Dobson-Belaire Wendy, Goodfield Jason, Borrelli Richard, Liu Fei Fei, Khan Zeba M
QuintilesIMS, Mississauga, Ontario, Canada.
QuintilesIMS, Mississauga, Ontario, Canada.
Value Health. 2018 Jan;21(1):110-116. doi: 10.1016/j.jval.2017.06.012. Epub 2017 Aug 1.
Using diagnosis code-based algorithms is the primary method of identifying patient cohorts for retrospective studies; nevertheless, many databases lack reliable diagnosis code information.
To develop precise algorithms based on medication claims/prescriber visits (MCs/PVs) to identify psoriasis (PsO) patients and psoriatic patients with arthritic conditions (PsO-AC), a proxy for psoriatic arthritis, in Canadian databases lacking diagnosis codes.
Algorithms were developed using medications with narrow indication profiles in combination with prescriber specialty to define PsO and PsO-AC. For a 3-year study period from July 1, 2009, algorithms were validated using the PharMetrics Plus database, which contains both adjudicated medication claims and diagnosis codes. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the developed algorithms were assessed using diagnosis code as the reference standard. Chosen algorithms were then applied to Canadian drug databases to profile the algorithm-identified PsO and PsO-AC cohorts.
In the selected database, 183,328 patients were identified for validation. The highest PPVs for PsO (85%) and PsO-AC (65%) occurred when a predictive algorithm of two or more MCs/PVs was compared with the reference standard of one or more diagnosis codes. NPV and specificity were high (99%-100%), whereas sensitivity was low (≤30%). Reducing the number of MCs/PVs or increasing diagnosis claims decreased the algorithms' PPVs.
We have developed an MC/PV-based algorithm to identify PsO patients with a high degree of accuracy, but accuracy for PsO-AC requires further investigation. Such methods allow researchers to conduct retrospective studies in databases in which diagnosis codes are absent.
使用基于诊断编码的算法是为回顾性研究识别患者队列的主要方法；然而，许多数据库缺乏可靠的诊断编码信息。
在缺乏诊断编码的加拿大数据库中，开发基于用药申请/开处方就诊（MCs/PVs）的精确算法，以识别银屑病（PsO）患者和患有关节炎的银屑病患者（PsO-AC，银屑病关节炎的替代指标）。
结合用药指征范围较窄的药物和开处方医生的专业领域来开发算法，以定义PsO和PsO-AC。在2009年7月1日起的3年研究期间，使用PharMetrics Plus数据库对算法进行验证，该数据库包含已判定的用药申请和诊断编码。以诊断编码作为参考标准，评估所开发算法的阳性预测值（PPV）、阴性预测值（NPV）、敏感性和特异性。然后将选定的算法应用于加拿大药物数据库，以描述经算法识别的PsO和PsO-AC队列的特征。
在选定的数据库中，共识别出183,328名患者用于验证。当将两个或更多MCs/PVs的预测算法与一个或更多诊断编码的参考标准进行比较时，PsO（85%）和PsO-AC（65%）的PPV最高。NPV和特异性较高（99%-100%），而敏感性较低（≤30%）。减少MCs/PVs的数量或增加诊断申请会降低算法的PPV。
我们开发了一种基于MC/PV的算法，可高度准确地识别PsO患者，但PsO-AC的准确性需要进一步研究。此类方法使研究人员能够在缺乏诊断编码的数据库中进行回顾性研究。
