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Conditional survival estimate of acute-on-chronic hepatitis B liver failure: A dynamic prediction based on a multicenter cohort

2015; Impact Journals LLC; Volume: 6; Issue: 27 Linguagem: Inglês

10.18632/oncotarget.4666

1949-2553

Ming‐Hua Zheng, Shengjie Wu, Keqing Shi, Huadong Yan, Hai Li, Gui‐Qi Zhu, Yaoyao Xie, Fa-Ling Wu, Yong‐Ping Chen,

Hepatitis B Virus Studies

// Ming-Hua Zheng 1, 2 , Sheng-Jie Wu 3 , Ke-Qing Shi 1, 2 , Hua-Dong Yan 4 , Hai Li 5 , Gui-Qi Zhu 1, 6 , Yao-Yao Xie 7 , Fa-Ling Wu 1, 2 , Yong-Ping Chen 1, 2 1 Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China 2 Institute of Hepatology, Wenzhou Medical University, Wenzhou 325000, China 3 Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China 4 Department of Infectious Diseases, Ningbo 315010, China 5 Department of Intensive Care Unit, Tianjin Infectious Disease Hospital, Tianjin 300000, China 6 School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou 325000, China 7 Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China Correspondence to: Yong-Ping Chen, e-mail: 13505777281@163.com Keywords: acute-on-chronic hepatitis B liver failure, conditional survival, relative survival, prognosis, risk factor Received: May 19, 2015 Accepted: July 02, 2015 Published: July 15, 2015 ABSTRACT Objectives: Counseling patients with acute-on-chronic hepatitis B liver failure (ACHBLF) on their individual risk of short-term mortality is challenging. This study aimed to develop a conditional survival estimate (CSE) for predicting individualized mortality risk in ACHBLF patients. Methods: We performed a large prospective cohort study of 278 ACHBLF patients from December 2010 to December 2013 at three participating medical centers. The Kaplan-Meier method was used to calculate the cumulative overall survival (OS). Cox proportional hazard regression models were used to analyze the risk factors associated with OS. 4-week CSE at "X" week after diagnostic established were calculated as CS 4 = OS (X+4) /OS (X) . Results: The actual OS at 2, 4, 6, 8, 12 weeks were 80.5%, 71.8%, 69.3%, 66.0% and 63.7%, respectively. Using CSE, the probability of surviving an additional 4 weeks, given that the patient had survived for 1, 3, 5, 7, 9 weeks was 74%, 86%, 92%, 93%, 97%, respectively. Patients with worse prognostic feathers, including MELD > 25, Child grade C, age > 45, HE, INR > 2.5, demonstrated the greatest increase in CSE over time, when compared with the "favorable" one (Δ36% vs. Δ10%; Δ28% vs. Δ16%; Δ29% vs. Δ15%; Δ60% vs. Δ12%; Δ33% vs. Δ12%; all P < 0.001; respectively). Conclusions: This easy-to-use CSE can accurately predict the changing probability of survival over time. It may facilitate risk communication between patients and physicians.