Manrodt Christopher, Curtis Anne B, Soderlund Dana, Fonarow Gregg C
Medtronic, plc, Mounds View, MN, United States.
Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, NY, United States.
Int J Med Inform. 2020 Jun;138:104138. doi: 10.1016/j.ijmedinf.2020.104138. Epub 2020 Apr 7.
Implantable cardioverter-defibrillators (ICDs) have been shown to reduce sudden cardiac death in appropriately selected patients, but they remain underutilized among indicated patients.
To develop a new approach to identifying guideline indications among patients implanted with ICDs by creating algorithms that extract data from electronic health records (EHR).
Published guidelines providing recommendations for ICD use were distilled into categories of diagnoses, measures, procedures, and terminologies. Criteria for each indication category were translated into clinical algorithms using administrative codes, search terms, and other required data. Cardiologists with guideline-development expertise reviewed these algorithms. After developing applications using a subset of data, phenotypes were evaluated against a curated Optum® de-identified EHR dataset, including 94,441 patients with ≥1 procedure codes for ICD implantation or follow-ups from 47 US provider networks.
Guideline-concordant indications were identified in 83.7 % of 49,560 patients with new ICD implants. The percentage of ICD patients with guideline-concordant indications ranged from 69.4%-88.1% for patients whose initial EHR records were 0-6 days to >365 days prior to implant, respectively. Many guideline criteria used data which could only be derived from unstructured provider notes and required significant algorithm development.
Defibrillator implant indications were detected in >80 % of patients receiving ICDs using rule-based algorithms in a curated EHR dataset. Computable phenotypes may enable researchers to analyze EHRs in more reproducible ways, by identifying guideline indications in patients with specific therapies such as ICDs, and, by extension, identifying patients who meet indications yet do not yet have indicated therapies.
植入式心脏复律除颤器（ICD）已被证明可降低适当选择患者的心源性猝死风险，但在符合指征的患者中其使用率仍然较低。
通过创建从电子健康记录（EHR）中提取数据的算法，开发一种新方法来识别植入ICD患者中的指南指征。
将已发表的关于ICD使用建议的指南提炼为诊断、测量、程序和术语类别。使用管理代码、搜索词和其他所需数据将每个指征类别的标准转化为临床算法。具有指南制定专业知识的心脏病专家对这些算法进行了审查。在使用一部分数据开发应用程序后，根据经过整理的Optum®去识别EHR数据集对表型进行评估，该数据集包括来自47个美国医疗服务网络的94441例有≥1个ICD植入或随访程序代码的患者。
在49560例新植入ICD的患者中，83.7%的患者被识别出符合指南的指征。对于初始EHR记录在植入前0 - 6天至>365天的患者，符合指南指征的ICD患者百分比分别为69.4% - 88.1%。许多指南标准使用的数据只能从未结构化的医疗服务提供者记录中获取，并且需要大量的算法开发。
在经过整理的EHR数据集中，使用基于规则的算法在超过80%接受ICD治疗的患者中检测到了除颤器植入指征。可计算表型可能使研究人员能够以更可重复的方式分析EHR，通过识别接受特定治疗（如ICD）患者中的指南指征，并进而识别符合指征但尚未接受指定治疗的患者。
