Development and evaluation of a predictive algorithm for unsatisfactory response among patients with pulmonary arterial hypertension using health insurance claims data

This study aimed to develop and validate a predictive algorithm for unsatisfactory response to initial pulmonary arterial hypertension (PAH) therapy using health insurance claims. Adult patients with PAH initiated on a first PAH therapy (index date) were identified from the Optum’s de-identified Clinformatics® Data Mart Database (1/1/2010-12/31/2019). A random survival forest algorithm was developed using patient-month data and predicted the “survival function” (i.e., risk of not having unsatisfactory response) over time. For each patient-month observation, risk factors were assessed in the 12 months prior. Unsatisfactory response was defined as the first instance of: (1) new PAH therapy, (2) PAH-related hospitalization or emergency room visit, (3) lung transplant or atrial septostomy, (4) PAH-related death, or (5) chronic oxygen therapy initiation. To facilitate use in clinical practice, a simplified risk score was also developed based on a linear combination of the most important risk factors identified in the algorithm. In total, 4,781 patients were included (median age =69.0 years; 58.6% female). Over a median follow-up of 14.0 months, 3,169 (66.3%) had an unsatisfactory response. The most important risk factors included in the algorithm were healthcare resource use (i.e., PAH-related outpatient visits, pulmonologist visits, cardiologist visits, all-cause hospitalizations), time since first PAH diagnosis, time since index date, Charlson Comorbidity Index, dyspnea, and age. Predictive accuracy was good for the full algorithm (C-statistic: 0.732) but was slightly lower for the simplified risk score (C-statistic: 0.668). The present claims-based algorithm performed well in predicting time to unsatisfactory response following initial PAH therapy.