Molina-Mora Jose Arturo, González Alejandra, Jiménez-Morgan Sergio, Cordero-Laurent Estela, Brenes Hebleen, Soto-Garita Claudio, Sequeira-Soto Jorge, Duarte-Martínez Francisco
Centro de Investigación en Enfermedades Tropicales (CIET) and Facultad de Microbiología, Universidad de Costa Rica, San José, 2060 Costa Rica.
Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud (INCIENSA), Tres Ríos, 30301 Costa Rica.
Phenomics. 2022 Jun 7;2(5):312-322. doi: 10.1007/s43657-022-00058-x. eCollection 2022 Oct.
The clinical manifestations of COVID-19, caused by the SARS-CoV-2, define a large spectrum of symptoms that are mainly dependent on the human host conditions. In Costa Rica, more than 169,000 cases and 2185 deaths were reported during the year 2020, the pre-vaccination period. To describe the clinical presentations at the time of diagnosis of SARS-CoV-2 infection in Costa Rica during the pre-vaccination period, we implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive cases. Profiles were compared based on symptoms, risk factors, viral load, and genomic features of the SARS-CoV-2 sequence. A total of 18 symptoms at time of diagnosis of SARS-CoV-2 infection were reported with a frequency > 1%, and those were used to identify seven clinical profiles with a specific composition of clinical manifestations. In the comparison between clusters, a lower viral load was found for the asymptomatic group, while the risk factors and the SARS-CoV-2 genomic features were distributed among all the clusters. No other distribution patterns were found for age, sex, vital status, and hospitalization. In conclusion, during the pre-vaccination time in Costa Rica, the symptoms at the time of diagnosis of SARS-CoV-2 infection were described in clinical profiles. The host co-morbidities and the SARS-CoV-2 genotypes are not specific of a particular profile, rather they are present in all the groups, including asymptomatic cases. In addition, this information can be used for decision-making by the local healthcare institutions (first point of contact with health professionals, case definition, or infrastructure). In further analyses, these results will be compared against the profiles of cases during the vaccination period.
The online version contains supplementary material available at 10.1007/s43657-022-00058-x.
由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的2019冠状病毒病（COVID-19）的临床表现具有广泛的症状，这些症状主要取决于人类宿主状况。在哥斯达黎加，2020年（疫苗接种前时期）报告了超过16.9万例病例和2185例死亡。为了描述哥斯达黎加疫苗接种前时期SARS-CoV-2感染诊断时的临床表现，我们使用机器学习进行基于症状的聚类，以在18974例阳性病例记录中识别群体水平的聚类或临床特征。根据症状、危险因素、病毒载量和SARS-CoV-2序列的基因组特征对特征进行比较。SARS-CoV-2感染诊断时共报告了18种出现频率>1%的症状，这些症状被用于识别具有特定临床表现组成的7种临床特征。在聚类之间的比较中，无症状组的病毒载量较低，而危险因素和SARS-CoV-2基因组特征分布在所有聚类中。在年龄、性别、生命状态和住院情况方面未发现其他分布模式。总之，在哥斯达黎加疫苗接种前时期，SARS-CoV-2感染诊断时的症状在临床特征中得到了描述。宿主合并症和SARS-CoV-2基因型并非特定于某一特定特征，而是存在于所有组中，包括无症状病例。此外，这些信息可用于当地医疗机构的决策（与卫生专业人员的首次接触点、病例定义或基础设施）。在进一步分析中，将把这些结果与疫苗接种期间病例的特征进行比较。
在线版本包含可在10.1007/s43657-022-00058-x获取的补充材料。
