Time to diagnosis of pulmonary hypertension and diagnostic burden: A retrospective analysis of nationwide US healthcare data.
Eva-Maria DiddenEileen LeeJulie WyckmansDeborah QuinnLoïc PerchenetPublished in: Pulmonary circulation (2023)
The main aim of this analysis was to investigate time from symptom onset (chronic unexplained dyspnoea [CUD]) to diagnosis of Group 1 pulmonary hypertension (PH)-pulmonary arterial hypertension (PAH)-and to characterize healthcare resource utilization leading up to diagnosis using a nationwide US claims and an electronic health record (EHR) database from Optum © . Eligible patients were ≥18 years old at first CUD diagnosis (index event) and had a PAH diagnosis on or after index date. Based on administrative codes, PAH was defined as right heart catheterization (RHC), ≥ 2 PAH diagnoses (1 within a year of RHC), and ≥1 post-RHC prescription for PAH treatment. All values are median (1st quartile-3rd quartile) unless otherwise stated. Of 854,722 patients with CUD in the claims database, 582 (0.1%) had PAH. Time from CUD to PAH diagnosis was 2.26 (0.73-4.22) years. PAH patients experienced 3 (2-4) transthoracic echocardiograms (TTEs), 6 (3-12) specialist visits, and 2 (1-4) hospitalizations during the diagnostic interval. Almost one-third of patients (29%) waited 10 months or more to have a TTE. Findings from the EHR database were broadly similar. Resource utilization during the diagnostic interval was also analyzed in an overall PH cohort: findings were generally similar to the PAH cohort (2 [1-3] TTEs, 4 [2-9] specialist visits and 2 [1-4] hospitalizations). These data indicate a delay in the diagnostic pathway for PAH, and illustrate the burden associated with PAH diagnosis.
Keyphrases
- electronic health record
- pulmonary hypertension
- healthcare
- polycyclic aromatic hydrocarbons
- pulmonary arterial hypertension
- end stage renal disease
- ejection fraction
- chronic kidney disease
- palliative care
- prognostic factors
- pulmonary artery
- patient reported outcomes
- health insurance
- cross sectional
- adverse drug
- emergency department
- atrial fibrillation
- coronary artery
- machine learning
- drug induced
- data analysis