Impact of dementia, living in a long-term care facility, and physical activity status on COVID-19 severity in older adults

BackgroundJapan is fast becoming an extremely aged society and older adults are known to be at risk of severe COVID-19. However, the impact of risk factors specific to this population for severe COVID-19 caused by the Omicron variant of concern (VOC) are not yet clear. MethodsWe performed an exploratory analysis using logistic regression to identify risk factors for severe COVID-19 illness among 4,868 older adults with a positive SARS-CoV-2 test result who were admitted to a healthcare facility between 1 January 2022 and 16 May 2022. We then conducted one-to-one propensity score (PS) matching for three factors--dementia, admission from a long-term care facility, and poor physical activity status--and used Fishers exact test to compare the proportion of severe COVID-19 cases in the matched data. We also estimated the average treatment effect on treated (ATT) in each PS matching analysis. ResultsOf the 4,868 cases analyzed, 1,380 were severe. Logistic regression analysis showed that age, male sex, cardiovascular disease, cerebrovascular disease, chronic lung disease, renal failure and/or dialysis, physician-diagnosed obesity, admission from a long-term care facility, and poor physical activity status were risk factors for severe disease. Vaccination and dementia were identified as factors associated with non-severe illness. The ATT for dementia, admission from a long-term care facility, and poor physical activity status was -0.04 (95% confidence interval -0.07, -0.01), 0.09 (0.06, 0.12), and 0.17 (0.14, 0.19), respectively. ConclusionsOur results suggest that poor physical activity status and living in a long-term care facility have a substantial impact on the risk of severe COVID-19 caused by the Omicron VOC, while dementia might be associated with non-severe illness.

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

BackgroundWith 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. MethodsPatients 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. ResultsWe 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. DiscussionUsing 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

BackgroundWe aimed to assess the impact of regional heterogeneity on the severity of COVID-19 in Japan. MethodsWe 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. ResultsThe 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. ConclusionsOur 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

ObjectivesTo 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. DesignA observational cohort study utilizing data entered in a Japan nationwide COVID-19 inpatient registry, COVIREGI-JP. SettingAs 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. ParticipantsParticipants 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 measuresPrimary 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. ResultsRisk 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. ConclusionsThis 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 registrationUMIN000039873. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000045453 Strengths and limitations of this studyO_LIIn this article, we studied the disease progression of COVID-19, by comparing the risk factors on three points: early severity, worst severity, and fatality. C_LIO_LIOur results are useful from a public health perspective, as we provide risk factors for predicting the severity on admission and disease progression from patients background factors. C_LIO_LIThis study pointed out the possibility that risk factors of the severity on admission, worst severity, and fatality are not consistent and may be propelled by different factors. C_LIO_LIOur data were collected from hundreds of healthcare facilities; thus data accuracy may be questionable. C_LIO_LIAlso, treatment type, dosage, duration, and combination varied immensely across the facilities and we did not consider treatments prior to and during hospitalization in the analysis. C_LI

Efficacy of remdesivir in Japanese patients hospitalised with COVID-19: A large observational study using the COVID-19 Registry Japan

ObjectivesAlthough 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. MethodsWe 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). ResultsThe 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). ConclusionsRemdesivir 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.