Predicting olfactory loss in chronic rhinosinusitis using machine learning

Abstract

Objective: Compare machine learning (ML)-based predictive analytics methods to traditional logistic regression in classification of olfactory dysfunction in chronic rhinosinusitis (CRS-OD) and identify predictors within a large multi-institutional cohort of refractory CRS patients.

Methods: Adult CRS patients enrolled in a prospective, multi-institutional, observational cohort study were assessed for baseline CRS-OD using a smell identification test (SIT) or brief SIT (bSIT). Four different ML methods were compared to traditional logistic regression for classification of CRS normosmics versus CRS-OD.

Results: Data were collected for 611 study participants who met inclusion criteria between 2011 April and 2015 July. Thirty-four percent of enrolled patients demonstrated olfactory loss on psychophysical testing. Differences between CRS normosmics and those with smell loss included objective disease measures (CT and endoscopy scores), age, sex, prior surgeries, socioeconomic status, steroid use, polyp presence, asthma, and aspirin sensitivity. Most ML methods performed favorably in terms of predictive ability. Top predictors include factors previously reported in the literature, as well as several socioeconomic factors.

Conclusion: Olfactory dysfunction is a variable phenomenon in CRS patients. ML methods perform well compared to traditional logistic regression in classification of normosmia versus smell loss in CRS, and are able to include numerous risk factors into prediction models. Several actionable features were identified as risk factors for CRS-OD. These results suggest that ML methods may be useful for current understanding and future study of hyposmia secondary to sinonasal disease, the most common cause of persistent olfactory loss in the general population.

Trial registration: ClinicalTrials.gov NCT01332136.

Keywords: AI/ML; artificial intelligence; chronic disease; olfaction; outcome assessment (health care); predictive analytics; sinusitis; smell.

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Figures

Fig. 1.

Comparing AUC between methods. A and B) Classification accuracy for the different models. The sample mean and standard deviation of the performance metrics is reported across all 50 cross-validation resampling estimates. C) Statistical comparison of methods. Upper triangle depicts the mean difference in AUC (d) between the model on the y-axis and the model on the x-axis. Lower triangle depicts the Q-value (multiple testing adjusted P-value) for the corrected resampled t-test. Blue values indicated statistically significant differences in mean AUC between the corresponding models on the x-axis and y-axis. SVM-Radial, support vector machine with a radial basis kernel; Log Reg-Step, logistic regression with stepwise variable selection.

Fig. 2.

Variable importance display for most accurate classification model (Support Vector Machine with a radial basis kernel). Note the inclusion of many predictor variables—32 are included with >10% variable importance, suggesting potential interaction between predictors. SVM-Radial, support vector machine with a radial basis kernel.

Fig. 3.

Top 10 Predictors and Variable importance for Random Forest and LASSO models. CF, cystic fibrosis.