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Survival prediction among patients with non-cancer-related end-stage liver disease
Yi-Wen Tsai1, I-Shiang Tzeng2, Yi-Chuan Chen3
1Department of Family Medicine, Chang Gung Memorial Hospital, Keelung, and Chang Gung University College of Medicine, Taoyuan, Taiwan.
Background:
Predicting the survival of non-cancer related end-stage-liver-disease patients in general practice has been difficult for physicians because of the extremely variable trajectories due to multiple complex clinical factors, hence it remains a challenging issue to date. This study aimed to develop and validate a specific prognostic scoring system to early recognize the prognosis and improve the quality of end-of life care for non-cancer end-stage-liver-disease population.
Materials And Methods:
A multicentre, retrospective cohort study was conducted during January 2010 ~ December 2012 and continued follow-up until December 2014. A cox proportional hazard regression analysis was used to derive and validate an optimized model. The main outcome measures were the 28-day, 3-month, 6-month, and 12-month mortality prediction. The performance of the novel model was evaluated, including discrimination and calibration.
Results:
A total of 4,080 consecutive subjects were enrolled. The AUROCs for the 3-month survival discrimination in the MELD, MELD-Na and novel model were 0.787, 0.705 and 0.804 (P<0.001); the 6-month survival discrimination were 0.781, 0.702 and 0.797 (P<0.001); the overall survival discrimination were 0.771, 0.694 and 0.785 (P = 0.002) respectively, whereas the novel model showed a significantly higher discrimination power than did the MELD and MELD-Na for the 3-month, 6-month and overall survival prediction. In addition, calibration of external validation cohort showed no statistical difference in all 5 groups compared with the observed groups.
Conclusion:
This is a clinically relevant, validated scoring system that can be used sequentially to stratify the prognosis in non-cancer cirrhotic populations, which may help the patients along with medical team in decision making to improve the quality of end-of-life care.
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