loading page

Using machine learning for personalized prediction of revision paranasal sinus surgery
  • +2
  • Mikko Nuutinen,
  • Jari Haukka,
  • Paula Virkkula,
  • Paulus Torkki,
  • Sanna Toppila-Salmi
Mikko Nuutinen
Helsingin yliopisto Medicum yksikko
Author Profile
Jari Haukka
Helsingin yliopisto Kansanterveystieteen osasto
Author Profile
Paula Virkkula
Helsingin yliopistollinen keskussairaala Paa- ja kaulakeskus
Author Profile
Paulus Torkki
Helsingin yliopisto Kansanterveystieteen osasto
Author Profile
Sanna Toppila-Salmi
Helsingin yliopisto Medicum yksikko
Author Profile

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.