Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin.
Clin Cardiol. 2013 Aug;36(8):486-91. doi: 10.1002/clc.22146. Epub 2013 Jun 5.
Emerging biomarkers for acute myocardial infarction (AMI) may enhance conventional risk-prediction algorithms if they are informative and associated with risk independently of established predictors. In this study, we constructed a cohort for testing emerging biomarkers for AMI in managed-care populations using existing biospecimen repositories linked to electronic health records (EHR).
Electronic health record-based biorepositories collected by healthcare systems can be federated to provide large, methodologically sound testing sets for biomarker validation.
Subjects ages 40 to 80 years were selected from 2 existing population-based biospecimen repositories. Incident AMI status and covariates were ascertained from the EHR. An ad hoc model for AMI risk was parameterized and validated. Simulation was used to test incremental gains in performance due to the inclusion of biomarkers in this model. Gains in performance were assessed in terms of area under the receiver operating characteristic curve (ROC-AUC) and case reclassification.
A total of 18 329 individuals (57% female) contributed 108 400 person-years of EHR follow-up. The crude AMI incidence was 10.8 and 5.0 per 1000 person-years among males and females, respectively. Compared with the model with risk factors alone, inclusion of a simulated biomarker yielded substantial gains in sensitivity without loss of specificity. Furthermore, a net ROC-AUC gain of 13.3% was observed, as well as correct reclassification of 9.8% of incident cases (79 of 806) that were otherwise not considered statin-indicated at baseline under the National Cholesterol Education Program Adult Treatment Panel III criteria.
More research is needed to assess incremental contribution of emerging biomarkers for AMI prediction in managed-care populations.
急性心肌梗死（AMI）的新兴生物标志物如果具有信息性并且与既定预测因素独立相关，则可以增强传统的风险预测算法。在这项研究中，我们使用与电子健康记录（EHR）相关联的现有生物标本库构建了一个针对管理式医疗人群中新兴 AMI 生物标志物的测试队列。
医疗保健系统收集的基于电子病历的生物库可以联合起来，为生物标志物验证提供大型、方法合理的测试集。
从 2 个现有的基于人群的生物标本库中选择年龄在 40 至 80 岁的受试者。从 EHR 中确定 AMI 发病情况和协变量。参数化和验证 AMI 风险的特定模型。使用模拟来测试由于该模型中包含生物标志物而导致性能的增量收益。根据接收器操作特征曲线下的面积（ROC-AUC）和病例重新分类来评估性能的提高。
共有 18329 人（57%为女性）提供了 108400 人年的 EHR 随访。男性和女性的粗 AMI 发病率分别为每 1000 人年 10.8 和 5.0。与仅具有危险因素的模型相比，包含模拟生物标志物可在不损失特异性的情况下大幅提高敏感性。此外，观察到 ROC-AUC 增益 13.3%，并且根据国家胆固醇教育计划成人治疗专家组 III 标准，在基线时没有被认为是他汀类药物指征的 806 例事件中的 9.8%得到了正确的重新分类。
需要更多的研究来评估新兴 AMI 预测生物标志物在管理式医疗人群中的增量贡献。
