Xu Yuan, Li Ning, Lu Mingshan, Dixon Elijah, Myers Robert P, Jolley Rachel J, Quan Hude
Beijing YouAn Hospital, Capital Medical University, Beijing, China.
Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
BMC Gastroenterol. 2017 Jan 7;17(1):5. doi: 10.1186/s12876-016-0559-4.
Risk adjustment is essential for valid comparison of patients' health outcomes or performances of health care providers. Several risk adjustment methods for liver diseases are commonly used but the optimal approach is unknown. This study aimed to compare the common risk adjustment methods for predicting in-hospital mortality in cirrhosis patients using electronic medical record (EMR) data.
The sample was derived from Beijing YouAn hospital between 2010 and 2014. Previously validated EMR extraction methods were applied to define liver disease conditions, Charlson comorbidity index (CCI), Elixhauser comorbidity index (ECI), Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), MELD sodium (MELDNa), and five-variable MELD (5vMELD). The performance of the common risk adjustment models as well as models combining disease severity and comorbidity indexes for predicting in-hospital mortality was compared using c-statistic.
Of 11,121 cirrhotic patients, 69.9% were males and 15.8% age 65 or older. The c-statistics across compared models ranged from 0.785 to 0.887. All models significantly outperformed the baseline model with age, sex, and admission status (c-statistic: 0.628). The c-statistics for the CCI, ECI, MELDNa, and CTP were 0.808, 0.825, 0.849, and 0.851, respectively. The c-statistic was 0.887 for combination of CTP and ECI, and 0.882 for combination of MELDNa score and ECI.
The liver disease severity indexes (i.e., CTP and MELDNa score) outperformed the CCI and ECI for predicting in-hospital mortality among cirrhosis patients using Chinese EMRs. Combining liver disease severity and comorbidities indexes could improve the discrimination power of predicting in-hospital mortality.
风险调整对于有效比较患者的健康结局或医疗服务提供者的表现至关重要。几种用于肝脏疾病的风险调整方法通常被使用，但最佳方法尚不清楚。本研究旨在使用电子病历（EMR）数据比较预测肝硬化患者住院死亡率的常见风险调整方法。
样本来自2010年至2014年期间的北京佑安医院。采用先前验证的EMR提取方法来定义肝脏疾病状况、查尔森合并症指数（CCI）、埃利克斯豪泽合并症指数（ECI）、Child-Turcotte-Pugh（CTP）、终末期肝病模型（MELD）、MELD钠（MELDNa）和五变量MELD（5vMELD）。使用c统计量比较常见风险调整模型以及结合疾病严重程度和合并症指数的模型预测住院死亡率的表现。
在11121例肝硬化患者中，69.9%为男性，15.8%年龄在65岁及以上。比较模型的c统计量范围为0.785至0.887。所有模型均显著优于年龄、性别和入院状态的基线模型（c统计量：0.628）。CCI、ECI、MELDNa和CTP的c统计量分别为0.808、0.825、0.849和0.851。CTP和ECI组合的c统计量为0.887，MELDNa评分和ECI组合的c统计量为0.882。
在使用中国EMR预测肝硬化患者住院死亡率方面，肝脏疾病严重程度指数（即CTP和MELDNa评分）优于CCI和ECI。结合肝脏疾病严重程度和合并症指数可提高预测住院死亡率的辨别能力。
