ABSTRACT
Introduction
Chronic rhinosinusitis with nasal polyps (CRSwNP) has a high recurrence rate after surgery despite the availability of medical treatments. Multiple clinical and biological factors have been associated with poor post-operative outcomes in patients with CRSwNP. However, these factors and their prognostic values have not yet been extensively summarized.
Areas covered
This systematic review included 49 cohort studies exploring the prognostic factors for post-operative outcomes in CRSwNP. A total of 7802 subjects and 174 factors were included. All investigated factors were classified into three categories according to their predictive value and evidence quality, of which 26 factors were considered plausible for post-operative outcome prediction. Previous nasal surgery, ethmoid-to-maxillary (E/M) ratio, fractional exhaled nitric oxide, tissue eosinophil count or percentage, tissue neutrophil count, tissue IL-5, tissue eosinophil cationic protein, and CLC or IgE in nasal secretion provided more reliable information for prognosis in at least two studies.
Expert opinion
Exploring predictors through noninvasive or minimally invasive methods for specimen collection is recommended for future work. Models combining multiple factors must be established, as no single factor is effective for the whole population.
Article highlights
Chronic rhinosinusitis with nasal polyps (CRSwNP) is a highly heterogeneous disease, and a substantial proportion of patients suffer recurrence after surgery.
Clinical markers with easy access, including previous nasal surgery and ethmoid-to-maxillary (E/M) ratio, have been widely investigated, yet their use in clinical practice remains suboptimal.
Various markers that are more reflective of type 2 inflammation characteristics, such as fractional exhaled nitric oxide, tissue eosinophil count or percentage, tissue IL-5, tissue eosinophil cationic protein, CLC or IgE in the nasal secretion, have been proposed, but definitive data are still lacking.
The development of prognostic combination models integrating key factors could offer a reference for individualized treatments for clinicians.
Declaration of interest
The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/1744666X.2023.2218089.