Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data
Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data
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DOI：
10.1016/j.jval.2018.06.014
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发表时间：
2019-01-01
期刊：
影响因子：
4.5
通讯作者：
Wong, Schiffon L.
中科院分区：
文献类型：
--
作者：
Hoa Van Le;Chi Thi Le Truong;Wong, Schiffon L.
背景资料：复发缓解型多发性硬化症（RRMS）对受影响的患者有重大影响;因此，提高对RRMS的理解非常重要，特别是在现实世界证据的背景下。目的：开发并验证用于在电子健康记录（EHR）中发现的非结构化临床记录和结构化/编码的医疗保健索赔数据中识别RRMS患者的算法。研究方法：查询美国综合交付网络数据（2010-2014年）的研究入选标准（可能的多发性硬化[MS]基础队列）：一个或多个MS诊断代码，患者年龄≥ 18岁，基线病史≥ 1年，无其他脱髓鞘疾病。开发了一组算法来搜索非结构化临床记录的叙述性文本（基于EHR临床记录的算法）和结构化/编码数据（基于声明的算法），以识别RRMS成人患者，排除有进展性MS证据的患者。基于EHR临床记录和基于索赔的算法计算阳性预测值。结果如下：从5308例可能患有MS的患者样本中，仅使用基于EHR临床记录的算法确定了837例RRMS患者，仅使用基于索赔的算法确定了2271例患者;使用两种算法确定了779例患者。基于EHR临床记录算法的阳性预测值为99.1%（95%置信区间[CI]，94.2%-100%），基于索赔算法的阳性预测值为94.6%（95% CI，89.1%-97.8%）至94.9%（95% CI，89.8%-97.9%）。结论：本研究中评价的算法确定了一个真实世界的RRMS患者队列，没有进展性MS的证据，可以在临床研究中自信地进行研究。版权所有（c）2019，ISPOR-卫生经济学和成果研究专业协会。爱思唯尔公司出版这是一个在CC BY-NC-ND许可证下的开放获取文章（http：//creativecommons.org/licenses/by-nc-nd/4.0/）。
Background: Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. Objectives: To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. Methods: US Integrated Delivery Network data (2010-2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes-based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes-based and claims-based algorithms. Results: From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes-based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%-100%) for the EHR clinical notes-based algorithms and 94.6% (95% CI, 89.1%-97.8%) to 94.9% (95% CI, 89.8%-97.9%) for the claims-based algorithms. Conclusions: The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence. Copyright (c) 2019, ISPOR-The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).