Pattanaprateep Oraluck, McEvoy Mark, Attia John, Thakkinstian Ammarin
Section for Clinical Epidemiology and Biostatistics, The Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi, Bangkok, 10400, Thailand.
Centre for Clinical Epidemiology and Biostatistics, and Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.
BMC Med Inform Decis Mak. 2017 Jul 4;17(1):96. doi: 10.1186/s12911-017-0496-3.
Nonsteroidal anti-inflammatory drugs (NSAIDs) and gastro-protective agents should be co-prescribed following a standard clinical practice guideline; however, adherence to this guideline in routine practice is unknown. This study applied an association rule model (ARM) to estimate rational NSAIDs and gastro-protective agents use in an outpatient prescriptions dataset.
A database of hospital outpatients from October 1st, 2013 to September 30th, 2015 was searched for any of following drugs: oral antacids (A02A), peptic ulcer and gastro-oesophageal reflux disease drugs (GORD, A02B), and anti-inflammatory and anti-rheumatic products, non-steroids or NSAIDs (M01A). Data including patient demographics, diagnoses, and drug utilization were also retrieved. An association rule model was used to analyze co-prescription of the same drug class (i.e., prescriptions within A02A-A02B, M01A) and between drug classes (A02A-A02B & M01A) using the Apriori algorithm in R. The lift value, was calculated by a ratio of confidence to expected confidence, which gave information about the association between drugs in the prescription.
We identified a total of 404,273 patients with 2,575,331 outpatient visits in 2 fiscal years. Mean age was 48 years and 34% were male. Among A02A, A02B and M01A drug classes, 12 rules of associations were discovered with support and confidence thresholds of 1% and 50%. The highest lift was between Omeprazole and Ranitidine (340 visits); about one-third of these visits (118) were prescriptions to non-GORD patients, contrary to guidelines. Another finding was the concomitant use of COX-2 inhibitors (Etoricoxib or Celecoxib) and PPIs. 35.6% of these were for patients aged less than 60 years with no GI complication and no Aspirin, inconsistent with guidelines.
Around one-third of occasions where these medications were co-prescribed were inconsistent with guidelines. With the rapid growth of health datasets, data mining methods may help assess quality of care and concordance with guidelines and best evidence.
按照标准临床实践指南，非甾体抗炎药（NSAIDs）和胃保护剂应联合处方；然而，在常规实践中对该指南的遵循情况尚不清楚。本研究应用关联规则模型（ARM）来评估门诊处方数据集中NSAIDs和胃保护剂的合理使用情况。
检索2013年10月1日至2015年9月30日期间医院门诊患者数据库，查找以下任何一种药物：口服抗酸剂（A02A）、消化性溃疡和胃食管反流病药物（GORD，A02B）以及抗炎和抗风湿产品、非甾体或NSAIDs（M01A）。还检索了包括患者人口统计学、诊断和药物使用情况的数据。使用关联规则模型，通过R语言中的Apriori算法分析同一药物类别（即A02A - A02B、M01A内的处方）以及不同药物类别（A02A - A02B和M01A）之间的联合处方情况。提升值通过置信度与预期置信度的比值计算得出，它给出了处方中药物之间关联的信息。
在两个财政年度中，我们共识别出404,273名患者，门诊就诊次数达2,575,331次。平均年龄为48岁，男性占34%。在A02A、A02B和M01A药物类别中，发现了12条关联规则，支持度和置信度阈值分别为1%和50%。提升值最高的是奥美拉唑和雷尼替丁之间（340次就诊）；其中约三分之一的就诊（118次）是给非GORD患者开具的处方，这与指南相悖。另一个发现是COX - 2抑制剂（依托考昔或塞来昔布）与质子泵抑制剂的联合使用。其中35.6%是给年龄小于60岁、无胃肠道并发症且未使用阿司匹林的患者，这与指南不符。
这些药物联合处方的情况中约有三分之一与指南不一致。随着健康数据集的快速增长，数据挖掘方法可能有助于评估医疗质量以及与指南和最佳证据的一致性。
