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Profiling Disease and Economic Burden in CRSwNP Using Machine Learning.

Neil Bhattacharyya¹, Jared Silver², Michael Bogart²

¹Mass Eye & Ear and Harvard Medical School, Boston, MA, USA.

Journal of Asthma and Allergy

|October 10, 2022

Summary

Machine learning identified distinct patient groups in chronic rhinosinusitis with nasal polyps (CRSwNP), revealing significant differences in healthcare costs and resource use among these CRSwNP clusters.

Keywords:

asthma chronic rhinosinusitis cost burden healthcare utilization machine learning nasal polyps

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Area of Science:

Otorhinolaryngology
Health Economics
Data Science

Background:

Chronic rhinosinusitis with nasal polyps (CRSwNP) presents a significant healthcare burden.
Existing research has not fully detailed the heterogeneity of clinical and economic impact among CRSwNP patients.

Purpose of the Study:

To identify distinct patient clusters within CRSwNP using unsupervised machine learning.
To compare the healthcare resource utilization (HRU) and economic costs across identified CRSwNP patient clusters.

Main Methods:

Retrospective analysis of adult CRSwNP patients from a healthcare database (January 2015-June 2019).
Latent class analysis employed to define patient clusters based on clinical characteristics.
Comparison of all-cause and nasal polyp (NP)-related HRU and costs between clusters.

Main Results:

Five distinct CRSwNP patient clusters were identified based on surgery, comorbidity, and medication use.
NP-related HRU and total costs were highest in surgical clusters (clusters 4 and 5).
All-cause costs were significantly higher in surgical clusters compared to non-surgical ones.

Conclusions:

Significant heterogeneity exists in the clinical and economic burden of CRSwNP.
Machine learning provides a novel approach to understanding the complex and diverse impact of CRSwNP.