Individual exposure of ambient particulate matters and eosinophilic chronic rhinosinusitis with nasal Polyps: Dose-Response, mediation effects and recurrence prediction

Abstract

Purpose: We evaluated the association between ambient particulate matter (PM) exposure and eosinophilic chronic rhinosinusitis with nasal polyps (CRSwNP), and predicted the CRSwNP recurrence risk using machine learning algorithms. Methods: In total, 1,086 patients with CRSwNP were recruited from nine hospitals in China during 2014–2019. The average annual concentrations of ambient PMs before surgery were assessed using satellite-based daily concentrations of PM2.5 and PM10 for a 1 × 1-km2 area. Linear regression and logistic regression models were used to evaluate the associations of PM exposure with eosinophilia and risks of eosinophilic CRSwNPs. In addition, mediation effect analysis was used to validate the interrelationships of the aforementioned factors. Finally, machine learning algorithms were used to predict the recurrence risks of CRSwNPs. Results: There was a significantly increased risk of eosinophilic CRSwNPs with each 10 μg/m3 increase in PMs, with odds ratios (ORs) of 1.039 (95% confidence interval [CI] = 1.007–1.073) for PM10 and 1.058 (95% CI = 1.007– 1.112) for PM2.5. Eosinophils had a significant mediation effect, which accounted for 52% and 35% of the relationships of CRSwNP recurrence with PM10 and PM2.5, respectively. Finally, we developed a naïve Bayesian model to predict the risk of CRSwNP recurrence based on PM exposure, inflammatory data, and patients’ demographic factors. Conclusions: Increased PM exposure is associated with an increased risk of eosinophilic CRSwNP in China. Therefore, patients with eosinophilic CRSwNP should reduce PM exposure to mitigate its harmful impacts.

Keywords

• Chronic rhinosinusitis with nasal polyps
• Particulate matters
• Eosinophils
• Machine learning
• Prediction model