Significance of clinical phenomes of patients with COVID-19 infection: A learning from 3795 patients in 80 reports
Significance of clinical phenomes of patients with COVID-19 infection: A learning from 3795 patients in 80 reports
复制标题
COVID-19 感染患者临床现象的意义：80 份报告中 3795 名患者的经验教训
DOI：
10.1002/ctm2.17
复制
发表时间：
2020-04-04
影响因子：
10.6
通讯作者：
Wang, Xiangdong
中科院分区：
文献类型：
--
作者：
Zhang, Linlin;Wang, Diane C.;Wang, Xiangdong
一种新型冠状病毒SARS-CoV-2已在多个国家引起疫情，通过人际传播的病例数量正在迅速增加。SARS-CoV-2感染患者的临床表现对于区分其与其他呼吸道感染至关重要。这些现象的程度和特征取决于感染的严重程度，例如，开始时发烧或轻微咳嗽，进展时出现肺炎体征，恶化时出现严重甚至致命的呼吸困难（急性呼吸窘迫综合征）。我们根据从爆发开始到2020年3月的80份已发表报告总结了3795名COVID-19患者的临床表现，以强调这些表现在诊断和治疗感染中的重要性和特异性，并评估对医疗服务的影响。资料显示，男性患者发病率高于女性，C反应蛋白水平升高，多数患者影像学表现为磨玻璃样阴影。将SARS-CoV-2感染的临床表型与SARS-CoV和MERS-CoV感染的临床表型进行比较。迫切需要开发基于人工智能的机器学习能力，以分析和整合基于放射组学或成像、基于患者、基于临床医生和基于分子测量的数据，以抗击COVID-19的爆发，并在未来对未知感染做出更有效的反应。
A new coronavirus SARS-CoV-2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human-to-human transmission. Clinical phenomes of patients with SARS-CoV-2 infection are critical in distinguishing it from other respiratory infections. The extent and characteristics of those phenomes varied depending on the severities of the infection, for example, beginning with fever or a mild cough, progressed with signs of pneumonia, and worsened with severe or even fatal respiratory difficulty in acute respiratory distress syndrome. We summarized clinical phenomes of 3795 patients with COVID-19 based on 80 published reports from the onset of outbreak to March 2020 to emphasize the importance and specificity of those phenomes in diagnosis and treatment of infection, and evaluate the impact on medical services. The data show that the incidence of male patients was higher than that of females and the level of C-reaction protein was increased as well as most patients' imaging included ground-glass opacity. Clinical phenomes of SARS-CoV-2 infection were compared with those of SARS-CoV and MERS-CoV infections. There is an urgent need to develop an artificial intelligence-based machine learning capacity to analyze and integrate radiomics- or imaging-based, patient-based, clinician-based, and molecular measurements-based data to fight the outbreak of COVID-19 and enable more efficient responses to unknown infections in future.