Oncotarget

Research Papers: Pathology:

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

Ming- Hua Zheng, Sheng-Jie Wu, Ke-Qing Shi, Hua-Dong Yan, Hai Li, Gui-Qi Zhu, Yao-Yao Xie, Fa-Ling Wu and Yong-Ping Chen _

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Oncotarget. 2015; 6:23261-23271. https://doi.org/10.18632/oncotarget.4666

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Abstract

Ming-Hua Zheng1,2, Sheng-Jie Wu3, Ke-Qing Shi1,2, Hua-Dong Yan4, Hai Li5, Gui-Qi Zhu1,6, Yao-Yao Xie7, Fa-Ling Wu1,2, Yong-Ping Chen1,2

1Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China

2Institute of Hepatology, Wenzhou Medical University, Wenzhou 325000, China

3Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China

4Department of Infectious Diseases, Ningbo 315010, China

5Department of Intensive Care Unit, Tianjin Infectious Disease Hospital, Tianjin 300000, China

6School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou 325000, China

7Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China

Correspondence to:

Yong-Ping Chen, e-mail: [email protected]

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 CS4 = 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.


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