Callejon-Leblic María A, Moreno-Luna Ramon, Del Cuvillo Alfonso, Reyes-Tejero Isabel M, Garcia-Villaran Miguel A, Santos-Peña Marta, Maza-Solano Juan M, Martín-Jimenez Daniel I, Palacios-Garcia Jose M, Fernandez-Velez Carlos, Gonzalez-Garcia Jaime, Sanchez-Calvo Juan M, Solanellas-Soler Juan, Sanchez-Gomez Serafin
Rhinology Unit, Department of Otolaryngology, Head and Neck Surgery, Virgen Macarena University Hospital, 41009 Seville, Spain.
Biomedical Engineering Group, University of Seville, 41092 Seville, Spain.
J Clin Med. 2021 Feb 3;10(4):570. doi: 10.3390/jcm10040570.
The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.
新型冠状病毒肺炎（COVID-19）疫情已在全球广泛传播。嗅觉和味觉丧失已成为COVID-19的主要预测指标。我们研究的目的是开发一个全面的机器学习（ML）建模框架，以评估嗅觉和味觉障碍以及其他症状在COVID-19感染中的预测价值。我们进行了一项多中心病例对照研究，其中对COVID-19疑似病例采用实时逆转录聚合酶链反应（RT-PCR）进行检测，并使用视觉模拟量表（VAS）让他们告知自身症状的存在情况和严重程度。将ML算法应用于收集的数据，通过随机将患者分为训练集（75%）和测试集（25%），采用50倍交叉验证方案来预测COVID-19诊断。共纳入777例患者。发现嗅觉和味觉丧失是COVID-19阳性几率较高的症状，比值比分别为6.21和2.42。当使用VAS预测COVID-19诊断时，所应用的ML算法平均准确率达到80%，敏感性为82%，特异性为78%。本研究得出结论，嗅觉和味觉障碍是准确的预测指标，ML算法是COVID-19诊断预测的有用工具。
