ArticleThe Annals of otology, rhinology, and laryngology2026
Correlation of Patient-Reported Symptoms With Rhinogram Features Beyond Simple Airway Resistance.
Rhea Darbari Kaul, Raquel Alvarado, Christine Choy, Haiyang Sun, Sidong Liu, Elizabeth Hua, Liam Grouse, Masoud Haghighi, Kate Liang, Emma Zou and 9 more
Abstract read
In one paragraphArticle in The Annals of otology, rhinology, and laryngology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 · Who and what moneyAuthors and funding
19 authors.
Rhea Darbari KaulRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0002-2945-6090 Raquel AlvaradoRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0001-7250-2200 Christine ChoyRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.
Haiyang SunCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Sidong LiuCentre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Elizabeth HuaRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0009-0002-8575-9851 Liam GrouseRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0009-0008-6635-0042 Masoud HaghighiRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0009-0006-5818-6452 Kate LiangRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0009-0005-7210-5615 Emma ZouRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0009-0002-3437-1486 Aari DesaiRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0009-0004-1024-353X Nicholas J CampionRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0003-3345-9486 Cedric ThielRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0002-6557-908X Ghasem AzemiMacquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.ORCID 0000-0002-8675-6858 Raymond SacksRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.
Raewyn G CampbellRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0002-6512-3613 Larry KalishRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.
Antonio Di IevaMacquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.ORCID 0000-0002-5341-5416 Richard J HarveyRhinology and Skull Base Research Group, Applied Medical Research Centre, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0002-6942-8975 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
introductionRhinomanometry, 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.
methodsA 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.
resultsAbout 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).
conclusionThis 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.
Indexed as
Airway ResistanceNasal ObstructionPatient Reported Outcome MeasuresRhinomanometryAdultCross-Sectional StudiesFemaleHumansMachine LearningMaleMiddle Agedairway resistanceimage interpretationmathematical computingpatient reported outcome measuresrhinomanometry
Identifiers
PMID42178145
PMCPMC13617207
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