Introduction: Rhinomanometry, a reference measure for the nasal airway, is often considered a research tool with only weak-to-moderate correlations with patient symptoms. However, like lung spirometry curves offer information beyond forced expiratory volume (FEV), rhinomanometry curves (rhinograms) have characteristics beyond simple nasal resistance at 150 Pascals. This study explored the correlation between rhinogram curve features and patient-reported outcomes (PROMs), when compared with nasal airway resistance. Methods: A diagnostic cross-sectional study was conducted on patients from a rhinology clinic. PROMs collected included ordinal nasal obstruction and visual analogue scale (VAS) of the more obstructed side. Rhinomanometry curves underwent mathematical polynomial fitting to extract 835 features. The primary outcome was correlation using Spearman’s rho (ρ) comparing curve-derived features with nasal airway resistance at 150 Pascals. Machine learning was applied to the top 8 correlated features to generate an AI predictive model. Results: About 601 patients (mean age 45 ± 16 years, 45% female) were analysed. Curve-derived features (ρ = 0.305) correlated more than total NAR at 150 Pa (ρ = 0.222) with VAS. Similarly with ordinal nasal obstruction, curve-derived features correlated more (ρ = 0.230) than total NAR at 150 Pa (ρ = 0.112). The best performing AI prediction models achieved correlations of 0.133 (VAS) and 0.117 (nasal obstruction). Conclusion: This study offers a novel method for rhinogram analysis with curve-derived features for correlation and predictive modelling. Whilst correlation scores remain weak-moderate with PROMs, they outperform nasal airway resistance. Therefore, rhinograms produced from rhinomanometry may offer more clinical information than a simplistic numerical resistance testing.
Kaul et al. (Sun,) studied this question.