|
KMID : 1144320210530040767
|
|
감염과 화학요법
2021 Volume.53 No. 4 p.767 ~ p.775
|
|
|
Clinical Effectiveness of REGN-COV2 in Patients with COVID-19 in Japan: A Retrospective Cohort Study with a Bayesian Inference
|
|
Amano Norihiko
Iwata Kentaro
Iwata Sachiyo
|
|
|
Abstract
|
|
|
Background: Neutralizing antibody cocktail therapy, REGN-COV2, is promising in preventing a severe form of coronavirus disease 2019 (COVID-19), but its effectiveness in Japan has not been fully investigated.
Materials and Methods: To evaluate the effectiveness of REGN-COV2, clinical data of 20 patients with COVID-19 who received REGN-COV2 was compared with the control by matching age and sex. The primary outcome was the time from the onset to defervescence, the duration of hospitalization, and oxygen requirement. A sensitivity analysis using Bayesian analysis was also conducted.
Results: The time to defervescence was significantly shorter in the treatment group (5.25 vs. 7.95 days, P = 0.02), and so was the duration of hospitalization (7.115 vs. 11.45, P = 0.0009). However, the oxygen therapy requirement did not differ between the two groups (15% vs. 35%, P = 0.27). For Bayesian analysis, the median posterior probability of the time to defervescence since the symptom onset on the REGN-COV2 group was 5.28 days [95% credible interval (CrI): 4.28 - 6.31 days], compared with the control of 7.99 days (95% CrI: 6.81 - 9.24 days). The posterior probability of the duration of the hospitalization on the REGN-COV2 group was 7.17 days (95% CrI: 5.99 - 8.24 days), compared with the control of 11.54 days (95% CrI: 10.28 - 13.14 days). The posterior probability of the oxygen requirement on the REGN-COV2 group was 18% (95% CrI: 3 - 33%), compared with the control of 36% (95% CrI: 16 - 54%).
Conclusion: REGN-COV2 may be effective in early defervescence and shorter hospitalization. Its effectiveness for preventing a severe form of infection needs to be evaluated by further studies.
|
|
|
KEYWORD
|
|
|
Coronavirus disease 2019, REGN-COV-2, Bayesian statistics, Markov Chain Monte Carlo method
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
  
|
|