First and second COVID-19 waves in Japan: A comparison of disease severity and characteristics

Epidemiology of Coronavirus Disease 2019 in Japan: Descriptive Findings and Lessons Learned through Surveillance during the First Three Waves

Introduction

Coronavirus disease 2019 (COVID-19) has caused unprecedented global morbidity and mortality. Japan has faced three epidemic "waves" of COVID-19 from early 2020 through early 2021. Here we narratively review the three waves in Japan, describe the key epidemiologic features of COVID-19, and discuss lessons learned.

Methods

We assessed publicly available surveillance data, routine surveillance reports, and other relevant sources-multiple indicators were monitored to improve interpretation of surveillance data. Weekly trends for each wave were described based on the number of case notifications; number of tests performed; proportion of those tests that were positive for the novel coronavirus; the prevalent number of COVID-19 hospitalizations (total hospitalizations and those categorized as severe); and number of COVID-19 deaths. For each indicator and wave, we recorded the first calendar week to show an increase over two consecutive previous weeks, along with the peak week.

Results

The spring wave was characterized by detection of cases imported from China, followed by notifications of sporadic cases without travel history, clusters, and mild/asymptomatic cases. The summer wave saw a large increase in notifications and a younger age distribution, but in the context of increased testing with lower test positivity. The winter wave brought considerable morbidity and mortality, surpassing the cumulative case counts and fatalities from the earlier waves, with high peak values. Overall, relative to the first wave, the burden of severe outcomes was lower in the second and higher in the third wave, but varied by prefecture. In all three waves, severe outcomes peaked after notification counts and test positivity peaked; severe outcomes were also consistently skewed toward the elderly.

Conclusions

Important lessons were learned from each wave and across waves-some aspects remained constant, while others changed over time. In order to rapidly detect an increase in incidence, continuous, timely, and sensitive surveillance-using multiple information sources with careful interpretations-will be key in COVID-19 control.

Clinical profile of hospitalized COVID-19 patients in first & second wave of the pandemic: Insights from an Indian registry based observational study

Background & objectives:

India witnessed a massive second surge of COVID-19 cases since March 2021 after a period of decline from September 2020. Data collected under the National Clinical Registry for COVID-19 (NCRC) were analysed to describe the differences in demographic and clinical features of COVID-19 patients recruited during these two successive waves.

Methods:

The NCRC, launched in September 2020, is an ongoing multicentre observational initiative, which provided the platform for the current investigation. Demographic, clinical, treatment and outcome data of hospitalized, confirmed COVID-19 patients were captured in an electronic data portal from 41 hospitals across India. Patients enrolled during September 1, 2020 to January 31, 2021 and February 1 to May 11, 2021 constituted participants of the two successive waves, respectively.

Results:

As on May 11, 2021, 18961 individuals were recruited in the registry, 12059 and 6903 reflecting in-patients from the first and second waves, respectively. Mean age of the patients was significantly lower in the second wave [48.7 (18.1) yr vs. 50.7 (18.0) yr, P<0.001] with higher proportion of patients in the younger age group intervals of <20, and 20-39 yr. Approximately 70 per cent of the admitted patients were ≥ 40 yr of age in both waves of the pandemic. The proportion of males were slightly lower in second wave as compared to the first [4400 (63.7%) vs. 7886 (65.4%), P=0.02]. Commonest presenting symptom was fever in both waves. In the second wave, a significantly higher proportion [2625 (48.6%) vs. 4420 (42.8%), P<0.003] complained of shortness of breath, developed ARDS [422(13%) vs. 880 (7.9%), P<0.001], required supplemental oxygen [1637 (50.3%) vs. 4771 (42.7%), P<0.001], and mechanical ventilation [260 (15.9%) vs. 530 (11.1%), P<0.001]. Mortality also significantly increased in the second wave [OR: 1.35 (95% CI: 1.19, 1.52)] in all age groups except in <20 yr.

Interpretation & conclusions:

The second wave of COVID-19 in India was slightly different in presentation than the first wave, with a younger demography, lesser comorbidities, and presentation with breathlessness in greater frequency.

Difference in mortality among individuals admitted to hospital with COVID-19 during the first and second waves in South Africa: a cohort study

Background

The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves.

Methods

In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression.

Findings

Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240·4 cases per 100 000 people vs 136·0 cases per 100 000 people; admissions, 27·9 admissions per 100 000 people vs 16·1 admissions per 100 000 people; deaths, 8·3 deaths per 100 000 people vs 3·6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1·19, 95% CI 1·18–1·20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40–64 years (adjusted odds ratio [aOR] 1·22, 95% CI 1·14–1·31), and older than 65 years (aOR 1·38, 1·25–1·52), compared with younger than 40 years; of Mixed race (aOR 1·21, 1·06–1·38) compared with White race; and admitted in the public sector (aOR 1·65, 1·41–1·92); and less likely to be Black (aOR 0·53, 0·47–0·60) and Indian (aOR 0·77, 0·66–0·91), compared with White; and have a comorbid condition (aOR 0·60, 0·55–0·67). For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1·31, 95% CI 1·28–1·35). In-hospital case-fatality risk increased from 17·7% in weeks of low admission (<3500 admissions) to 26·9% in weeks of very high admission (>8000 admissions; aOR 1·24, 1·17–1·32).

Interpretation

In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage.

Funding

DATCOV as a national surveillance system is funded by the National Institute for Communicable Diseases and the South African National Government.

Predictors of severe COVID-19 in kidney transplant recipients in the different epidemic waves: Analysis of the Spanish Registry

SARS-CoV-2 infection has produced high mortality in kidney transplant (KT) recipients, especially in the elderly. Until December 2020, 1011 KT with COVID-19 have been prospectively included in the Spanish Registry and followed until recovery or death. In multivariable analysis, age, pneumonia, and KT performed ≤6 months before COVID-19 were predictors of death, whereas gastrointestinal symptoms were protective. Survival analysis showed significant increasing mortality risk in four subgroups according to recipient age and time after KT (age <65 years and posttransplant time >6 months, age <65 and time ≤6, age ≥65 and time >6 and age ≥65 and time ≤6): mortality rates were, respectively, 11.3%, 24.5%, 35.4%, and 54.5% (p < .001). Patients were significantly younger, presented less pneumonia, and received less frequently specific anti-COVID-19 treatment in the second wave (July-December) than in the first one (March-June). Overall mortality was lower in the second wave (15.1 vs. 27.4%, p < .001) but similar in critical patients (66.7% vs. 58.1%, p = .29). The interaction between age and time post-KT should be considered when selecting recipients for transplantation in the COVID-19 pandemic. Advanced age and a recent KT should foster strict protective measures, including vaccination.

The profile of patients hospitalized with COVID-19 under the Quarantine Act in a designated hospital near an international airport in Japan

The Japanese Government has implemented quarantine measures in response to the COVID-19 pandemic. Individuals testing positive at the airport's quarantine office were lodged either in a designated hotel or hospital under the Quarantine Act. The aim of this study is to describe the management of patients with COVID-19 admitted under the Quarantine Act and to evaluate its impact on medical resources. Data were retrospectively collected, including demographics, comorbidities, status at admission, clinical condition, treatment, outcomes, status at discharge, duration of hospitalization, and the cost of hospitalization for all patients hospitalized with COVID-19 at this facility under the Quarantine Act between January 2020 and April 2021. A total of 48 patients (39 males, 9 females; median age: 38.5 years) with COVID-19, half (52.1%) of which were Japanese, were hospitalized under the Quarantine Act. The majority (87.5%) of the patients lived or planned to stay outside of Chiba Prefecture. The most frequent time of admission was 9 PM-1 AM. Hypoxia on admission was observed in 10 (20.8%) patients and oxygen therapy was provided to 8 (16.7%). One patient died due to respiratory failure. The median duration of hospitalization was 11 days. The total cost of hospitalization was 82,705,289 yen (approximately $760,000), which was covered by public funds. Patients hospitalized with COVID-19 under the Quarantine Act were younger and less severely ill than inpatients with COVID-19 from among the general population in Japan (according to a COVID-19 registry), but consumed a significant amount of medical resources at this hospital. An efficient system to manage patients with COVID-19 in designated hotels should be created and indications for hospitalization should be determined.

Risk factors for severity on admission and the disease progression during hospitalisation in a large cohort of patients with COVID-19 in Japan

OBJECTIVES

To investigate the risk factors contributing to severity on admission. Additionally, risk factors of worst severity and fatality were studied. Moreover, factors were compared based on three points: early severity, worst severity and fatality.

DESIGN

An observational cohort study using data entered in a Japan nationwide COVID-19 inpatient registry, COVIREGI-JP.

SETTING

As of 28 September 2020, 10480 cases from 802 facilities have been registered. Participating facilities cover a wide range of hospitals where patients with COVID-19 are admitted in Japan.

PARTICIPANTS

Participants who had a positive test result on any applicable SARS-CoV-2 diagnostic tests were admitted to participating healthcare facilities. A total of 3829 cases were identified from 16 January to 31 May 2020, of which 3376 cases were included in this study.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome was severe or nonsevere on admission, determined by the requirement of mechanical ventilation or oxygen therapy, SpO2 or respiratory rate. Secondary outcome was the worst severity during hospitalisation, judged by the requirement of oxygen and/orinvasive mechanical ventilation/extracorporeal membrane oxygenation.

RESULTS

Risk factors for severity on admission were older age, men, cardiovascular disease, chronic respiratory disease, diabetes, obesity and hypertension. Cerebrovascular disease, liver disease, renal disease or dialysis, solid tumour and hyperlipidaemia did not influence severity on admission; however, it influenced worst severity. Fatality rates for obesity, hypertension and hyperlipidaemia were relatively lower.

CONCLUSIONS

This study segregated the comorbidities influencing 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, hyperlipidaemia 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 NUMBER

UMIN000039873. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000045453.

Nationwide survey of the coronavirus disease 2019 prevention and treatment systems for kidney disease patients: a study of Japanese Society of Nephrology-certified educational facilities

Background

There are no reports of a large-scale survey on the infection prevention measures against coronavirus disease 2019 (COVID-19) in nephrology facilities. This study investigated the facility-level nephrology practices adopted during the COVID-19 pandemic and their associated challenges. Additionally, the treatment patterns and outcomes of chronic kidney disease (CKD) patients with COVID-19 were reviewed.

Methods

We conducted a nationwide questionnaire survey of 704 educational facilities that were certified by the Japanese Society of Nephrology (JSN) from October 20, 2020 to November 16, 2020. The questionnaire reviewed the facility characteristics, infection prevention measures taken during routine nephrology practice, impact of COVID-19 on nephrology practice, experiences in managing CKD patients with COVID-19, and nosocomial transmission in the nephrology unit.

Results

Of the 347 facilities that responded, 95.1% checked outpatients’ body temperatures and COVID-19 symptoms at their visits. To reduce face-to-face contact, 80% and 70% of the facilities lengthened the intervals between outpatient visits and introduced online/telephonic consultations, respectively. As a result, more than half of the hospitals experienced a decrease in the numbers of outpatients and inpatients (64% and 50%, respectively). During the study period, 347 facilities managed 479 CKD patients with COVID-19. Oxygen administration and mechanical ventilation were performed for 47.8% and 16.5% of the patients, respectively, with a 9.2% total mortality rate.

Conclusion

This survey demonstrated that JSN-certified educational nephrology facilities adopted multiple measures to manage the COVID-19 pandemic; however, they faced several challenges. Sharing these experiences could standardize these approaches and prepare us better for the future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10157-021-02082-z.

COVID-19 Infection during Pregnancy: Risk of Vertical Transmission, Fetal, and Neonatal Outcomes

The COVID-19 pandemic is a worldwide, critical public health challenge and is considered one of the most communicable diseases that the world had faced so far. Response and symptoms associated with COVID-19 vary between the different cases recorded, but it is amply described that symptoms become more aggressive in subjects with a weaker immune system. This includes older subjects, patients with chronic diseases, patients with immunosuppression treatment, and pregnant women. Pregnant women are receiving more attention not only because of their altered physiological and immunological function but also for the potential risk of viral vertical transmission to the fetus or infant. However, very limited data about the impact of maternal infection during pregnancy, such as the possibility of vertical transmission in utero, during birth, or via breastfeeding, is available. Moreover, the impact of infection on the newborn in the short and long term remains poorly understood. Therefore, it is vital to collect and analyze data from pregnant women infected with COVID-19 to understand the viral pathophysiology during pregnancy and its effects on the offspring. In this article, we review the current knowledge about pre-and post-natal COVID-19 infection, and we discuss whether vertical transmission takes place in pregnant women infected with the virus and what are the current recommendations that pregnant women should follow in order to be protected from the virus.

Clinical features and outcomes of COVID-19 in older adults: a systematic review and meta-analysis

Background

Few studies have focused on exploring the clinical characteristics and outcomes of COVID-19 in older patients. We conducted this systematic review and meta-analysis to have a better understanding of the clinical characteristics of older COVID-19 patients.

Methods

A systematic search of PubMed and Scopus was performed from December 2019 to May 3rd, 2020. Observational studies including older adults (age ≥ 60 years) with COVID-19 infection and reporting clinical characteristics or outcome were included. Primary outcome was assessing weighted pooled prevalence (WPP) of severity and outcomes. Secondary outcomes were clinical features including comorbidities and need of respiratory support.

Result

Forty-six studies with 13,624 older patients were included. Severe infection was seen in 51% (95% CI– 36-65%, I²–95%) patients while 22% (95% CI– 16-28%, I²–88%) were critically ill. Overall, 11% (95% CI– 5-21%, I²–98%) patients died. The common comorbidities were hypertension (48, 95% CI– 36-60% I²–92%), diabetes mellitus (22, 95% CI– 13-32%, I²–86%) and cardiovascular disease (19, 95% CI – 11-28%, I²–85%). Common symptoms were fever (83, 95% CI– 66-97%, I²–91%), cough (60, 95% CI– 50-70%, I²–71%) and dyspnoea (42, 95% CI– 19-67%, I²–94%). Overall, 84% (95% CI– 60-100%, I²–81%) required oxygen support and 21% (95% CI– 0-49%, I²–91%) required mechanical ventilation. Majority of studies had medium to high risk of bias and overall quality of evidence was low for all outcomes.

Conclusion

Approximately half of older patients with COVID-19 have severe infection, one in five are critically ill and one in ten die. More high-quality evidence is needed to study outcomes in this vulnerable patient population and factors affecting these outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02261-3.

Characteristics of patients with novel coronavirus disease (COVID-19) during the first surge versus the second surge of infections in Osaka Prefecture, Japan

In Japan, the differences in characteristics, severity, and mortality of novel coronavirus disease (COVID-19) patients between the first and second surges of infections have not been fully understood. This study is a retrospective cohort study of COVID-19 patients confirmed between February 1 and August 31, 2020 in Osaka Prefecture, Japan. Publicly available information on patients was collected from the website of Osaka Prefecture. Patients were divided into two groups according to the date of the positive laboratory test result: the first surge (February 1 to May 22) and the second surge (May 23 to August 31). Patients' characteristics were compared between the two groups. A multivariable Cox proportional-hazards model was applied to compare severity and mortality between the two groups, where sex, age group at the onset date, city of residence, and days to test positive were adjusted. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. A total of 8,541 patients included 1,780 and 6,761 patients in the first and second surges, respectively. Age at the onset date was younger in the second surge (p < 0.001), and median of days from the onset date to the positive test date shortened from 7 to 6 days (p < 0.001). The multivariable Cox proportional-hazards model revealed that both severity and mortality were lower in the second surge than in the first surge (severity: HR: 0.51 [0.39-0.67]; mortality: HR: 0.37 [0.25-0.56]). In conclusion, severity and mortality were lower in the second surge than in the first surge among COVID-19 patients in Osaka Prefecture, Japan.

Japanese WHO Collaborating Centres (WHO CCs) fight against COVID-19

WHO Regional Office for the Western Pacific (WPRO) organized an online meeting connecting WHO Collaborating Centres (WHO CCs) in the region on 25 August 2020, to share experiences and promote networking on COVID-19 response. The meeting shared regional update on situation and responses, and COVID-19 related experiences of selected WHO CCs, followed by discussions on opportunities for enhancing collaboration between WPRO and WHO CCs. Priorities of WPROs support to countries included a health systems approach rather than single intervention. On behalf of WHO CCs in Japan, the National Center for Global Health and Medicine (NCGM) delivered a presentation on the results of a survey about COVID-19 related activities of these WHO CCs. These activities were categorized into collaboration with WHO, research and development, public health responses, and clinical services. Collaboration with WHO included sending consultants through the scheme of GOARN, strengthening of COVID-19 testing, and contribution to development of WPRO guidelines. Research and development involved establishment of a nationwide registry of COVID-19 clinical data. Following the meeting, NCGM further enhanced its activities as WHO CC. Since WHO CCs in the country have a wide range of expertise that could contribute to health system strengthening, it is worthwhile for the WHO CCs to consider amending existing work plans for supporting countries in the region to incorporate a health systems approach as part of COVID-19 response strategies.

First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain

Many countries have seen a two-wave pattern in reported cases of coronavirus disease-19 during the 2020 pandemic, with a first wave during spring followed by the current second wave in late summer and autumn. Empirical data show that the characteristics of the effects of the virus do vary between the two periods. Differences in age range and severity of the disease have been reported, although the comparative characteristics of the two waves still remain largely unknown. Those characteristics are compared in this study using data from two equal periods of 3 and a half months. The first period, between 15th March and 30th June, corresponding to the entire first wave, and the second, between 1st July and 15th October, corresponding to part of the second wave, still present at the time of writing this article. Two hundred and four patients were hospitalized during the first period, and 264 during the second period. Patients in the second wave were younger and the duration of hospitalization and case fatality rate were lower than those in the first wave. In the second wave, there were more children, and pregnant and post-partum women. The most frequent signs and symptoms in both waves were fever, dyspnea, pneumonia, and cough, and the most relevant comorbidities were cardiovascular diseases, type 2 diabetes mellitus, and chronic neurological diseases. Patients from the second wave more frequently presented renal and gastrointestinal symptoms, were more often treated with non-invasive mechanical ventilation and corticoids, and less often with invasive mechanical ventilation, conventional oxygen therapy and anticoagulants. Several differences in mortality risk factors were also observed. These results might help to understand the characteristics of the second wave and the behaviour and danger of SARS-CoV-2 in the Mediterranean area and in Western Europe. Further studies are needed to confirm our findings.

First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain

Many countries have seen a two-wave pattern in reported cases of coronavirus disease-19 during the 2020 pandemic, with a first wave during spring followed by the current second wave in late summer and autumn. Empirical data show that the characteristics of the effects of the virus do vary between the two periods. Differences in age range and severity of the disease have been reported, although the comparative characteristics of the two waves still remain largely unknown. Those characteristics are compared in this study using data from two equal periods of 3 and a half months. The first period, between 15^th March and 30^th June, corresponding to the entire first wave, and the second, between 1^st July and 15^th October, corresponding to part of the second wave, still present at the time of writing this article. Two hundred and four patients were hospitalized during the first period, and 264 during the second period. Patients in the second wave were younger and the duration of hospitalization and case fatality rate were lower than those in the first wave. In the second wave, there were more children, and pregnant and post-partum women. The most frequent signs and symptoms in both waves were fever, dyspnea, pneumonia, and cough, and the most relevant comorbidities were cardiovascular diseases, type 2 diabetes mellitus, and chronic neurological diseases. Patients from the second wave more frequently presented renal and gastrointestinal symptoms, were more often treated with non-invasive mechanical ventilation and corticoids, and less often with invasive mechanical ventilation, conventional oxygen therapy and anticoagulants. Several differences in mortality risk factors were also observed. These results might help to understand the characteristics of the second wave and the behaviour and danger of SARS-CoV-2 in the Mediterranean area and in Western Europe. Further studies are needed to confirm our findings.

Declining Intensive Care Unit Mortality of COVID-19: A Multi-Center Study

Background

Coronavirus disease 2019 (COVID-19) mortality has waned significantly over time; however, factors contributing towards this reduction largely remain unidentified. The purpose of this study was to evaluate the trend in mortality at our large tertiary academic health system and factors contributing to this trend.

Methods

This is a retrospective cohort study of intensive care unit (ICU) patients diagnosed with COVID-19 between March and August 2020 admitted across 14 hospitals in the Philadelphia area. Collected data included demographics, comorbidities, admission risk of mortality score, laboratory values, medical interventions, survival outcomes, hospital and ICU length of stay (LOS) and discharge disposition. Chi-square (χ²) test, Fisher exact test, Cochran-Mantel-Haenszel method, multinomial logistic regression models, independent sample t-test, Mann-Whitney U test and one-way analysis of variance (ANOVA) were used.

Results

A total of 1,204 patients were included. Overall mortality was 39%. Mortality declined significantly from 46% in March to 14% in August 2020 (P < 0.05). The most common underlying comorbidities were hypertension (60.2%), diabetes mellitus (44.7%), dyslipidemia (31.6%) and congestive heart failure (14.7%). Hydroxychloroquine (HCQ) use was more commonly associated with the patients who died, while the use of remdesivir, tocilizumab, steroids and duration of these medications were not significantly different. Peak values of ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP) and D-dimer levels were significantly higher in patients who died (P < 0.05). The mean hospital LOS was significantly longer in the patients who survived compared to the patients who died (18 vs. 12, P < 0.05).

Conclusions

The mortality of patients admitted to our ICU system significantly decreased over time. Factors that may have contributed to this may be the result of a better understanding of COVID-19 pathophysiology and treatments. Further research is needed to elucidate the factors contributing to a reduction in the mortality rate for this patient population.

Prediction of COVID-19 mortality among hospitalized patients in Sudan.. [DOI: 10.1101/2021.03.09.21253179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

COVID-19 was primarily reported in China. The mortality rate across countries had ranged from 1% up to more than 10% and it is underestimated in some countries. Advanced age is the most frequently reported factor associated to mortality. Other factors were the presence of comorbidities such as diabetes mellitus, hypertension and obesity. Several models for mortality prediction had been developed to assist in improving the prognosis. The aim of our study was to assess the factors related to mortality among COVID-19 patients and develop a prediction model based on these factors.

Methods

A retrospective cohort study assessed the factors related to the mortality among COVID-19 patients who attended Imperial Hospital isolation centre on November-December, 2020, Khartoum, Sudan. Statistical tests performed were chi-square test, odds ratio and regression to develop the prediction model. Tests were considered statistically significant whenp< 0.05.

Results

105 patients were studied. 29% of the patients were deceased, while, 71% were discharged alive. A statistically significant association was found between the age and severity with regards to mortality rate (p=0.034, 0.018 respectively). The model equation for mortality prediction: Mortality = −14.724+ (1.387* Age) + (−0.323* Gender) + (1.814* Admission) + (0.193* Ischemic Heart Disease) + (−0.369* Fever) + (1.595* Cough) + (1.953* Complications) + (0.149* Duration of hospitalization) + (0.999* Enoxaparin dose).

Conclusions

Age, admission ward, cough and enoxaparin dose were statistically significant predictors for COVID-19 mortality (p= 0.014, 0.011, 0.015, 0.006 respectively).

Identifying Clinical Phenotypes in Moderate to Severe Acute Respiratory Distress Syndrome Related to COVID-19: The COVADIS Study

Objectives: Different phenotypes have been identified in acute respiratory distress syndrome (ARDS). Existence of several phenotypes in coronavirus disease (COVID-19) related acute respiratory distress syndrome is unknown. We sought to identify different phenotypes of patients with moderate to severe ARDS related to COVID-19. Methods: We conducted an observational study of 416 COVID-19 patients with moderate to severe ARDS at 21 intensive care units in Belgium and France. The primary outcome was day-28 ventilatory free days. Secondary outcomes were mortality on day 28, acute kidney injury, acute cardiac injury, pulmonary embolism, and deep venous thrombosis. Multiple factor analysis and hierarchical classification on principal components were performed to distinguish different clinical phenotypes. Results: We identified three different phenotypes in 150, 176, and 90 patients, respectively. Phenotype 3 was characterized by short evolution, severe hypoxemia, and old comorbid patients. Phenotype 1 was mainly characterized by the absence of comorbidities, relatively high compliance, and long duration of symptoms, whereas phenotype 2 was characterized female sex, and the presence of mild comorbidities such as uncomplicated diabetes or chronic hypertension. The compliance in phenotype 2 was lower than that in phenotype 1, with higher plateau and driving pressure. Phenotype 3 was associated with higher mortality compared to phenotypes 1 and 2. Conclusions: In COVID-19 patients with moderate to severe ARDS, we identified three clinical phenotypes. One of these included older people with comorbidities who had a fulminant course of disease with poor prognosis. Requirement of different treatments and ventilatory strategies for each phenotype needs further investigation.

Characteristics and outcomes in hospitalized COVID-19 patients during the first 28 days of the spring and autumn pandemic waves in Milan: An observational prospective study

BACKGROUND

The therapeutic approach to COVID-19 and healthcare system preparedness improved during 2020. We compared characteristics and outcomes of hospitalized COVID-19 patients during the first 28 days of the March and October pandemic waves in Milan, Italy.

MATERIAL AND METHODS

A prospective, observational study enrolling adult patients hospitalized with COVID-19 pneumonia during March 7-April 4 (1st period) and October 15-November 12 (2nd period). During the 1st period hydroxychloroquine, lopinavir/ritonavir and therapeutic enoxaparin when thrombosis was confirmed were administered; systemic corticosteroids were given in case of severe pneumonia. During the 2nd period dexamethasone, methylprednisolone, remdesivir, thromboprophylaxis or anticoagulation were administered according to international recommendations. Patients with respiratory distress on oxygen masks initiated CPAP. Outcomes were: length of hospital stay, all-cause in-hospital mortality and need for intubation.

RESULTS

We included 70 patients (75% males) during the 1st and 76 patients (51% males, p = 0.522) during the 2nd period. Prevalence of severe respiratory failure (30% vs. 12%, p = 0.006), and D-dimer >3000 FEU (34% vs. 15%, P = 0.012) were reduced during the 2nd period, while anticoagulation and corticosteroids were more frequently administered (both p < 0.01). Mortality and time to referral were also reduced (39.4% vs. 22.4%, p = 0.019 and 6 vs. 5 days, p = 0.014), while need for intubation didn't change. Hospitalization length was comparable, but the proportion of patients discharged home was higher during the 2nd period (28.2% vs. 55.4%, p = 0.001).

CONCLUSIONS

Changing treatment paradigms and early referral might have reduced mortality in COVID-19 patients. The effects of specific therapeutic regimens needs further confirmation in future clinical studies.

Associating the Change in New COVID-19 Cases to GDP per Capita in 38 European Countries in the First Wave of the Pandemic

COVID-19 has affected all countries globally. We explore associations between the change in new COVID-19 registered cases per million population and various macroeconomic and well-being indicators in 38 European countries over a 2-month period (1st April-31st May 2020). A statistically significant (p = 0.002) negative association was estimated between the change in new COVID-19 cases and GDP per capita, after controlling for key health determinants including public expenditure on health, life expectancy, smoking tobacco and sanitation. The country with the highest GDP per capita in Europe (i.e., Luxemburg) was found to experience the lowest change in new COVID-19 cases within the time period whilst the opposite was found for countries with lower GDP per capita (i.e., Ukraine, Bulgaria, and Romania). The outcomes of this study indicate that, in the first wave of the pandemic in Europe, a country's GDP per capita might be associated with a lower rate of new COVID-19 cases. The study concludes by suggesting that in European regions a country's economic performance should be a critical health priority for policy makers.

Standard and Anomalous Waves of COVID-19: A Multiple-Wave Growth Model for Epidemics

We apply a generalized logistic growth model, with time-dependent parameters, to describe the fatality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. In the case of two waves only, the model parameters vary as a function of time according to a logistic function, whose two extreme values, i.e., for early and late times, characterize the first and second waves, respectively. For the multiple-wave model, the time-dependency of the parameters is likewise described by a multi-step logistic function with N intermediate plateaus, representing the N waves of the epidemic. We show that the theoretical curves are in excellent agreement with the empirical data for all countries considered here, namely: Brazil, Canada, Germany, Iran, Italy, Japan, Mexico, South Africa, Sweden, and the USA. The model also allows for predictions about the time of occurrence and severity of the subsequent waves. It is shown furthermore that the subsequent waves of COVID-19 can be generically classified into two main types, namely, standard and anomalous waves, according as to whether a given wave starts well after or well before the preceding one has subsided, respectively.