利用管理数据集开发并验证一种预测肝硬化终末期肝病模型（MELD）评分的新型工具。
Development and Validation of a Novel Tool to Predict Model for End-Stage Liver Disease (MELD) Scores in Cirrhosis, Using Administrative Datasets.
作者信息
Simon Tracey G, Schneeweiss Sebastian, Wyss Richard, Lu Zhigang, Bessette Lily G, York Cassandra, Lin Kueiyu Joshua
机构信息
Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
出版信息
Clin Epidemiol. 2023 Mar 14;15:349-362. doi: 10.2147/CLEP.S387253. eCollection 2023.
BACKGROUND
The Model for End-Stage Liver Disease (MELD) score predicts disease severity and mortality in cirrhosis. To improve cirrhosis phenotyping in administrative databases lacking laboratory data, we aimed to develop and externally validate claims-based MELD prediction models, using claims data linked to electronic health records (EHR).
METHODS
We included adults with established cirrhosis in two Medicare-linked EHR networks (training and internal validation; 2007-2017), and a Medicaid-linked EHR network (external validation; 2000-2014). Using least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation, we selected among 146 investigator-specified variables to develop models for predicting continuous MELD and relevant MELD categories (MELD<10, MELD≥15 and MELD≥20), with observed MELD calculated from laboratory data. Regression coefficients for each model were applied to the validation sets to predict patient-level MELD and assess model performance.
RESULTS
We identified 4501 patients in the Medicare training set (mean age 75.1 years, 18.5% female, mean MELD=13.0), and 2435 patients in the Medicare validation set (mean age: 74.3 years, 31.7% female, mean MELD=12.3). Our final model for predicting continuous MELD included 112 variables, explaining 58% of observed MELD variability; in the Medicare validation set, the area-under-the-receiver operating characteristic curves (AUC) for MELD<10 and MELD≥15 were 0.84 and 0.90, respectively; the AUC for the model predicting MELD≥20 (using 27 variables) was 0.93. Overall, these models correctly classified 77% of patients with MELD<10 (95% CI=0.75-0.78), 85% of patients with MELD≥15 (95% CI=0.84-0.87), and 87% of patients with MELD≥20 (95% CI=0.86-0.88). Results were consistent in the external validation set (n=2240).
CONCLUSION
Our MELD prediction tools can be used to improve cirrhosis phenotyping in administrative datasets lacking laboratory data.
背景
终末期肝病模型（MELD）评分可预测肝硬化患者的疾病严重程度和死亡率。为了在缺乏实验室数据的管理数据库中改善肝硬化的表型分析，我们旨在开发并外部验证基于索赔的MELD预测模型，使用与电子健康记录（EHR）相关联的索赔数据。
方法
我们纳入了两个与医疗保险相关的EHR网络（训练和内部验证；2007 - 2017年）以及一个与医疗补助相关的EHR网络（外部验证；2000 - 2014年）中确诊为肝硬化的成年人。使用带有5折交叉验证的最小绝对收缩和选择算子（LASSO），我们从146个研究者指定的变量中进行选择，以开发预测连续MELD及相关MELD类别（MELD<10、MELD≥15和MELD≥20）的模型，其中观察到的MELD通过实验室数据计算得出。将每个模型的回归系数应用于验证集，以预测患者水平的MELD并评估模型性能。
结果
我们在医疗保险训练集中识别出4501例患者（平均年龄75.1岁，女性占18.5%，平均MELD = 13.0），在医疗保险验证集中识别出2435例患者（平均年龄：74.3岁，女性占31.7%，平均MELD = 12.3）。我们最终预测连续MELD的模型包含112个变量，解释了观察到的MELD变异性的58%；在医疗保险验证集中，MELD<10和MELD≥15的受试者操作特征曲线下面积（AUC）分别为0.84和0.90；预测MELD≥20（使用27个变量）的模型的AUC为0.93。总体而言，这些模型正确分类了77%的MELD<10患者（95%CI = 0.75 - 0.78），85%的MELD≥15患者（95%CI = 0.84 - 0.87），以及87%的MELD≥20患者（95%CI = 0.86 - 0.88）。外部验证集（n = 2240）的结果与之相符。
结论
我们的MELD预测工具可用于改善缺乏实验室数据的管理数据集中的肝硬化表型分析。
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