Development and validation of a code-based algorithm using in-hospital medical records to identify patients with pulmonary arterial hypertension in a French healthcare database

Abstract

Introduction Pulmonary arterial hypertension (PAH) is a rare and severe disease for which most of the evidence about prognostic factors, evolution and treatment efficacy comes from cohorts, registries and clinical trials. We therefore aimed to develop and validate a new PAH identification algorithm that can be used in the French healthcare database “Système National des Données de Santé (SNDS)”. Methods We developed and validated the algorithm using the Grenoble Alpes University Hospital medical charts. We first identified PAH patients following a previously validated algorithm, using in-hospital ICD-10 (10th revision of the International Statistical Classification of Diseases) codes, right heart catheterisation procedure and PAH-specific treatment dispensing. Then, we refined the latter with the exclusion of chronic thromboembolic pulmonary hypertension procedures and treatment, the main misclassification factor. Second, we validated this algorithm using a gold standard review of in-hospital medical charts and calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy. Finally, we applied this algorithm in the French healthcare database and described the characteristics of the identified patients. Results In the Grenoble University Hospital, we identified 252 unique patients meeting all the algorithm's criteria between 1 January 2010 and 30 June 2022, and reviewed all medical records. The sensitivity, specificity, PPV, NPV and accuracy were 91.0%, 74.3%, 67.9%, 93.3% and 80.6%, respectively. Application of this algorithm to the SNDS yielded the identification of 9931 patients with consistent characteristics compared to PAH registries. Conclusion Overall, we propose a new PAH identification algorithm developed and adapted to the French specificities that can be used in future studies using the French healthcare database.