ABSTRACT
BACKGROUND: In Japan, during the coronavirus disease 2019 (COVID-19) pandemic, patients with non-hypoxia are recommended to recuperate at home or in pre-hospital facilities. However, it was observed that unexpected hypoxia may occur and become severe subsequently in patients whose symptoms were initially expected to improve naturally. The aim of this study is to validate biomarkers that can predict at an early stage the emergence of hypoxia in COVID-19 patients without hypoxia. METHODS: We retrospectively enrolled 193 patients with COVID-19, excluding patients with hypoxia and severe disease from the onset. Participants were classified into two groups according to the emergence of hypoxia during the clinical course, and the laboratory data were compared to identify biomarkers that could predict early the emergence of hypoxia. RESULTS: The areas under the curve for serum cystatin C (CysC) and C-reactive protein (CRP) levels for the emergence of hypoxia during the clinical course were higher than those for other biomarkers (CysC, 0.84 and CRP, 0.83). Multivariate analysis showed that high serum CysC and CRP levels were associated with the emergence of hypoxia during the clinical course. CONCLUSIONS: Elevated serum CysC and CRP levels were associated with the emergence of hypoxia during the clinical course in COVID-19 patients without hypoxia. These findings may help determine the need for hospitalization in initially non-hypoxic COVID-19 patients.
Subject(s)
COVID-19 , Cystatin C , Humans , C-Reactive Protein , Retrospective Studies , Predictive Value of Tests , BiomarkersABSTRACT
BACKGROUND: A characteristic feature of SARS-CoV-2 is its ability to transmit from pre- or asymptomatic patients, complicating the tracing of infection pathways and causing outbreaks. Despite several reports that whole genome sequencing (WGS) and haplotype networks are useful for epidemiologic analysis, little is known about their use in nosocomial infections. AIM: We aimed to demonstrate the advantages of genetic epidemiology in identifying the link in nosocomial infection by comparing single nucleotide variations (SNVs) of isolates from patients associated with an outbreak in Showa University Hospital. METHODS: We used specimens from 32 patients in whom COVID-19 had been diagnosed using clinical reverse transcription-polymerase chain reaction tests. RNA of SARS-CoV-2 from specimens was reverse-transcribed and analysed using WGS. SNVs were extracted and used for lineage determination, phylogenetic tree analysis, and median-joining analysis. FINDINGS: The lineage of SARS-CoV-2 that was associated with outbreak in Showa University Hospital was B.1.1.214, which was consistent with that found in the Kanto metropolitan area during the same period. Consistent with canonical epidemiological observations, haplotype network analysis was successful for the classification of patients. Additionally, phylogenetic tree analysis revealed three independent introductions of the virus into the hospital during the outbreak. Further, median-joining analysis indicated that four patients were directly infected by any of the others in the same cluster. CONCLUSION: Genetic epidemiology with WGS and haplotype networks is useful for tracing transmission and optimizing prevention strategies in nosocomial outbreaks.