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.

中文翻译：

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使用机器学习预测慢性鼻窦炎的嗅觉丧失

目的 将基于机器学习 (ML) 的预测分析方法与传统逻辑回归在慢性鼻窦炎 (CRS-OD) 嗅觉功能障碍分类中进行比较，并在大型多机构难治性 CRS 患者队列中确定预测因子。方法 使用气味识别测试 (SIT) 或简短 SIT (bSIT) 对参加一项前瞻性、多机构、观察性队列研究的成年 CRS 患者进行基线 CRS-OD 评估。将四种不同的 ML 方法与传统逻辑回归进行比较，以对 CRS 正常和 CRS-OD 进行分类。结果 收集了 2011 年 4 月至 2015 年 7 月期间符合纳入标准的 611 名研究参与者的数据。百分之三十四的入组患者在心理物理测试中表现出嗅觉丧失。CRS 正常者和嗅觉丧失者之间的差异包括客观疾病测量（CT 和内窥镜评分）、年龄、性别、既往手术、社会经济状况、类固醇使用、息肉存在、哮喘和阿司匹林敏感性。大多数机器学习方法在预测能力方面表现良好。主要预测因素包括文献中先前报道的因素以及一些社会经济因素。结论 嗅觉功能障碍是 CRS 患者的一种变异现象。与传统的逻辑回归相比，机器学习方法在 CRS 中嗅觉正常与嗅觉丧失的分类方面表现良好，并且能够将众多风险因素纳入预测模型中。几个可操作的特征被确定为 CRS-OD 的危险因素。这些结果表明，机器学习方法可能有助于当前理解和未来研究鼻窦疾病继发的嗅觉减退，鼻窦疾病是普通人群持续性嗅觉丧失的最常见原因。