Validation of Cardiovascular End Points Ascertainment Leveraging Multisource Electronic Health Records Harmonized Into a Common Data Model in the ADAPTABLE Randomized Clinical Trial
Validation of Cardiovascular End Points Ascertainment Leveraging Multisource Electronic Health Records Harmonized Into a Common Data Model in the ADAPTABLE Randomized Clinical Trial
复制标题
DOI：
10.1161/circoutcomes.121.008190
复制
发表时间：
2021-12-01
影响因子：
6.9
通讯作者：
Jones, W. Schuyler
中科院分区：
文献类型：
--
作者：
Marquis-Gravel, Guillaume;Hammill, Bradley G.;Jones, W. Schuyler
背景：ADAPTABLE 试验（阿司匹林剂量：以患者为中心的试验评估益处和长期有效性）是在国家以患者为中心的临床研究网络内进行的第一个随机试验，使用格式化为通用数据模型的电子健康记录数据作为终点确定的主要来源，无需标准裁决的确认。这项预先设定的研究的目的是评估从国家以患者为中心的临床研究网络中捕获的非致命终点的有效性，使用传统的盲法裁决作为金标准。 方法：总共 15076 名患有动脉粥样硬化性心血管疾病的参与者被随机分配服用两种剂量的阿司匹林（81 毫克和 325 毫克，每天一次）。使用应用于国家以患者为中心的临床研究网络数据的编程算法捕获非致命终点（非致命性心肌梗死、非致命性中风和需要输血制品的大出血住院）。终点的随机子集由特定疾病的专家评审员独立审查。分别计算列为主要诊断和非主要诊断的终点的编程算法的阳性预测值。 结果：总共确定了 225 个终点（91 个心肌梗死事件、89 个卒中事件和 45 个出血事件），其中 142 个（63%）被列为主要诊断。 14% 的事件缺少完整的源文档。与裁决相比，因心肌梗塞、中风和大出血住院的阳性预测值分别为 90%、72% 和 93%。当仅考虑初级诊断时，阳性预测值分别为 93%、91% 和 97%。当仅考虑非主要诊断时，阳性预测值分别为 82%、36% 和 71%。 结论：与盲法裁决相比，通过查询国家以患者为中心的临床研究网络分发的统一数据来确定临床终点对于确定 ADAPTABLE 中的心肌梗死住院情况是有效的。当分析非主要诊断时，因出血和中风住院的矛盾事件的比例很高，但仅考虑主要诊断时则不然。
BACKGROUND: The ADAPTABLE trial (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) is the first randomized trial conducted within the National Patient-Centered Clinical Research Network to use the electronic health record data formatted into a common data model as the primary source of end point ascertainment, without confirmation by standard adjudication. The objective of this prespecified study is to assess the validity of nonfatal end points captured from the National Patient-Centered Clinical Research Network, using traditional blinded adjudication as the gold standard.METHODS: A total of 15 076 participants with established atherosclerotic cardiovascular disease were randomized to two doses of aspirin (81 mg and 325 mg once daily). Nonfatal end points (hospitalization for nonfatal myocardial infarction, nonfatal stroke, and major bleeding requiring transfusion of blood products) were captured with the use of programming algorithms applied to National Patient-Centered Clinical Research Network data. A random subset of end points was independently reviewed by a disease-specific expert adjudicator. The positive predictive value of the programming algorithms were calculated separately for end points listed as primary and as nonprimary diagnoses.RESULTS: A total of 225 end points were identified (91 myocardial infarction events, 89 stroke events, and 45 bleeding events), including 142 (63%) that were listed as primary diagnoses. Complete source documents were missing for 14% of events. The positive predictive value were 90%, 72%, and 93% for hospitalizations for myocardial infarction, stroke, and major bleeding, respectively, as compared to adjudication. When only primary diagnoses were considered, positive predictive value were 93%, 91%, and 97%, respectively. When only nonprimary diagnoses were considered, positive predictive value were 82%, 36%, and 71%.CONCLUSIONS: As compared with blinded adjudication, clinical end point ascertainment from queries of the National Patient-Centered Clinical Research Network distributed harmonized data was valid to identify hospitalizations for myocardial infarction in ADAPTABLE. The proportion of contradicted events was high for hospitalizations for bleeding and strokes when nonprimary diagnoses were analyzed, but not when only primary diagnoses were considered.