Cao Ying, Yao Tianhua, Li Ronghao, Tan Liang, Zhang Zhixiong, Qi Junsheng, Zhang Rui, Wu Yazhou, Chen Zhiqiang, Yin Changlin
Department of Critical Care Medicine, The first affiliated hospital(Southwest Hospital), Army Medical University (Third Military Medical University), Chongqing, 400038, China.
Department of Health Statistics, Faculty of Military Preventive Medicine, Army Medical University (Third Military Medical University), No. 30, Gaotan Yanzheng Street, Shapingba District, Chongqing, 400038, China.
BMC Infect Dis. 2024 Dec 18;24(1):1406. doi: 10.1186/s12879-024-10297-0.
During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identify the factors influencing Omicron TR and to develop machine learning models to predict TR risk. Accurately predicting re-positive patients is crucial for identifying high-risk individuals, optimizing resource allocation, and developing personalized treatment and management plans, thereby effectively controlling the spread of the epidemic, reducing community burden, and ensuring public health.
A retrospective study was conducted among individuals infected with Omicron BA.2 variant from April 12 to May 25, 2022, in the largest Shanghai Fangcang shelter hospital. Five machine learning models were compared, including k-nearest-neighbors (KNN), logistic regression (logistic), bootstrap aggregation (bagging), error back-propagation (BP) neural network, and support vector machines (SVM), to select the best prediction model for the TR risk factors.
A total of 35,488 cases were included in this real-world study. The TR and control groups comprised of 6,171 and 29,317 cases respectively, with a re-positive rate of 17.39%. Higher occurrence of TR was observed in young age, males, those with obvious symptoms, underlying diseases, and a low Ct value. The KNN model proved to be the best in predicting the prognosis in the overall evaluation (accuracy = 0.8198, recall = 0.8026, and AUC = 0.8110 in the test set).
Higher TR risk was found in infected cases who were underage or with underlying diseases; vaccine brand and inoculation status were not significantly associated with TR. KNN was the most effective machine learning model to predict TR occurrence in isolation.
在2022年4月至5月中国上海奥密克戎BA.2变异株疫情暴发期间，PCR核酸检测复阳（TR）情况频繁出现，但TR的危险因素和预测模型仍不明确。本研究旨在确定影响奥密克戎TR的因素，并开发机器学习模型以预测TR风险。准确预测复阳患者对于识别高危个体、优化资源分配以及制定个性化治疗和管理方案至关重要，从而有效控制疫情传播、减轻社区负担并确保公众健康。
对2022年4月12日至5月25日在上海最大的方舱医院感染奥密克戎BA.2变异株的个体进行回顾性研究。比较了五种机器学习模型，包括k近邻（KNN）、逻辑回归（logistic）、装袋法（bagging）、误差反向传播（BP）神经网络和支持向量机（SVM），以选择针对TR危险因素的最佳预测模型。
这项真实世界研究共纳入35488例病例。TR组和对照组分别包含6171例和29317例病例，复阳率为17.39%。在年轻人、男性、有明显症状者、有基础疾病者以及Ct值较低者中观察到更高的TR发生率。在总体评估中，KNN模型在预测预后方面表现最佳（测试集中准确率 = 0.8198，召回率 = 0.8026，AUC = 0.8110）。
在未成年或有基础疾病的感染病例中发现更高的TR风险；疫苗品牌和接种状况与TR无显著关联。KNN是单独预测TR发生的最有效机器学习模型。
