Department of Medical Informatics, Kagawa University Hospital, Kagawa, Japan.
Office of Medical Informatics and Epidemiology, Pharmaceutical and Medical Devices Agency, Tokyo, Japan.
Pharmacoepidemiol Drug Saf. 2022 May;31(5):524-533. doi: 10.1002/pds.5423. Epub 2022 Mar 9.
We aimed to develop a reliable identification algorithm combining diagnostic codes with several treatment factors for inpatients with acute ischemic stroke (AIS) to conduct pharmacoepidemiological studies using the administrative database MID-NET® in Japan.
We validated 11 identification algorithms based on 56 different diagnostic codes (International Classification of Diseases, Tenth Revision; ICD-10) using Diagnosis Procedure Combination (DPC) data combined with information on AIS therapeutic procedures added as "AND" condition or "OR" condition. The target population for this study was 366 randomly selected hospitalized patients with possible cases of AIS, defined as relevant ICD-10 codes and diagnostic imaging and prescription or surgical procedure, in three institutions between April 1, 2015 and March 31, 2017. We determined the positive predictive values (PPVs) of these identification algorithms based on comparisons with a gold standard consisting of chart reviews by experienced specialist physicians. Additionally, the sensitivities of them among 166 patients with the possible cases of AIS at a single institution were evaluated.
The PPVs were 0.618 (95% confidence interval [CI]: 0.566-0.667) to 0.909 (95% CI: 0.708-0.989) and progressively increased with adding or limiting information on AIS therapeutic procedures as "AND" condition in the identification algorithms. The PPVs for identification algorithms based on diagnostic codes I63.x were >0.8. However, the sensitivities progressively decreased to a maximum of ~0.2 after adding information on AIS therapeutic procedures as "AND" condition.
The identification algorithms based on the combination of appropriate ICD-10 diagnostic codes in DPC data and other AIS treatment factors may be useful to studies for AIS at a national level using MID-NET®.
我们旨在开发一种可靠的识别算法，将诊断代码与几个治疗因素结合起来，用于对日本 MID-NET®管理数据库中的急性缺血性脑卒中（AIS）患者进行药物流行病学研究。
我们使用 DPC 数据验证了 11 种基于 56 个不同诊断代码（国际疾病分类，第十次修订版；ICD-10）的识别算法，并结合了添加为“AND”条件或“OR”条件的 AIS 治疗程序的信息。该研究的目标人群是三所机构在 2015 年 4 月 1 日至 2017 年 3 月 31 日期间随机选择的 366 名可能患有 AIS 的住院患者，其定义为相关 ICD-10 代码和诊断成像以及处方或手术程序。我们根据与由经验丰富的专科医生进行图表审查组成的黄金标准的比较，确定了这些识别算法的阳性预测值（PPV）。此外，我们还评估了它们在单一机构的 166 名可能患有 AIS 的患者中的敏感性。
这些识别算法的 PPV 为 0.618（95%置信区间[CI]：0.566-0.667）至 0.909（95%CI：0.708-0.989），并随着将 AIS 治疗程序的信息作为“AND”条件添加到识别算法中而逐渐增加。基于 I63.x 诊断代码的识别算法的 PPV 均大于 0.8。然而，在将 AIS 治疗程序的信息作为“AND”条件添加后，敏感性逐渐降低至最高约 0.2。
基于 DPC 数据中适当的 ICD-10 诊断代码与其他 AIS 治疗因素的组合的识别算法可能对使用 MID-NET®进行全国范围内的 AIS 研究有用。
