Accuracy of Algorithms to Identify Pulmonary Arterial Hypertension in Administrative Data: A Systematic Review

Background: The diagnosis of pulmonary arterial hypertension (PAH) is challenging, and there is significant overlap with the more heterogenous diagnosis of pulmonary hypertension (PH). Clinical and research efforts that rely on administrative data are limited by current coding systems that do not adequately reflect the clinical classification scheme. The aim of this systematic review is to investigate current algorithms to detect PAH using administrative data and to appraise the diagnostic accuracy of these algorithms against a reference standard.

Methods: We conducted comprehensive searches of Medline, Embase, and Web of Science from their inception. We included English-language articles that applied an algorithm to an administrative or electronic health record database to identify PAH in adults.

Results: Of 2,669 unique citations identified, 32 studies met all inclusion criteria. Only four of these studies validated their algorithm against a reference standard. Algorithms varied widely, ranging from single International Classification of Diseases (ICD) codes to combinations of visit, procedure, and pharmacy codes. ICD codes alone performed poorly, with positive predictive values ranging from 3.3% to 66.7%. The addition of PAH-specific therapy and diagnostic procedures to the algorithm improved the diagnostic accuracy.

Conclusions: Algorithms to identify PAH in administrative databases vary widely, and few are validated. The sole use of ICD codes performs poorly, potentially leading to biased results. ICD codes should be revised to better discriminate between PH groups, and universally accepted algorithms need to be developed and validated to capture PAH in administrative data, better informing research and clinical efforts.