Predicting Olfactory Loss In Chronic Rhinosinusitis Using Machine Learning

Objective Compare machine learning (ML) based predictive analytics methods compared 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 April 2011 and July 2015. 34% of enrolled patients demonstrated olfactory loss on objective 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 outperformed traditional logistic regression in terms of predictive ability. Top predictors include known factors reported in the literature, as well as several socioeconomic factors. Conclusion Olfactory dysfunction is a variable phenomenon within a large multicenter cohort of refractory CRS patients. ML methods outperform traditional logistic regression in classification of normosmia versus smell loss in CRS, and are able to include numerous risk factors into prediction models. These results carry implications for basic science and clinical research in hyposmia secondary to sinonasal disease, the most common cause of persistent olfactory loss in the general population.