Association between cycle threshold (Ct ) values and clinical and laboratory data in inpatients with COVID-19 and asymptomatic health workers.
Juan Pablo Ramirez-HinojosaYunuen Rodriguez-SanchezAngel Kaleb Romero-GonzalezMarisol Chavez-GutierrezNelly Raquel Gonzalez-ArenasAurora Ibarra-ArceSara Arroyo-EscalanteBeatriz Zavaleta-VillaMoises Leon-JuarezVictor Javier Cruz-HolguinLuz Elena Espinosa de Los Monteros-PerezAngelica Olivo-DiazRigoberto Hernandez-CastroLourdes Suarez-RoaHector Prado-CallerosOctavio Sierra-MartinezGuillermina Avila-RamirezAna FlisserPablo MaravillaMirza Romero-ValdovinosPublished in: Journal of medical virology (2021)
In-house assays for the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse-transcription polymerase chain reaction (qRT-PCR), are feasible alternatives, particularly in developing countries. Cycle threshold (Ct ) values obtained by qRT-PCR were compared with clinical and laboratory data from saliva of inpatients with COVID-19 and asymptomatic health workers (AHW) were studied. Saliva specimens from 58 inpatients confirmed by qRT-PCR for SARS-CoV-2 using nasopharyngeal specimens, and 105 AHW were studied by qRT-PCR using three sets of primers for the N (N1, N2, and N3) gene of SARS-CoV-2, according to the CDC Diagnostic Panel protocol, showing a positivity of 88% for inpatients and 8% for AHW. Bivariate analysis revealed an association between Ct < 38.0 values for N2 and mechanical ventilation assistance among patients (p = .013). In addition, values of aspartate-transaminase, lactate dehydrogenase, and ferritin showed significant correlations with Ct values of N1 and N3 genes in inpatients. Therefore, our results show that Ct values correlate with some relevant clinical data for inpatients with COVID-19.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- image quality
- dual energy
- computed tomography
- contrast enhanced
- coronavirus disease
- mechanical ventilation
- public health
- positron emission tomography
- healthcare
- electronic health record
- magnetic resonance imaging
- big data
- mental health
- genome wide
- magnetic resonance
- randomized controlled trial
- intensive care unit
- machine learning
- cell cycle
- cell proliferation
- real time pcr
- gene expression
- social media
- data analysis
- bioinformatics analysis