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 re...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.展开更多
基金This work was funded by Instituto Costarricense de Investigación y Enseñanza en Nutrición y Salud(INCIENSA,DG-of-2020-174)Vicerrectoría de Investigación-Universidad de Costa Rica,with the Project“C0196 Protocolo bioinformático y de inteligencia artificial para el apoyo de la vigilancia epidemiológica basada en laboratorio del virus SARS-CoV-2 mediante la identificación de patrones genómicos y clínico-demográficos en Costa Rica(2020-2022)”.
文摘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.
