Graham Jove, Iverson Andy, Monteiro Joao, Weiner Katherine, Southall Kara, Schiller Katherine, Gupta Mudit, Simard Edgar P
Center for Pharmacy Innovation and Outcomes, Geisinger Clinic, Danville, PA, USA.
Global Clinical Research and Analytics Medtronic, Inc., Minneapolis, MN, USA.
Int J Cardiol Heart Vasc. 2022 Feb 19;39:100974. doi: 10.1016/j.ijcha.2022.100974. eCollection 2022 Apr.
Use of existing data in electronic health records (EHRs) could be used more extensively to better leverage real world data for clinical studies, but only if standard, reliable processes are developed. Numerous computable phenotypes have been validated against manual chart review, and common data models (CDMs) exist to aid implementation of such phenotypes across platforms and sites. Our objective was to measure consistency between data that had previously been manually collected for an implantable cardiac device registry and CDM-based phenotypes for the condition of heart failure (HF).
Patients enrolled in an implantable cardiac device registry at two hospitals from 2013 to 2018 contributed to this analysis wherein registry data were compared to PCORnet CDM-formatted EHR data. Seven different phenotype algorithms were used to search for the presence of HF and compare the results with the registry. Sensitivity, specificity, predictive value and congruence were calculated for each phenotype.
In the registry, 176 of 319 (55%) patients had history of HF, compared with different phenotypes estimating between 96 (30%) and 188 (59%). The least-restrictive phenotypes (any diagnosis) had high sensitivity and specificity (90%/80%), but more restrictive phenotypes had higher specificity (e.g., code present in problem list, 94%). Differences were observed using time-based criteria (e.g., days between visit diagnoses) and between participating hospitals.
Consistency between manually-collected registry data and CDM-based phenotypes for history of HF was high overall, but use of different phenotypes impacted sensitivity and specificity, and results may differ depending on the medical condition of interest.
电子健康记录（EHRs）中现有数据的使用可以更广泛地用于更好地利用真实世界数据进行临床研究，但前提是要开发出标准、可靠的流程。许多可计算表型已经通过人工病历审查得到验证，并且存在通用数据模型（CDMs）来帮助在不同平台和地点实施此类表型。我们的目标是衡量之前为植入式心脏设备注册手动收集的数据与基于CDM的心力衰竭（HF）状况表型之间的一致性。
2013年至2018年在两家医院参加植入式心脏设备注册的患者参与了本分析，其中将注册数据与PCORnet CDM格式的EHR数据进行了比较。使用七种不同的表型算法来搜索HF的存在，并将结果与注册数据进行比较。计算每种表型的敏感性、特异性、预测值和一致性。
在注册数据中，319名患者中有176名（55%）有HF病史，而不同表型估计的比例在96名（30%）至188名（59%）之间。限制最少的表型（任何诊断）具有较高的敏感性和特异性（90%/80%），但限制更多的表型具有更高的特异性（例如，问题列表中存在代码，94%）。使用基于时间的标准（例如，就诊诊断之间的天数）以及参与医院之间观察到了差异。
总体而言，手动收集的注册数据与基于CDM的HF病史表型之间的一致性较高，但使用不同的表型会影响敏感性和特异性，并且结果可能因感兴趣的医疗状况而异。
