The Impact of SARS-CoV-2 Viral Load on the Mortality of Hospitalized Patients: A Retrospective Analysis

Introduction Coronavirus disease 2019 (COVID-19) has emerged worldwide since December 2019. The standard method for diagnosis is via nucleic acid amplification testing, usually with a reverse-transcription polymerase chain reaction (RT-PCR). Hospitalized infected individuals may require ventilation and may have higher mortality rates. We aim to evaluate the clinical impact of nasopharyngeal viral load on these outcomes. Materials and methods We conducted a retrospective cohort study of patients hospitalized with COVID-19 from 17 March 2020 to 1 June 2020 at a tertiary care hospital. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load was assessed using cycle threshold (Ct) values from an RT-PCR assay applied to nasopharyngeal swab samples. We compared the clinical characteristics of survivors vs. non-survivors and assessed whether the viral load was independently associated with in-hospital 30-day mortality. Results We evaluated 197 patients. Thirty-day mortality was verified in 71 (36%) subjects. In the adjusted effects model, only the E-gene Ct value [odd ratio (OR) .873; confidence interval (CI) 95% .769-.992; p .037], age, the number of days of symptoms before admission, lactate dehydrogenase (LDH), and the oxygen saturation (SatO(2))-to-fraction of inspired oxygen (FiO(2)) ratio remained significantly associated with 30-day mortality. There was no identified association between the viral loads and disease severity, the need for ventilation, or length of stay. Discussion Our results are, in part, concordant with previous papers. One possible limitation to our study is the fact that possibly included disproportionately more patients with poorer outcomes since hospitalization was required. Therefore, further research is required. Conclusion SARS-CoV-2 viral load on admission may be an independent predictor of 30-day mortality among hospitalized patients with COVID-19. Providing this information to clinicians could potentially be used to guide risk stratification.