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比较PCORnet抗生素与儿童生长研究中PCORnet通用数据模型的处方和配药数据。

Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study.

作者信息

Lin Pi-I D, Daley Matthew F, Boone-Heinonen Janne, Rifas-Shiman Sheryl L, Bailey L Charles, Forrest Christopher B, Horgan Casie E, Sturtevant Jessica L, Toh Sengwee, Young Jessica G, Block Jason P

机构信息

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, US.

Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO, US.

出版信息

EGEMS (Wash DC). 2019 Apr 12;7(1):11. doi: 10.5334/egems.274.

Abstract

Researchers often use prescribing data from electronic health records (EHR) or dispensing data from medication or medical claims to determine medication utilization. However, neither source has complete information on medication use. We compared antibiotic prescribing and dispensing records for 200,395 patients in the National Patient-Centered Clinical Research Network (PCORnet) Antibiotics and Childhood Growth Study. We stratified analyses by delivery system type [closed integrated (cIDS) and non-cIDS]; 90.5 percent and 39.4 percent of prescribing records had matching dispensing records, and 92.7 percent and 64.0 percent of dispensing records had matching prescribing records at cIDS and non-cIDS, respectively. Most of the dispensings without a matching prescription did not have same-day encounters in the EHR, suggesting they were medications given outside the institution providing data, such as those from urgent care or retail clinics. The sensitivity of prescriptions in the EHR, using dispensings as a gold standard, was 99.1 percent and 89.9 percent for cIDS and non-cIDS, respectively. Only 0.7 percent and 6.1 percent of patients at cIDS and non-cIDS, respectively, were classified as false-negative, i.e. entirely unexposed to antibiotics when they in fact had dispensings. These patients were more likely to have a complex chronic condition or asthma. Overall, prescription records worked well to identify exposure to antibiotics. EHR data, such as the data available in PCORnet, is a unique and vital resource for clinical research. Closing data gaps by understanding why prescriptions may not be captured can improve this type of data, making it more robust for observational research.

摘要

研究人员经常使用电子健康记录(EHR)中的处方数据或药物或医疗理赔中的配药数据来确定药物使用情况。然而,这两种来源都没有关于药物使用的完整信息。我们比较了国家以患者为中心的临床研究网络(PCORnet)抗生素与儿童生长研究中200,395名患者的抗生素处方和配药记录。我们按交付系统类型[封闭式整合(cIDS)和非cIDS]进行分层分析;在cIDS和非cIDS中,分别有90.5%和39.4%的处方记录有匹配的配药记录,以及92.7%和64.0%的配药记录有匹配的处方记录。大多数没有匹配处方的配药在EHR中没有当日就诊记录,这表明它们是在提供数据的机构之外开具的药物,例如来自紧急护理或零售诊所的药物。以配药作为金标准,EHR中处方的敏感性在cIDS和非cIDS中分别为99.1%和89.9%。在cIDS和非cIDS中,分别只有0.7%和6.1%的患者被归类为假阴性,即实际上有配药但却被判定为完全未接触过抗生素。这些患者更有可能患有复杂的慢性病或哮喘。总体而言,处方记录在识别抗生素接触情况方面表现良好。EHR数据,如PCORnet中可用的数据,是临床研究中一种独特且重要的资源。通过了解为何处方可能未被记录来弥合数据差距,可以改善这类数据,使其在观察性研究中更具稳健性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/210c/6460498/91774ac8f27a/egems-7-1-274-g1.jpg

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