Hong Joo Young, Kim Hun-Sung, Choi In Young
Division of Biomedical Informatics, Systems Biomedical Informatics Research Centre, Seoul National University College of Medicine, Seoul, Korea.
Cipherome Inc., Seoul, Korea.
Healthc Inform Res. 2017 Jul;23(3):199-207. doi: 10.4258/hir.2017.23.3.199. Epub 2017 Jul 31.
To enable early detection of adverse drug reactions (ADRs) in patients using HMG-CoA reductase inhibitors (statins), we developed an algorithm that automatically detects liver injury caused by statins from Electronic Medical Record (EMR) data. We verified the performance of our algorithm through manual ADR assessment and a direct chart review.
The subjects in this study were patients who had been prescribed a statin for the first time among outpatients in Seoul St. Mary's Hospital in Korea between January 2009 and December 2012. We extracted basic information about the patients, including laboratory information, underlying disease, diagnosis information, prescription information, and concomitant drugs. We developed an automatic ADR detection algorithm by using EMR data. We validated the results of the algorithm through a chart review.
We developed the algorithm to assess ADR occurrences based on alanine transaminase (ALT) and alkaline phosphatase (ALP) levels. According to the proposed algorithm, any of these result options could be attained: ADR-free, little association, strong association, and weak association or indeterminable. The results of the ADR assessments obtained using the proposed algorithm showed that the data of 126 patients (1.4% of all 9,241 patients) included suspicious figures, thus indicating the possibility of an ADR. In the EMR chart review for verifying the algorithm, ADRs of 33 patients were not associated with statin use; therefore, the ADR occurrence rate was found to be 1.0% (93/9,241). Therefore, the positive predictive value was calculated to be 73.8% (93/126; 95% confidence interval, 69.2%-77.6%). No differences were observed between statin types ( = 0.472).
For early detection of statin-induced liver injury, we developed an automatic ADR assessment algorithm. We expect that algorithms that are more reliable can be developed if we conduct supplement clinical studies with a focus on adverse drug effects.
为了能够早期检测使用HMG-CoA还原酶抑制剂（他汀类药物）患者的药物不良反应（ADR），我们开发了一种算法，该算法可从电子病历（EMR）数据中自动检测由他汀类药物引起的肝损伤。我们通过人工ADR评估和直接病历审查来验证我们算法的性能。
本研究的受试者为2009年1月至2012年12月期间在韩国首尔圣玛丽医院首次接受他汀类药物处方的门诊患者。我们提取了患者的基本信息，包括实验室信息、基础疾病、诊断信息、处方信息和伴随用药。我们利用EMR数据开发了一种自动ADR检测算法。我们通过病历审查来验证算法的结果。
我们开发了基于丙氨酸转氨酶（ALT）和碱性磷酸酶（ALP）水平评估ADR发生情况的算法。根据所提出的算法，可获得以下任何一种结果选项：无ADR、关联不大、强关联、弱关联或无法确定。使用所提出的算法获得的ADR评估结果显示，126名患者（占所有9241名患者的1.4%）的数据包含可疑数据，因此表明存在ADR的可能性。在用于验证算法的EMR病历审查中，33名患者的ADR与他汀类药物的使用无关；因此，发现ADR发生率为1.0%（93/9241）。因此，计算出的阳性预测值为73.8%（93/126；95%置信区间，69.2%-77.6%）。他汀类药物类型之间未观察到差异（P = 0.472）。
为了早期检测他汀类药物引起的肝损伤，我们开发了一种自动ADR评估算法。我们期望，如果我们以药物不良反应为重点进行补充临床研究，能够开发出更可靠的算法。
