Drug effectiveness for COVID-19 inpatients inferred from Japanese medical claim data using propensity score matching [version 1; peer review: 1 approved with reservations]
Background: Earlier studies and clinical trials have shown that the drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs are expected to prevent severe coronavirus 2019 (COVID-19) outcomes and death. Methods: We used observational data for Japan to assess the e...
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Published in | F1000 research Vol. 12; p. 398 |
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2023
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Abstract | Background: Earlier studies and clinical trials have shown that the drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs are expected to prevent severe coronavirus 2019 (COVID-19) outcomes and death.
Methods: We used observational data for Japan to assess the effectiveness of these drugs for COVID-19. We applied propensity scoring, which can treat the choice of administered drug as a random assignment to inpatients, to the Medical Information Analysis Databank operated by National Hospital Organization in Japan. The outcome was defined as mortality. Subjects were all inpatients, inpatients with oxygen administration, and inpatients using respiratory ventilators, classified by three age classes: all ages, 65 years old or older, and younger than 65 years old. Information about demographical characteristics, underlying disease, administered drug, the proportion of Alpha, Beta and Omicron variant strains, and vaccine coverage were used as explanatory variable in logistic regression.
Results: Estimated results indicated that only an antibody cocktail (sotrovimab, casirivimab and imdevimab) raised the probability to save life consistently. By contrast, other drugs might reduce the probability of saving life. The results indicated that an antiviral drug (remdesivir), a steroid (dexamethasone), and an anti-inflammatory drug (baricitinib and tocilizumab) might not contribute to saving life even at the pseudo-situation of random assignment. However, this logistic regression at the first step might have only insufficient explanatory power.
Conclusions: We found a high likelihood that antibody cocktails were consistently effective to raise the probability of saving life, though a lesser likelihood in other drugs for older patients with mild to severe severity and all age patients with moderate severity. |
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AbstractList | Background: Earlier studies and clinical trials have shown that the drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs are expected to prevent severe coronavirus 2019 (COVID-19) outcomes and death.
Methods: We used observational data for Japan to assess the effectiveness of these drugs for COVID-19. We applied propensity scoring, which can treat the choice of administered drug as a random assignment to inpatients, to the Medical Information Analysis Databank operated by National Hospital Organization in Japan. The outcome was defined as mortality. Subjects were all inpatients, inpatients with oxygen administration, and inpatients using respiratory ventilators, classified by three age classes: all ages, 65 years old or older, and younger than 65 years old. Information about demographical characteristics, underlying disease, administered drug, the proportion of Alpha, Beta and Omicron variant strains, and vaccine coverage were used as explanatory variable in logistic regression.
Results: Estimated results indicated that only an antibody cocktail (sotrovimab, casirivimab and imdevimab) raised the probability to save life consistently. By contrast, other drugs might reduce the probability of saving life. The results indicated that an antiviral drug (remdesivir), a steroid (dexamethasone), and an anti-inflammatory drug (baricitinib and tocilizumab) might not contribute to saving life even at the pseudo-situation of random assignment. However, this logistic regression at the first step might have only insufficient explanatory power.
Conclusions: We found a high likelihood that antibody cocktails were consistently effective to raise the probability of saving life, though a lesser likelihood in other drugs for older patients with mild to severe severity and all age patients with moderate severity. Background: Earlier studies and clinical trials have shown that the drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs are expected to prevent severe coronavirus 2019 (COVID-19) outcomes and death. Methods: We used observational data for Japan to assess the effectiveness of these drugs for COVID-19. We applied propensity scoring, which can treat the choice of administered drug as a random assignment to inpatients, to the Medical Information Analysis Databank operated by National Hospital Organization in Japan. The outcome was defined as mortality. Subjects were all inpatients, inpatients with oxygen administration, and inpatients using respiratory ventilators, classified by three age classes: all ages, 65 years old or older, and younger than 65 years old. Information about demographical characteristics, underlying disease, administered drug, the proportion of Alpha, Beta and Omicron variant strains, and vaccine coverage were used as explanatory variable in logistic regression. Results: Estimated results indicated that only an antibody cocktail (sotrovimab, casirivimab and imdevimab) raised the probability to save life consistently. By contrast, other drugs might reduce the probability of saving life. The results indicated that an antiviral drug (remdesivir), a steroid (dexamethasone), and an anti-inflammatory drug (baricitinib and tocilizumab) might not contribute to saving life even at the pseudo-situation of random assignment. However, this logistic regression at the first step might have only insufficient explanatory power. Conclusions: We found a high likelihood that antibody cocktails were consistently effective to raise the probability of saving life, though a lesser likelihood in other drugs for older patients with mild to severe severity and all age patients with moderate severity. |
Author | Taniguchi, Kiyosu Horiguchi, Hiromasa Mitsushima, Shingo |
Author_xml | – sequence: 1 givenname: Shingo orcidid: 0000-0002-5827-5475 surname: Mitsushima fullname: Mitsushima, Shingo email: mitsushi@niid.go.jp organization: Center for Field Epidemic Intelligence, Research and Professional Development, National Institute of Infectious Diseases, Shinjuku-ku, Tokyo, 1620052, Japan – sequence: 2 givenname: Hiromasa surname: Horiguchi fullname: Horiguchi, Hiromasa organization: Department of Clinical Data Management and Research, Clinical Research Center, National Hospital Organization, Meguro-ku, Tokyo, 1528621, Japan – sequence: 3 givenname: Kiyosu surname: Taniguchi fullname: Taniguchi, Kiyosu organization: General Director, National Hospital Organization Mie National Hospital, Tsu, Mie, 5140125, Japan |
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Cites_doi | 10.1016/S0140-6736(22)01586-0 10.1097/BSD.0000000000000932 10.6084/m9.figshare.22102016.v1 10.2307/2971733 10.1056/NEJMoa2107934 10.1056/NEJMoa2118542 10.3389/fphar.2022.742273 10.1371/journal.pmed.1003820 10.1056/NEJMoa2108163 10.1056/NEJMoa2030340 10.2147/CLEP.S359072 10.1080/22221751.2022.2155250 10.1093/cid/ciab875 10.1056/NEJMoa2116044 10.1016/j.cjca.2015.05.015 10.1016/S2213-2600(21)00331-3 10.1056/NEJMoa2021436 10.1056/NEJMoa2100433 |
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Copyright | Copyright: © 2023 Mitsushima S et al. Copyright: © 2023 Mitsushima S et al. 2023 |
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Keywords | COVID-19 antiviral drug mutated strain mortality underlying diseases antibody cocktail steroid and anti-inflammatory drug |
Language | English |
License | http://creativecommons.org/licenses/by/4.0/: This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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Notes | No competing interests were disclosed. |
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Snippet | Background: Earlier studies and clinical trials have shown that the drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs... Background: Earlier studies and clinical trials have shown that the drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs... |
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Title | Drug effectiveness for COVID-19 inpatients inferred from Japanese medical claim data using propensity score matching [version 1; peer review: 1 approved with reservations] |
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