Introduction
Chronic rhinosinusitis (CRS) is a symptomatic inflammatory disease of
the nasal and paranasal mucosalasting more than 12
weeks1. It has a prevalence of about 11% and a
remarkable impact on health and costs1. The main
phenotypes are CRS with nasal polyps (CRSwNP) and without
(CRSsNP)1-3. The majority of western CRS cases are
characterized by type 2 -high inflammation with elevated levels of
eosinophils, interleukin-4 (IL-4), IL-5 and IL-131.
Endoscopic sinus surgery (ESS) has shown to be cost-effective
treatment4, if conservative therapy (such as
intranasal corticosteroids and nasal saline irrigation) is
insufficient1. About a sixth of patients respond
unsatisfactory to initial ESS and require revision
ESS1.
Early identification of the risk of CRS recurrence after ESS is
cost-effective5,6. It helps target treatment
correctly39 and prevent permanent tissue
changes1. A substantial number of studies have
identified risk factors of revision ESS7–15. The
studies vary according to sample sizes (n=6615 or
n=610009), variable collection (large retrospective
data base9 or prospective
questionnaires8) or geographic locations (such as
US9, Australia16 or
Finland7). The commonly recognized risk factors
include nasal polyps, asthma, allergy, non-steroidal anti-inflammatory
drug (NSAID), exacerbated respiratory disease (NERD) and previous ESS.
In a meta-analysis13, the strongest predictors of
revision ESS were allergic fungal rhinosinusitis, NERD, asthma, prior
polypectomy. However, no prior research has analysed the prediction
accuracy of revision ESS at the individual level or for variables having
a non-linear association.
The aim of this study was to examine accuracy of personalized prediction
of revision ESS, and to identify most important predictor variables via
modern machine learning algorithms.