Abstract
Background: Uncontrolled chronic rhinosinusitis (CRS) needing
consideration of surgery is a growing health problem yet its risk
factors at individual level are not known. Our aim was to examine risk
factors of revision endoscopic sinus surgery (ESS) at the individual
level by using artificial intelligence.
Methods: Demographic and visit variables were collected from
electronic health records (EHR) of 790 operated CRS patients. The effect
of variables on the prediction accuracy of revision ESS was examined at
the individual level via machine learning models.
Results: Revision ESS was performed to 114 (14.7%) CRS
patients. The logistic regression, gradient boosting and random forest
classifiers had similar performance (AUC values .746, .745 and .747,
respectively) for predicting revision ESS. The best performance was
yielded by using logistic regression and long predictor data retrieval
time (AUC .809, precision 36%, sensitivity 70%) as compared with data
collection time from baseline visit until 0, 3 and 6 months after the
baseline ESS (AUC values .668, .717 and .746, respectively). The number
of visits, number of days from the baseline visit to the baseline ESS,
age, CRS with nasal polyps (CRSwNP), asthma, NERD and immunodeficiency
or its suspicion were associated with revision ESS. Age and the number
of visits before baseline ESS had non-linear effects for the
predictions.
Conclusions: Intelligent data analysis found important
predictors of revision ESS at the individual level, such as visit
frequency, age, Type 2 high diseases and immunodeficiency or its
suspicion.
Keywords: chronic rhinosinusitis, endoscopic sinus surgery,
machine learning, personalized prediction, revision surgery