DISCo, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.
SCVSA Department, University of Parma, Parco Area delle Science 11/a, 43124, Parman, Italy.
J Med Syst. 2020 Jul 1;44(8):135. doi: 10.1007/s10916-020-01597-4.
The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/ ).
由于 SARS-CoV-2 冠状病毒引起的 COVID-19 大流行，在其爆发后的头 4 个月内，已蔓延至全球 200 多个国家，确诊病例超过 200 万例（可能感染人数更多），死亡近 20 万例。通过（实时）逆转录聚合酶链反应（rRT-PCR）扩增病毒 RNA 是目前确认感染的金标准检测方法，尽管它存在已知的缺点：周转时间长（生成结果需要 3-4 小时）、试剂可能短缺、假阴性率高达 15-20%、需要认证实验室、昂贵的设备和经过培训的人员。因此，需要替代的、更快、更便宜和更易获得的检测方法。我们使用从 279 名因 COVID-19 症状而被收入米兰圣拉斐尔医院急诊室的患者的常规血液检查（即白细胞计数和血小板、CRP、AST、ALT、GGT、ALP、LDH 血浆水平）中的血液化学值，开发了两种机器学习分类模型。这些患者中，有 177 人 rRT-PCR 检测结果为阳性，102 人 rRT-PCR 检测结果为阴性。我们开发了两种机器学习模型，用于区分 SARS-CoV-2 阳性和阴性患者：它们的准确率在 82%至 86%之间，灵敏度在 92%至 95%之间，与金标准相比相当不错。我们还开发了一个可解释的决策树模型，作为临床医生解读血液检测（甚至离线）COVID-19 疑似病例的简单决策辅助工具。这项研究证明了使用血液检测分析和机器学习替代 rRT-PCR 来识别 COVID-19 阳性患者的可行性和临床合理性。这在那些像发展中国家一样缺乏 rRT-PCR 试剂和专门实验室的国家尤其有用。我们提供了一个用于临床参考和评估的基于网络的工具（该工具可在 https://covid19-blood-ml.herokuapp.com/ 获得）。
