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Abstract
REGEN-COV, a combination of the monoclonal antibodies casirivimab and imdevimab, has been approved as a treatment for high-risk patients infected with SARS-CoV-2 within five days of their diagnosis. We performed a retrospective cohort study, and used data repositories of Israel’s largest healthcare organization to determine the real-world effectiveness of REGEN-COV treatment against COVID-19-related hospitalization, severe disease, and death. We compared patients infected with Delta variant and treated with REGEN-COV (n = 289) to those infected but not-treated with REGEN-COV (n = 1,296). Demographic and clinical characteristics were used to match patients and for further adjustment as part of the C0x model. Estimated treatment effectiveness was defined as one minus the hazard ratio. Treatment effectiveness of REGEN-COV was 56.4% (95% CI: 23.7–75.1%) in preventing COVID-19 hospitalization, 59.2% (95% CI: 19.9–79.2%) in preventing severe COVID-19, and 93.5% (95% CI: 52.1–99.1%) in preventing COVID-19 death in the 28 days after treatment. In conclusion, REGEN-COV was effective in reducing the risk of severe sequelae in high-risk COVID-19 patients.
REGEN-COV is a SARS-CoV-2 combined monoclonal antibody treatment which has been shown to be effective in randomised controlled trials. Here, the authors assess its real-world effectiveness using data from Israel during the Delta wave and find that it reduced the risk of hospitalisation, severe disease and death.
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1 Clalit Health Services, Clalit Research Institute, Innovation Division, Tel Aviv, Israel (GRID:grid.414553.2) (ISNI:0000 0004 0575 3597)
2 Clalit Health Services, Clalit Research Institute, Innovation Division, Tel Aviv, Israel (GRID:grid.414553.2) (ISNI:0000 0004 0575 3597); Ben Gurion University, Software and Information Systems Engineering, Be’er Sheva, Israel (GRID:grid.7489.2) (ISNI:0000 0004 1937 0511); Harvard Medical School, Department of Biomedical Informatics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
3 The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Boston Children’s Hospital, Predictive Medicine Group, Computational Health Informatics Program, Boston, USA (GRID:grid.2515.3) (ISNI:0000 0004 0378 8438); Harvard Medical School, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
4 Ben Gurion University, Software and Information Systems Engineering, Be’er Sheva, Israel (GRID:grid.7489.2) (ISNI:0000 0004 1937 0511); Harvard Medical School, Department of Biomedical Informatics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
5 Clalit Health Services, Clalit Community Division, Tel Aviv, Israel (GRID:grid.414553.2) (ISNI:0000 0004 0575 3597)
6 Israel Defense Forces Medical Corps, Tel Aviv, Israel (GRID:grid.414541.1); Hebrew University, Department of Military Medicine, Faculty of Medicine, Jerusalem, Israel (GRID:grid.9619.7) (ISNI:0000 0004 1937 0538)
7 Clalit Health Services, Clalit Research Institute, Innovation Division, Tel Aviv, Israel (GRID:grid.414553.2) (ISNI:0000 0004 0575 3597); The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Ben Gurion University of the Negev, School of Public Health, Faculty of Health Sciences, Be’er Sheva, Israel (GRID:grid.7489.2) (ISNI:0000 0004 1937 0511)