Development and Preliminary Validation of a Medicare Claims-Based Model to Predict Left Ventricular Ejection Fraction Class in Patients With Heart Failure
Development and Preliminary Validation of a Medicare Claims-Based Model to Predict Left Ventricular Ejection Fraction Class in Patients With Heart Failure
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DOI：
10.1161/circoutcomes.118.004700
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发表时间：
2018-12-01
影响因子：
6.9
通讯作者：
Schneeweiss, Sebastian
中科院分区：
文献类型：
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作者：
Desai, Rishi J.;Lin, Kueiyu Joshua;Schneeweiss, Sebastian
背景：射血分数（EF）分级是心力衰竭（HF）治疗反应的重要预测指标；然而，医保理赔数据库缺乏EF信息，限制了其在HF临床和卫生服务研究中的作用。
方法与结果：我们将医疗保险理赔数据与包含EF测量值的电子病历相链接，这些病历来自2个学术医疗中心的11073例HF患者队列。利用来自中心1的数据（“训练样本”）构建了一个基于理赔的EF分级预测模型，并使用来自中心2的数据（“测试样本”）进行验证。采用最小绝对收缩算子和贝叶斯信息准则的线性和逻辑回归模型，从训练样本的总共57个候选变量中选择相关的预测变量。与将患者分为3种EF分级（降低，<0.40；中度降低，0.40 - 0.49；或保留，≥0.50）相比，在测试样本中，将患者分为2种EF分级（降低EF < 0.45与保留EF（≥0.45）的模型准确性更高。在测试样本中，最有效的模型有35个预测因子，能使83%的患者被正确分类（95%置信区间，82% - 84%）。该模型对降低EF和保留EF的阳性预测值分别为0.73（95%置信区间，0.68 - 0.78）和0.84（95%置信区间，0.83 - 0.86），敏感性分别为0.29（95%置信区间，0.25 - 0.32）和0.97（95%置信区间，0.97 - 0.98）。除了HF特异性诊断代码外，其他因素包括年龄、性别、药物使用以及合并症，如心肌梗死和瓣膜疾病，也是EF分级之间的重要鉴别因素。
结论：本研究中开发的基于理赔的模型可用于在无法获得EF测量值的情况下，在评估常规护理中HF患者的健康结局、利用模式和成本的研究中，识别具有特定EF分级的患者亚组。
BACKGROUND: Ejection fraction (EF) class is an important predictor of treatment response in heart failure (HF); however, administrative claims databases lack information on EF, limiting their usefulness in clinical and health services research of HF.METHODS AND RESULTS: We linked Medicare claims data to electronic medical records containing EF measurements for a cohort of 11 073 patients with HF from 2 academic medical centers. A a claims-based model predicting EF class was constructed using data from center 1 ("training sample") and validated using data from center 2 ("testing sample"). Linear and logistic regression models with least absolute square shrinkage operator and Bayesian information criteria were developed to select the relevant predictor variables out of the total 57 candidate variables in the training sample. Higher accuracy was noted in the testing sample with models classifying patients into 2 EF classes (reduced EF < 0.45) versus preserved EF (>= 0.45) when compared with classifying patients into 3 EF classes (reduced, < 0.40, moderately reduced, 0.40-0.49, or preserved, >= 0.50). In the testing sample, the most efficient model had 35 predictors and resulted in 83% of patients being correctly classified (95% CI, 82%-84%). The model had positive predictive value of 0.73 (95% CI, 0.68-0.78) and 0.84 (95% CI, 0.83-0.86) and sensitivity of 0.29 (95% CI, 0.25-0.32) and 0.97 (95% CI, 0.97-0.98) for reduced and preserved EF, respectively. In addition to HF-specific diagnosis codes, other factors including age, sex, medication use, and comorbidities, such as myocardial infarction and valve disorders, were important discriminators between EF classes.CONCLUSIONS: The claims-based model developed in this study may be used to identify patient subgroups with specific EF class in studies evaluating the health outcomes, utilization patterns, and cost, of HF patients in routine care when EF measurements are not available.