubin and albumin levels in 778 gastric cancer patients from a single hospital in China who were divided among prospective training and retrospective validation cohorts. X-tile software was used to identify optimal cutoff values for separating training cohort patients into higher and lower overall survival (OS) groups, based on total bilirubin (TBIL) and albumin levels. In univariate analysis, tumor grade and TNM stage were associated with OS. After adjusting for tumor grade and TNM stage, TBIL and albumin levels were still clearly associated with OS. These results were confirmed in the 299 patients in the validation cohort. A nomogram based on TBIL and albumin levels was more accurate than the TNM staging system for predicting prognosis in both cohorts. These results suggest that serum TBIL and albumin levels are independent predictors of OS in gastric cancer patients, and that an index that combines TBIL and albumin levels with the TNM staging system might have more predictive value than any of these measures alone.
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