Impact of regional heterogeneity on the severity of COVID-19 (preprint)

Background: We aimed to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. Methods: We included 27,865 cases registered between January 2020 and February 2021 in the COVID-19 Registry of Japan to examine the relationship between the National Early Warning Score (NEWS) of COVID-19 patients on the day of admission and the prefecture where the patients live. A hierarchical Bayesian model was used to examine the random effect of each prefecture in addition to the patients’ backgrounds. In addition, we compared the results of two models; one model included the number of beds secured for COVID-19 patients in each prefecture as one of the fixed effects, and the other model did not. Results: The results indicated that the prefecture had a substantial impact on the severity of COVID-19 on admission. Even when considering the effect of the number of beds separately, the heterogeneity caused by the random effect of each prefecture affected the severity of the case on admission. Conclusions: Our analysis revealed a possible association between regional heterogeneity and increased/decreased risk of severe COVID-19 infection on admission. This heterogeneity was derived not only from the number of beds secured in each prefecture but also from other factors.

Predictive Model of Risk Factors of High Flow Nasal Cannula Using Machine Learning in COVID-19 (preprint)

Background: With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Methods: Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period and normal times, respectively. Results: We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40-59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60-79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Discussion: Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

Impact of regional heterogeneity on the severity of COVID-19 (preprint)

Background: We aimed to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. Methods: We included 27,865 cases registered between January 2020 and February 2021 in the COVID-19 Registry of Japan to examine the relationship between the National Early Warning Score (NEWS) of COVID-19 patients on the day of admission and the prefecture where the patients live. A hierarchical Bayesian model was used to examine the random effect of each prefecture in addition to the patients' backgrounds. In addition, we compared the results of two models; one model included the number of beds secured for COVID-19 patients in each prefecture as one of the fixed effects, and the other model did not. Results: The results indicated that the prefecture had a substantial impact on the severity of COVID-19 on admission. Even when considering the effect of the number of beds separately, the heterogeneity caused by the random effect of each prefecture affected the severity of the case on admission. Conclusions: Our analysis revealed a possible association between regional heterogeneity and increased/decreased risk of severe COVID-19 infection on admission. This heterogeneity was derived not only from the number of beds secured in each prefecture but also from other factors.

Risk factors for severity on admission and the disease progression during hospitalization in a large cohort of COVID-19 patients in Japan (preprint)

Objectives: To investigate the risk factors contributing to severity on admission. Additionally, risk factors on worst severity and fatality were studied. Moreover, factors were compared based on three points: early severity, worst severity, and fatality. Design: A observational cohort study utilizing data entered in a Japan nationwide COVID-19 inpatient registry, COVIREGI-JP. Setting: As of August 31, 2020, 7,546 cases from 780 facilities have been registered. Participating facilities cover a wide range of hospitals where COVID-19 patients are admitted in Japan. Participants: Participants who had a positive test result on any applicable SARS-CoV-2 diagnostic tests, and were admitted to participating healthcare facilities. A total of 3,829 cases were identified from January 16 to May 31, 2020, of which 3,376 cases were included in this study. Primary and secondary outcoe measures: Primary outcome was severe or non-severe on admission, determined by the requirement of mechanical ventilation or oxygen therapy, SpO2, or respiratory rate. Secondary outcome was the worst severity during hospitalization, judged by the requirement of oxygen and/or IMV/ECMO. Results: Risk factors for severity on admission were older age, male, cardiovascular disease, chronic respiratory disease, diabetes, obesity, and hypertension. Cerebrovascular disease, liver disease, renal disease or dialysis, solid tumor, and hyperlipidemia did not influence severity on admission ; however it influenced worst severity. Fatality rates for obesity, hypertension, and hyperlipidemia were relatively lower. Conclusions: This study segregated the comorbidities driving severity and death. It is possible that risk factors for severity on admission, worst severity, and fatality are not consistent and may be propelled by different factors. Specifically, while hypertension, hyperlipidemia, and obesity had major effect on worst severity, their impact was mild on fatality in the Japanese population. Some studies contradict our results; therefore, detailed analyses, considering in-hospital treatments, are needed for validation. Trial registration: UMIN000039873. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000045453

Efficacy of remdesivir in hospitalised COVID-19 patients in Japan: A large observational study using the COVID-19 Registry Japan (preprint)

Objectives Although several randomised controlled trials have compared the efficacy of remdesivir with that of placebo, there is limited evidence regarding its effect in the early stage of nonsevere COVID-19 cases. Methods We evaluated the efficacy of remdesivir on the early stage of nonsevere COVID-19 using the COVID-19 Registry Japan, a nationwide registry of hospitalised COVID-19 patients in Japan. Two regimens (start remdesivir therapy within 4 days from admission vs. no remdesivir during hospitalisation) among patients without the need for supplementary oxygen therapy were compared by a three-step processing (cloning, censoring, and weighting) method. The primary outcome was supplementary oxygen requirement during hospitalisation. Secondary outcomes were 30-day fatality risk and risk of invasive mechanical ventilation or extracorporeal membrane oxygenation (IMV/ECMO). Results The data of 12,657 cases met our inclusion criteria. The ‘start remdesivir’ regimen showed a lower risk of supplementary oxygen requirement (hazard ratio: 0.861, p < 0.001). Both 30-day fatality risk and risk of IMV/ECMO introduction were not significantly different between the two regimens (hazard ratios: 1.05 and 0.886, p values: 0.070 and 0.440, respectively). Conclusions Remdesivir might reduce the risk of oxygen requirement during hospitalisation in the early stage of COVID-19; however, it had no positive effect on the clinical outcome and reduction of IMV/ECMO requirement.

Effect of evacuation of Japanese residents from Wuhan, China, on preventing transmission of novel coronavirus infection: a modelling study (preprint)

Objective: In late January 2020, the Japanese government carried out three evacuations by aircraft from Wuhan, China, to avoid further cases of coronavirus disease 2019 (COVID-19) among Wuhan's Japanese residents. Evacuation by aircraft may be an effective countermeasure against outbreaks of infectious diseases, but evidence of its effect is scarce. This study estimated how many COVID-19 cases were prevented among the Japanese residents of Wuhan by the evacuation countermeasure. Results: Eleven imported COVID-19 cases were reported on Feb 1 from among the total 566 evacuees who returned to Japan. In the case of no evacuations being made, the cumulative number of COVID-19 cases among Wuhan's Japanese residents was estimated to reach 25 (95% CI [20,29]) on Feb 8 and 34 (95% CI [28,40]) on Feb 15. A 1-week delay in the evacuation might be led to 14 additional cases and a 2-week delay to 23 additional cases. Evacuation by aircraft can contribute substantially to reducing the number of infected cases in the initial stage of the outbreak.

Rethinking antiviral effects for COVID-19 in clinical studies: early initiation is key to successful treatment (preprint)

Development of an effective antiviral drug for COVID-19 is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence for effective drugs from clinical studies is limited. The lack of evidence could be in part due to heterogeneity of virus dynamics among patients and late initiation of treatment. We first quantified the heterogeneity of viral dynamics which could be a confounder in compassionate use programs. Second, we demonstrated that an antiviral drug is unlikely to be effective if initiated after a short period following symptom onset. For accurate evaluation of the efficacy of an antiviral drug for COVID-19, antiviral treatment should be initiated before or soon after symptom onset in randomized clinical trials.

Modelling SARS-CoV-2 Dynamics: Implications for Therapy (preprint)

The scientific community is focussed on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data collected from the same specimen (throat / nasal swabs or nasopharyngeal / sputum / tracheal aspirate), we compare within-host dynamics for patients infected in the current outbreak with analogous dynamics for MERS-CoV and SARS-CoV infections. Our quantitative analyses revealed that SARS-CoV-2 infection dynamics are more severe than those for mild cases of MERS-CoV, but are similar to severe cases, and that the viral dynamics of SARS-CoV infection are similar to those of MERS-CoV in mild cases but not in severe case. Consequently, SARS-CoV-2 generates infection dynamics that are more severe than SARS-CoV. Furthermore, we used our viral dynamics model to predict the effectiveness of unlicensed drugs that have different methods of action. The effectiveness was measured by AUC of viral load. Our results indicated that therapies that block de novo infections or virus production are most likely to be effective if initiated before the peak viral load (which occurs around three days after symptom onset on average), but therapies that promote cytotoxicity are likely to have only limited effects. Our unique mathematical approach provides insights into the pathogenesis of SARS-CoV-2 in humans, which are useful for development of antiviral therapies.