Albumin and Fibrinogen Combined Prognostic Grade Predicts Prognosis of Patients with Prostate Cancer

نویسندگان

  • Yanqing Wang
  • Wei Chen
  • Chuanyi Hu
  • Xiaofei Wen
  • Jiahua Pan
  • Fan Xu
  • Yinjie Zhu
  • Xiaoguang Shao
  • Xun Shangguan
  • Liancheng Fan
  • Jianjun Sha
  • Zezhou Wang
  • Yong Cai
  • Qiang Liu
  • Baijun Dong
  • Wei Xue
چکیده

Background: The nutritional status and systemic inflammation are thought to be associated with outcome in multiple types of cancer. The objective of this study was to determine the prognostic value of pretreatment albumin and fibrinogen combined prognostic grade (AFPG) in prostate cancer (PCa). Methods: 462 prostate cancer patients who had undergone androgen deprivation therapy (ADT) as first-line therapy at four cencters were retrospectively analyzed. The serum albumin levels and plasma fibrinogen levels were measured at the time of diagnosis. The AFPG was calculated according to albumin and fibrinogen levels dichotomized by optimal cut-off values or clinical reference values. Univariate and multivariate cox regression analyses were performed to determine the associations of AFPG with progression-free survival (PFS), cancer-specific survival (CSS) and overall survival (OS). Prognostic accuracy was evaluated with the Harrell concordance index. Results: Multivariate analyses identified AFPG as an independent prognostic indicator for PFS, CSS and OS (each p < 0.01). According to optimal cut-off values, the addition of AFPG to the final models improved predictive accuracy for PFS, CSS and OS compared with the clinicopathological base models, which included Gleason score and incidence of metastasis. Moreover, AFPG according to optimal cut-off values was a better prognostic predictor than albumin levels alone or fibrinogen levels alone or AFPG according to clinical reference values. Conclusion: Decreased AFPG could predict a significantly poor prognosis in patients with PCa. Thus, we recommend adding AFPG according to optimal cut-off values to traditional prognostic model to improve the predictive accuracy.

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عنوان ژورنال:

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2017