ABSTRACT
Early detection of SARS-CoV-2 in the emergency department (ED) is a crucial necessity, especially in settings of overcrowding: establishing a pre-diagnostic test probability of infection would help to triage patients and reduce diagnostic errors, and it could be useful in resource-limited countries. Here, we established and validated a clinical predictor of infection based on routine admission hematological parameters. The diagnostic model was developed by comparing 85 consecutive patients with symptomatic COVID-19 confirmed by RT-PCR with 85 symptomatic, SARS-CoV-2-negative controls. Abnormal hematological parameters significantly (p < 0.05) associated with SARS-CoV-2 infection were used to derive a "cumulative score" between 0 and 16. The model was validated in an independent cohort of 170 SARS-CoV-2-positive patients. Several routine hematology parameters were significantly (p < 0.05) associated with SARS-CoV-2 infection. A "cumulative score" score ≥7 discriminated COVID-19-postive patients from controls with a sensitivity of 94% and specificity of 100% (p < 0.001). The high sensitivity of the predictive model was confirmed in the prospective validation set, and the cumulative score (i) predicted SARS-CoV-2 positivity even when the first oro-nasopharyngeal swab RT-PCR result was reported as a false negative in both cohorts and (ii) resulted to be independent from disease severity. The cumulative score based on routine blood parameters can be used to predict an early and accurate diagnosis of SARS-CoV-2 infection in symptomatic patients, thereby facilitating triage and optimizing early management and isolation from the COVID-19 free population, particularly useful in overcrowding situations and in resource-poor settings.
ABSTRACT
SARS-CoV-2 virus is responsible for the current worldwide coronavirus disease 2019 (COVID-19) pandemic, infecting millions of people and causing hundreds of thousands of deaths. Understanding the antibody response to SARS-CoV-2 is crucial for the development of vaccines, therapeutics and public health interventions. However, lack of consistency in methods used to monitor antibody response to SARS-CoV-2 leaves some uncertainty in our fine understanding of the human antibody response mounted following SARS-CoV-2 infection. We developed a peptide-based enzyme-linked immunosorbent assay (ELISA) by selecting 7 synthetic peptides from the spike, membrane, and nucleocapsid protein sequences of SARS-CoV-2, which effectively detects the antibody response mounted by all COVID-19 convalescent tested. Strikingly, the assay shows a profound difference in antibody response among individual subjects, which may have a significant impact on disease severity. Together, our results define an efficient and specific serological assay to consistently measure the antibody response following SARS-CoV-2 infection, as well as help the design of vaccine and therapeuticals for prevention and treatment of COVID-19.