Progress in research on predictors of adverse outcomes in patients with nasal inflammatory diseases

TL;DR

This paper reviews progress in identifying early predictors of poor outcomes in nasal inflammatory diseases to improve diagnosis and treatment.

Contribution

The paper summarizes recent advances in using inflammatory markers, microbiota, and clinical factors to predict adverse outcomes in nasal inflammatory diseases.

Findings

Inflammatory cytokines and cell markers are key predictors of poor outcomes in nasal inflammatory diseases.

Nasal microbiota and metabolites are emerging as important indicators for predicting disease recurrence and treatment response.

Targeted interventions like monoclonal antibodies and microbial modulation are being explored based on these predictors.

Abstract

Nasal inflammatory disease has a complex pathogenesis, high incidence and long disease course. Complete resolution is often challenging, and these diseases are closely related to upper and lower respiratory tract diseases. For common nasal inflammatory diseases, such as chronic rhinosinusitis (CRS), allergic rhinitis (AR), and fungal rhinosinusitis (FRS), adverse outcomes, such as repeated inflammation, AR combined with asthma, and postoperative recurrence, often occur despite standardized treatments, causing great distress to patients and increasing societal costs due to the need for long-term and repeated treatments. Therefore, the identification of early predictors of unfavorable outcomes of nasal inflammatory diseases is important for achieving early diagnosis, intervention and treatment of nasal inflammatory diseases. This paper summarizes the progress in research on the role of indicators, such as inflammatory cytokines, inflammatory cells, metabolites, nasal flora, and clinical parameters, in predicting poor outcomes in patients with nasal inflammatory diseases.

Schematic of predictors of adverse outcomes and targeted interventions in nasal inflammatory diseases. This graphical abstract outlines key poor outcomes (e.g., refractory CRS, AR with asthma comorbidity, postoperative recurrence, olfactory dysfunction, accelerated progression in lFRs), core predictive indicators (inflammatory factors, inflammatory cell markers, metabolites, nasal microbiota and clinical factors), and corresponding targeted interventions (e.g., monoclonal antibody therapy, nasal microbial modulation) in nasal inflammatory diseases. EOS, eosinophil; AI, artificial intelligence; CRS, chronic rhinosinusitis; AR, allergic rhinitis; IFRS, invasive fungal rhinosinusitis.Diagram illustrating nasal inflammatory diseases, highlighting factors like inflammatory cytokines, cell markers, microorganisms, metabolites, and clinically relevant factors. Treatments include monoclonal antibody therapy, nasal microbial environment improvements, and various therapies like hormones, antihistamines, immunotherapy, lipid metabolites, and traditional Chinese medicines.

Schematic of predictors of adverse outcomes and targeted interventions in nasal inflammatory diseases. This graphical abstract outlines key poor outcomes (e.g., refractory CRS, AR with asthma comorbidity, postoperative recurrence, olfactory dysfunction, accelerated progression in lFRs), core predictive indicators (inflammatory factors, inflammatory cell markers, metabolites, nasal microbiota and clinical factors), and corresponding targeted interventions (e.g., monoclonal antibody therapy, nasal microbial modulation) in nasal inflammatory diseases. EOS, eosinophil; AI, artificial intelligence; CRS, chronic rhinosinusitis; AR, allergic rhinitis; IFRS, invasive fungal rhinosinusitis.

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