Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning
Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning
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DOI：
10.1093/clinchem/hvaa200
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发表时间：
2020-11-01
影响因子：
9.3
通讯作者：
Wang, Fei
中科院分区：
文献类型：
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作者：
Yang, He S.;Hou, Yu;Wang, Fei
背景：迫切需要准确的诊断策略来快速识别SARS-CoV-2阳性患者，以便管理患者护理和保护卫生保健人员。主要的诊断试验是通过RT-PCR从鼻咽拭子样本中检测病毒RNA，但并不是所有患者护理地点都能立即获得结果。相比之下，常规实验室检测很容易获得，周转时间(TAT)通常在1-2小时内。方法：我们开发了一个机器学习模型，结合了患者的人口统计特征(年龄、性别、种族)和27项常规实验室检测，以预测个人的SARS-CoV-2感染状态。在SARS-CoV-2 RT-PCR结果公布前2天内获得的实验室检测结果用于对3,356例SARS-CoV-2 RT-PCR检测患者(1,402例阳性和1,954例阴性)进行梯度增强决策树训练。结果：该模型的受试者工作特征曲线下面积为0.854(95%CI：0.829-0.878)。将该模型应用于来自独立医院的独立患者数据集，得到了可比的AUC(0.838)，验证了其使用的普遍性。此外，我们的模型预测了在2天内RT-PCR结果由阴性转为阳性的66%的个体的初始SARS-CoV-2阳性。结论：该模型利用常规的实验室检测结果，为在RT-PCR结果公布之前早期和快速识别高危SARS-CoV-2感染患者提供了机会。在资金或供应受限而无法进行RT-PCR检测的地区，它可能在辅助识别SARS-CoV-2感染患者方面发挥重要作用。
BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.METHOD: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital.RESULTS: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days.CONCLUSION: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.