Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, OX3 7DQ, UK.
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QW, UK.
Int Immunopharmacol. 2020 Sep;86:106705. doi: 10.1016/j.intimp.2020.106705. Epub 2020 Jun 16.
Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94-95%) and those not admitted to hospital or in the community (AUC = 80-86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited.
自 2019 年 12 月以来，新型冠状病毒 SARS-CoV-2 已被确定为 COVID-19 大流行的病原体。早期症状与普通感冒和流感等其他常见病症重叠，因此早期筛查和诊断是医疗从业者的关键目标。本研究的目的是使用机器学习（ML）、人工神经网络（ANN）和简单的统计检验，在不了解个体症状或病史的情况下，从全血细胞计数中识别 SARS-CoV-2 阳性患者。分析和训练中使用的数据集包含了来自巴西圣保罗爱因斯坦以色列医院就诊患者的匿名全血细胞计数结果，这些患者在就诊时采集了样本，用于进行 SARS-CoV-2 rt-PCR 检测。医院对患者数据进行了匿名化处理，将临床数据标准化为平均值为零，单位标准差。这些数据是公开的，目的是让研究人员开发出使医院能够快速预测并可能识别 SARS-CoV-2 阳性患者的方法。我们发现，对于普通病房中的患者（AUC=94-95%）和未住院或未在社区中的患者（AUC=80-86%），使用全血细胞计数随机森林、浅层学习和灵活的 ANN 模型，可以高精度地预测 SARS-CoV-2 患者。此外，社区内患者的 4 项血液计数的简单线性组合，AUC 可达 85%。SARS-CoV-2 阳性患者的不同血液参数的归一化数据显示，血小板、白细胞、嗜酸性粒细胞、嗜碱性粒细胞和淋巴细胞减少，单核细胞增加。SARS-CoV-2 阳性患者表现出一种特征性的免疫反应模式，以及在全血细胞计数中测量的不同参数的变化，这些变化可以通过简单快速的血液测试检测到。虽然在感染的早期阶段，症状已知与其他常见病症重叠，但与目前的 SARS-CoV-2 实时聚合酶链反应（rt-PCR）检测相比，全血细胞计数的参数可以更早地分析出病毒类型。这种新方法有可能极大地改善目前基于 PCR 的诊断工具有限的患者的初步筛查。
