Sprecher Viviane P, Didden Eva-Maria, Swerdel Joel N, Muller Audrey
Actelion Pharmaceuticals Ltd, Allschwil, Switzerland.
Janssen Pharmaceuticals, Raritan, NJ, USA.
Pulm Circ. 2020 Nov 10;10(4):2045894020961713. doi: 10.1177/2045894020961713. eCollection 2020 Oct-Dec.
Large administrative healthcare (including insurance claims) databases are used for various retrospective real-world evidence studies. However, in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, identifying patients retrospectively based on administrative codes remains challenging, as it relies on code combinations (algorithms) and the accuracy for patient identification of most of them is unknown. This study aimed to assess the performance of various algorithms in correctly identifying patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension in administrative databases. A systematic literature review was performed to find publications detailing code-based algorithms used to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients. PheValuator, a diagnostic predictive modelling tool, was applied to three US claims databases, yielding models that estimated the probability of a patient having the disease. These models were used to evaluate the performance characteristics of selected pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension algorithms. With increasing algorithm complexity, average positive predictive value increased (pulmonary arterial hypertension: 13.4-66.0%; chronic thromboembolic pulmonary hypertension: 10.3-75.1%) and average sensitivity decreased (pulmonary arterial hypertension: 61.5-2.7%; chronic thromboembolic pulmonary hypertension: 20.7-0.2%). Specificities and negative predictive values were high (≥97.5%) for all algorithms. Several of the algorithms performed well overall when considering all of these four performance parameters, and all algorithms performed with similar accuracy across the three claims databases studied, even though most were designed for patient identification in a specific database. Therefore, it is the objective of a study that will determine which algorithm may be most suitable; one- or two-component algorithms are most inclusive and three- or four-component algorithms identify most precise pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension populations, respectively.
大型行政医疗保健（包括保险理赔）数据库被用于各种回顾性真实世界证据研究。然而，在肺动脉高压和慢性血栓栓塞性肺动脉高压中，基于行政代码进行回顾性患者识别仍然具有挑战性，因为这依赖于代码组合（算法），并且大多数代码组合用于患者识别的准确性尚不清楚。本研究旨在评估各种算法在行政数据库中正确识别肺动脉高压或慢性血栓栓塞性肺动脉高压患者的性能。进行了一项系统的文献综述，以查找详细描述用于识别肺动脉高压和慢性血栓栓塞性肺动脉高压患者的基于代码的算法的出版物。将诊断预测建模工具PheValuator应用于三个美国理赔数据库，生成估计患者患该病概率的模型。这些模型用于评估所选肺动脉高压和慢性血栓栓塞性肺动脉高压算法的性能特征。随着算法复杂性的增加，平均阳性预测值增加（肺动脉高压：13.4%-66.0%；慢性血栓栓塞性肺动脉高压：10.3%-75.1%），平均敏感性降低（肺动脉高压：61.5%-2.7%；慢性血栓栓塞性肺动脉高压：20.7%-0.2%）。所有算法的特异性和阴性预测值都很高（≥97.5%）。当考虑所有这四个性能参数时，几种算法总体表现良好，并且在所研究的三个理赔数据库中，所有算法的执行准确性相似，尽管大多数算法是为在特定数据库中进行患者识别而设计的。因此，本研究的目的是确定哪种算法可能最合适；单组分或双组分算法包容性最强，三组分或四组分算法分别识别最精确的肺动脉高压或慢性血栓栓塞性肺动脉高压人群。
