Clinical and laboratory profiles of the SARS-CoV-2 Delta variant compared with pre-Delta variants. (July 2022) - Bhakta, Shivang; Sanghavi, Devang K.; Johnson, Patrick W.; Kunze, Katie L.; Neville, Matthew R.; Wadei, Hani M.; Bosch, Wendelyn; Carter, Rickey E.; Shah, Sadia Z.; Pollock, Benjamin D.; Oman, Sven P.; Speicher, Leigh; et al.
Highlights: Clinical characteristics differed between the Delta and pre-Delta variant in hospitalized cases. Patients with Delta-like characteristics have higher inflammatory markers. The gradient boosting model may identify patients with Delta-like characteristics. This approach may address real-world challenges of variant sequencing. Abstract: Objectives: The emergence of SARS-CoV-2 variants of concern has led to significant phenotypical changes in transmissibility, virulence, and public health measures. Our study used clinical data to compare characteristics between a Delta variant wave and a pre-Delta variant wave of hospitalized patients. Methods: This single-center retrospective study defined a wave as an increasing number of COVID-19 hospitalizations, which peaked and later decreased. Data from the United States Department of Health and Human Services were used to identify the waves' primary variant. Wave 1 (August 8, 2020–April 1, 2021) was characterized by heterogeneous variants, whereas Wave 2 (June 26, 2021–October 18, 2021) was predominantly the Delta variant. Descriptive statistics, regression techniques, and machine learning approaches supported the comparisons between waves. Results: From the cohort (N = 1318), Wave 2 patients (n = 665) were more likely to be younger, have fewer comorbidities, require more care in the intensive care unit, and show an inflammatory profile with higher C-reactive protein, lactate dehydrogenase, ferritin, fibrinogen, prothrombin time, activated thromboplastin time, and international normalized ratio compared with Wave 1 patients (n = 653). The gradient boosting model showed an area under the receiver operating characteristic curve of 0.854 (sensitivity 86.4%; specificity 61.5%; positive predictive value 73.8%; negative predictive value 78.3%). Conclusion: Clinical and laboratory characteristics can be used to estimate the COVID-19 variant regardless of genomic testing availability. This finding has implications for variant-driven treatment protocols and further research.