Development and Validation of a Noninvasive Risk Score Model for Liver Cirrhosis in At-Risk Alcohol Drinkers Without HBV/HCV Infection

Author:

Liu Yin1ORCID,Guo Lan-Wei1ORCID,Xu Hui-Fang1ORCID,Kang Rui-Hua1ORCID,Zheng Li-Yang1ORCID,Zhang Lu-Yao1ORCID,Chen Qiong1ORCID,Sun Xi-Bin1ORCID,Qiao You-Lin12ORCID,Zhang Shao-Kai1ORCID

Affiliation:

1. 1Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.

2. 2Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

Abstract At-risk alcohol consumption is the established most important risk factor for cirrhosis in people without HBV/HCV infection. We aimed to develop and validate a simple and non-invasive tool for triaging cirrhosis risk in at-risk alcohol drinkers without HBV/HCV infection. A large-sample size, cross-sectional study within the framework of a population-based Cancer Screening Program in Urban China (CanSPUC) was conducted. Data on the liver cancer screening in Henan province, China were used. At-risk alcohol drinkers were those who currently drink one or more alcohol units per week for at least six months. A total of 6,581 eligible participants enrolled from October 1, 2013 to December 31, 2016 were included into the derivation dataset, and 2,096 eligible participants enrolled from January 1, 2017 to October 31, 2018 were included into the external validation dataset, respectively. Using the derivation dataset, a 20-point scale risk score model was developed, based on sex, education background, dietary intake of vegetables, dietary intake of roughage, smoking index, length of secondhand smoke exposure, history of fatty liver, history of diabetes, and first-degree family history of liver cancer. The model showed excellent discrimination (AUC  =  0.787; 95% CI, 0.7603–0.812) and calibration (Hosmer–Lemeshow test: P = 0.123) in the derivation dataset and an optimal cut-off value of 12 yield sensitivity of 61.3%, specificity of 82.7%. The model also had achieved similar performance in the external validation dataset. In conclusion, this model can be a practical tool to identify and triage population at high risk of cirrhosis in at-risk alcohol drinkers without HBV/HCV infection. Prevention Relevance: The risk model we developed will not only be used as a practical tool to triage high risk groups for liver cirrhosis, but also have implications for public health measures, such as guidelines for the prevention of liver cancer, in at-risk alcohol drinkers without HBV/HCV infection.

Funder

Science and Technology Department of Henan Province

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology

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