TY - JOUR
T1 - Clinical and laboratory profiles of the SARS-CoV-2 Delta variant compared with pre-Delta variants
AU - Bhakta, Shivang
AU - Sanghavi, Devang K.
AU - Johnson, Patrick W.
AU - Kunze, Katie L.
AU - Neville, Matthew R.
AU - Wadei, Hani M.
AU - Bosch, Wendelyn
AU - Carter, Rickey E.
AU - Shah, Sadia Z.
AU - Pollock, Benjamin D.
AU - Oman, Sven P.
AU - Speicher, Leigh
AU - Siegel, Jason
AU - Libertin, Claudia R.
AU - Matson, Mark W.
AU - Moreno Franco, Pablo
AU - Cowart, Jennifer B.
N1 - Funding Information:
The authors have no competing interests to declare. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Approval was not required.
Publisher Copyright:
© 2022
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - COVID-19
KW - Delta variant
KW - Genomics
KW - Gradient boosting model
KW - Machine learning
KW - Variants of concern
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U2 - 10.1016/j.ijid.2022.04.050
DO - 10.1016/j.ijid.2022.04.050
M3 - Article
C2 - 35487339
AN - SCOPUS:85129838703
SN - 1201-9712
VL - 120
SP - 88
EP - 95
JO - International Journal of Infectious Diseases
JF - International Journal of Infectious Diseases
ER -