北京生物醫(yī)學(xué)工程

基于LASSO方法智能診斷新冠肺炎重癥與非重癥

TheIntelligent diagnosis of severe and non-severe COVID-19based on LASSO methodZHONG Xiaoli,RENJinxia, XIAOFeng, XUHaibo

作者：鐘小麗任金霞肖峰徐海波

單位：武漢大學(xué)中南醫(yī)院醫(yī)學(xué)影像科（武漢 430071）

關(guān)鍵詞：新型冠狀病毒肺炎；臨床特征；特征提取；特征建模；鑒別診斷

分類號：R318

出版年·卷·期（頁碼）：2020·39·5（499-505）

摘要：

目的總結(jié)重癥和非重癥新型冠狀病毒肺炎患者的臨床特征，探討基于特征分析智能診斷重癥和非重癥的可行性。方法回顧性分析確診的112例新冠肺炎患者的臨床資料，選取臨床及實驗室檢查特征32個。首先分析所有特征的組間差異性，然后使用LASSO (least absolute shrinkage and selection operator)方法對存在組間差異的特征進行進一步的篩選及分類建模，最后采用ROC曲線、AUC值、靈敏度及特異性等指標對模型在訓(xùn)練集和測試集上進行評價。結(jié)果 32個臨床特征中有23個特征存在組間差異，經(jīng)過LASSO篩選后最終選定9個特征進行建模。模型的AUC（95%CI）、靈敏度、特異性：訓(xùn)練集0.962(0.927, 0.997)、0.912和0.909；測試集0.902(0.789, 1.000)、1.000和0.789。結(jié)論基于LASSO方法篩選的臨床指標特征能夠區(qū)分新冠肺炎非重癥和重癥，可為早期臨床診斷治療提供可靠依據(jù)。

Objective To summarize the clinical characteristics of patients with severe and non-severe COVID-19 ,and to explore the feasibility of intelligent diagnosis of severe and non-severe based on feature analysis. Methods As the clinical characteristics was one of the diagnostic criteria for suspected cases of new coronavirus, this study retrospectively analyzed the clinical data of 112 patients diagnosed with COVID-19 and extracted 32 clinical and laboratory features. First, we analyzed the differences between all the features. Then the least absolute shrinkage and selection operator (LASSO) method was used to further screen and construct a classification model for those features with differences between groups. Finally, we used indexes including the ROC curve, AUC value, sensitivity and specificity to evaluate the model on both the training and test sets. Results Among these 32 features, 23 features existed difference between groups and 9 features were finally selected for modeling after LASSO screening. The AUC (95% CI), sensitivity, and specificity of the model in the training set reached 0.962 (0.927, 0.997), 0.912, and 0.909, and in the test set these values were 0.902 (0.789, 1.000), 1.000, and 0.789. Conclusions The clinical features which are selected based on the LASSO method can distinguish between severe and non-severe COVID-19, which can provide a reliable basis for early clinical diagnosis and treatment.

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