• Kuang-Cheng Chan, Jia-Rong Yeh, and Wei-Zen Sun.
• Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan.
• Liver Int. 2017 Aug 1; 37 (8): 1239-1248.

Background & AimsModel for end-stage liver disease (MELD) score has been extensively used to prioritize patients for liver transplantation and determine their prognosis, but with limited predictive value. Autonomic dysfunction may correlate with increased mortality after liver transplant. In this study, two autonomic biomarkers, complexity and deceleration capacity, were added to the predicting model for 1-year mortality after liver transplantation.MethodsIn all, 30 patients with end-stage liver diseases awaiting liver transplantation were included. Complexity and deceleration capacity were calculated by multi-scale entropy and phase-rectified signal averaging, respectively. Different combinations of autonomic factors and MELD score were used to predict mortality rate of liver transplant after 1-year follow-up. Receiver-operating characteristics curve analysis was performed to determine clinical predictability. Area under the receiver-operating characteristics curve represents the overall accuracy.ResultsThe 1-year mortality rate was 16.7% (5/30). The overall accuracy of MELD score used for predicting mortality after liver transplantation was 0.752. By adding complexity and deceleration capacity into the predicting model, the accuracy increased to 0.912. Notably, the accuracy of the prediction using complexity and deceleration capacity alone was 0.912.ConclusionComplexity and deceleration capacity, which represent different dynamical properties of a human autonomic system, are critical factors for predicting mortality rate of liver transplantation. We recommend that these pre-operative autonomic factors may be helpful as critical adjuncts to predicting model of mortality rate in prioritizing organ allocation.© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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