Profiling Disease and Economic Burden in CRSwNP Using Machine Learning

Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with high healthcare resource utilization (HRU) and economic cost; however, heterogeneity of clinical burden among patients with differing clinical characteristics has not been fully elucidated. Here, an unsupervised machine learning approach supported by clinical validation identified distinct clusters of patients with CRSwNP and compared healthcare burden.

This retrospective analysis identified adult patients with ≥2 claims for CRSwNP and date of first diagnosis (index date) between January 2015 and June 2019 from a healthcare database. Patients were required to have enrollment in the database 6-months pre- and 12-months post-index. Patients were assigned to clusters using latent class analysis. All-cause and nasal polyp (NP)-related HRU and costs were compared between clusters.

Among 12,807 patients, 5 clusters were identified: cluster 1: no surgery/low comorbidity/low medication use (n = 4076); cluster 2: no surgery/low comorbidity/high medication use (n = 2201); cluster 3: no surgery/high comorbidity/high medication use (n = 2093); cluster 4: surgery/low comorbidity/moderate medication use (n = 3168); cluster 5: surgery/high comorbidity/high medication use (n = 1269). All-cause HRU was similar across clusters. NP-related HRU was highest in the surgical clusters (clusters 4 and 5). All-cause costs were similar in clusters 1-3 ($15,833-$17,461) and highest in clusters 4 ($31,083) and 5 ($31,103), driven by outpatient costs. Total NP-related costs were also highest for clusters 4 and 5 ($14,193 and $16,100, respectively).

Substantial heterogeneity exists in clinical and economic burden among patients with CRSwNP. Machine learning offers a novel approach to better understand the diverse, complex burden of illness in CRSwNP.