Effective Factors for Risk of Death in Patients with Cirrhosis Using Competing Risks Model

Including multivariate survival data can be pointed competing risks data. There are various methods to analyze such data such as semi-parametric methods of cause-specific hazard function, and cumulative incidence function. The aim of this study was to deploy competing risks model to analyze the determinants of patients with liver cirrhosis.
During the one-year study period (June 2008-June 2009), we used the recorded information of patients with cirrhosis who were scheduled for liver transplantation and were followed up for at least 7 years in Imam Khomeini Hospital. In order to determine the effective factors for the time of death and transplantation of the patients, the cumulative incidence function regression method and data analysis were performed using R statistical software. The error level of 0.05 was considered for all tests.
305 patients with cirrhosis including 180 men and 125 women were studied. The mean age SD of the patients was 39.8 14.54 years. At the end of the study, 82 patients died and 40 patients underwent liver transplantation. 7-year survival for the dead patients and for those who received liver transplantation was 18.3 and 21.2 months, respectively.
To investigate the effective factors for the time of death and transplantation of patients with cirrhosis, cumulative incidence function seems desirable. After fitting the multivariate regression model of cumulative incidence function, age, serum albumin, and encephalopathy had a significant effect on the time of the patients’ death. So planning and policy making in prioritizing patients to receive liver transplantation can be made on the basis of their age. Also serum albumin and encephalopathy as two other effective factors can contribute to the prioritization. In this study, among the various causes of cirrhosis, hepatitis B (23%) and cryptogenesis (22.6%), were the most important factors.