Underlying conditions and risk of hospitalisation, ICU admission and mortality among those with COVID-19 in Ireland: A national surveillance study

Demographics, comorbidities and risk factors for severe disease from the early SARS-CoV-2 infection cases in Queensland, Australia

BACKGROUND

Demographics and comorbidities associated with coronavirus disease 2019 (COVID-19) severity differs between subpopulations and should be determined to aid future pandemic planning and preparedness.

AIM

To describe the demographics and comorbidities of patients diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Queensland (QLD), Australia, between January 2020 and May 2021. Also, to determine the relationship between these characteristics and disease severity based on the highest level of care.

METHODS

A retrospective case series analysis was conducted using data obtained from the Notifiable Conditions System. Data on patients confirmed with SARS-CoV-2 infection in QLD were included in this analysis. Descriptive statistics and logistic regression modelling were used to analyse factors that contributed to disease severity.

RESULTS

One thousand six hundred twenty-five patients with SARS-CoV-2 infection were diagnosed in the study period and analysed. The median age was 41 years and 54.3% (n = 882) were males. A total of 550 patients were hospitalised and 20 patients were admitted to the intensive care unit (ICU). In those admitted to the ICU, 95% (n = 19) were older than 45 years and 95% (n = 19) were male. Comorbidities significantly associated with hospitalisation were chronic cardiac disease (excluding hypertension) and diabetes, and for ICU admission were morbid obesity, chronic respiratory disease and chronic cardiac disease. No demographic factors were shown to be significantly associated with disease severity.

CONCLUSIONS

Comorbidities associated with the highest level of COVID-19 disease severity were morbid obesity, chronic respiratory disease and cardiac disease. These data can assist with identifying high-risk patients susceptible to severe COVID-19 and can be used to facilitate preparations for future pandemics.

Factors associated with SARS-COV-2 positive test in Lifelines

BACKGROUND

Severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) can affect anyone, however, it is often mixed with other respiratory diseases. This study aimed to identify the factors associated with SARS-COV-2 positive test.

METHODS

Participants from the Northern Netherlands representative of the general population were included if filled in the questionnaire about well-being between June 2020-April 2021 and were tested for SARS-COV-2. The outcome was a self-reported test as measured by polymerase chain reaction. The data were collected on age, sex, household, smoking, alcohol use, physical activity, quality of life, fatigue, symptoms and medications use. Participants were matched on sex, age and the timing of their SARS-COV-2 tests maintaining a 1:4 ratio and classified into those with a positive and negative SARS-COV-2 using logistic regression. The performance of the model was compared with other machine-learning algorithms by the area under the receiving operating curve.

RESULTS

2564 (20%) of 12786 participants had a positive SARS-COV-2 test. The factors associated with a higher risk of SARS-COV-2 positive test in multivariate logistic regression were: contact with someone tested positive for SARS-COV-2, ≥1 household members, typical SARS-COV-2 symptoms, male gender and fatigue. The factors associated with a lower risk of SARS-COV-2 positive test were higher quality of life, inhaler use, runny nose, lower back pain, diarrhea, pain when breathing, sore throat, pain in neck, shoulder or arm, numbness or tingling, and stomach pain. The performance of the logistic models was comparable with that of random forest, support vector machine and gradient boosting machine.

CONCLUSIONS

Having a contact with someone tested positive for SARS-COV-2 and living in a household with someone else are the most important factors related to a positive SARS-COV-2 test. The loss of smell or taste is the most prominent symptom associated with a positive test. Symptoms like runny nose, pain when breathing, sore throat are more likely to be indicative of other conditions.

Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study

China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.

Obesity as an independent risk factor for COVID-19 severity and mortality

BACKGROUND

Since December 2019, the world has struggled with the COVID-19 pandemic. Even after the introduction of various vaccines, this disease still takes a considerable toll. In order to improve the optimal allocation of resources and communication of prognosis, healthcare providers and patients need an accurate understanding of factors (such as obesity) that are associated with a higher risk of adverse outcomes from the COVID-19 infection.

OBJECTIVES

To evaluate obesity as an independent prognostic factor for COVID-19 severity and mortality among adult patients in whom infection with the COVID-19 virus is confirmed.

SEARCH METHODS

MEDLINE, Embase, two COVID-19 reference collections, and four Chinese biomedical databases were searched up to April 2021.

SELECTION CRITERIA

We included case-control, case-series, prospective and retrospective cohort studies, and secondary analyses of randomised controlled trials if they evaluated associations between obesity and COVID-19 adverse outcomes including mortality, mechanical ventilation, intensive care unit (ICU) admission, hospitalisation, severe COVID, and COVID pneumonia. Given our interest in ascertaining the independent association between obesity and these outcomes, we selected studies that adjusted for at least one factor other than obesity. Studies were evaluated for inclusion by two independent reviewers working in duplicate.  DATA COLLECTION AND ANALYSIS: Using standardised data extraction forms, we extracted relevant information from the included studies. When appropriate, we pooled the estimates of association across studies with the use of random-effects meta-analyses. The Quality in Prognostic Studies (QUIPS) tool provided the platform for assessing the risk of bias across each included study. In our main comparison, we conducted meta-analyses for each obesity class separately. We also meta-analysed unclassified obesity and obesity as a continuous variable (5 kg/m2 increase in BMI (body mass index)). We used the GRADE framework to rate our certainty in the importance of the association observed between obesity and each outcome. As obesity is closely associated with other comorbidities, we decided to prespecify the minimum adjustment set of variables including age, sex, diabetes, hypertension, and cardiovascular disease for subgroup analysis.  MAIN RESULTS: We identified 171 studies, 149 of which were included in meta-analyses.  As compared to 'normal' BMI (18.5 to 24.9 kg/m2) or patients without obesity, those with obesity classes I (BMI 30 to 35 kg/m2), and II (BMI 35 to 40 kg/m2) were not at increased odds for mortality (Class I: odds ratio [OR] 1.04, 95% confidence interval [CI] 0.94 to 1.16, high certainty (15 studies, 335,209 participants); Class II: OR 1.16, 95% CI 0.99 to 1.36, high certainty (11 studies, 317,925 participants)). However, those with class III obesity (BMI 40 kg/m2 and above) may be at increased odds for mortality (Class III: OR 1.67, 95% CI 1.39 to 2.00, low certainty, (19 studies, 354,967 participants)) compared to normal BMI or patients without obesity. For mechanical ventilation, we observed increasing odds with higher classes of obesity in comparison to normal BMI or patients without obesity (class I: OR 1.38, 95% CI 1.20 to 1.59, 10 studies, 187,895 participants, moderate certainty; class II: OR 1.67, 95% CI 1.42 to 1.96, 6 studies, 171,149 participants, high certainty; class III: OR 2.17, 95% CI 1.59 to 2.97, 12 studies, 174,520 participants, high certainty). However, we did not observe a dose-response relationship across increasing obesity classifications for ICU admission and hospitalisation.

AUTHORS' CONCLUSIONS

Our findings suggest that obesity is an important independent prognostic factor in the setting of COVID-19. Consideration of obesity may inform the optimal management and allocation of limited resources in the care of COVID-19 patients.

Cohort profile: a longitudinal regional cohort study to assess COVID-19 seroprevalence in blood donors - baseline characteristics of the SeMaCo study participants

PURPOSE

The SeMaCo study (Serologische Untersuchungen bei Blutspendern des Großraums Magdeburg auf Antikörper gegen SARS-CoV-2), a prospective, longitudinal cohort study with four survey phases spanning 3-5 months each over a period of 22 months, extends the spectrum of seroepidemiological studies in Germany. We present here a careful characterisation of the initial survey phase of the cohort to provide baseline data on infection incidence and obtained from questionnaires, focussing in particular on the attitude towards COVID-19 vaccinations, the vaccination success and the vaccination acceptance.

PARTICIPANTS

A total of 2195 individual blood donors from the donor pool of the blood donation service of the University Hospital Magdeburg were enrolled in the initial survey phase from 20 January 2021 to 30 April 2021. 2138 participants gave sociodemographic/contact data (51.7% male, mean age 44 years) and 2082 participants answered the vaccination questionnaire.

FINDINGS TO DATE

Out of 2195 participants with antibody results, 1909 (87.0%) were antibody negative. The remaining 286 subjects (13.0%) were either antibody-positive and vaccinated (160/286; 55.9%) or antibody-positive without vaccination information (17/286; 5.9%) or antibody-positive and unvaccinated (109/286; 38.1%). The latter result reflects the rate of true or highly probable SARS-CoV-2 infections in our initial study cohort.

FUTURE PLANS

The study primarily aims to measure the prevalence and long-term kinetics of IgG-antibodies against SARS-CoV-2. Including the baseline, the study foresees four survey periods of 3-4 months each. At each visit, we will assess the blood donors' attitude towards vaccination, the antibody response following vaccination and/or infection, as well as undesired vaccination effects. We aim to test the same participants during the survey periods by repeated invitations for blood donation to ensure a long-term (follow-up) in as many study participants as possible. After the four survey phases, a longitudinal data set will be created that reflects the course of the antibody levels/frequencies as well as the infection and vaccination incidence.

TRIAL REGISTRATION NUMBER

DRKS00023263.

Disparities in Underlying Health Conditions and COVID-19 Infection and Mortality in Louisiana, USA

BACKGROUND

Louisiana is ranked among the top 10 states with the highest COVID-19 death rate in the USA, and African Americans (AA) that account 32.2% (1.5 million) of the state's population have been impacted differentially with higher rates of chronic health conditions such as hypertension, obesity, and diabetes. These conditions can compromise immune systems and increase susceptibility to COVID-19. Prior health disparity and COVID-19 studies in Louisiana are limited to comprehensively evaluate the risk of underlying health conditions on COVID-19 incidence and death in minority communities and thus the study aims to address this research gap.

METHODS

Negative binomial regression analyses were used to correlate risk factors with COVID-19 incidence and death rates using SAS software. Spatial distribution and burden of COVID-19 incidence and mortality rates were mapped using ArcGIS Pro.

RESULTS

We found that AA COVID-19 death was three times higher than other races, and mortality rate was ten times higher in counties with more than 40% AA. Highest AA case and death counts were found in Orleans County; mortality rate in Bienville; and incidence rate in East Feliciana. Hypertension, diabetes, and obesity were significantly correlated with both COVID-19 incidence and mortality rates in AA. Greater odds of incidence and death rates also found in counties with higher AA population density with higher burden of underlying health conditions. Furthermore, living in poverty, being 65 years and older significantly influenced COVID-19 cases and deaths in the state.

CONCLUSIONS

The study highlights the need to reduce the burden of health disparities in underserved communities, and help to inform the public, scientific communities, and policy makers to plan effective responses to reduce the risks of COVID-19 infection, death, and other potential infectious diseases at the state.

A cross-sectional follow-up study of physical activity in adults with moderate and severe haemophilia

AIM

To conduct a cross-sectional follow-up assessment of physical activity (PA) in people with moderate and severe haemophilia (PwMSH) from the Irish Personalised Approach to the Treatment of Haemophilia (iPATH) study.

METHODS

Between June-December 2021, participants' PA was measured over one week using accelerometery, and was compared with their previously measured data from the original iPATH assessment. Self-awareness of PA and the impact of the Covid-19 pandemic on PA, pain, mobility and function were retrospectively examined using a survey.

RESULTS

Of 30 participants who returned surveys [n = 19, severe (FVIII, <.01 IU/mL); n = 4, moderate (FVIII, .01-.05 IU/mL); n = 7, severe (FIX, <.01 IU/mL); age: 47 (36, 55) years], 28 completed accelerometery (follow-up time: 3 years). There were no significant differences in accelerometer PA (all p > .05), but achievement of World Health Organisation guidelines increased (67.9%-75.0%; p = .646). Increased self-awareness of PA was reported by 76.7%, and 66.7% reported desires to become more physically active. Compared to normal, most reported either no differences or lower levels of PA during lockdown restrictions. Self-reported PA increased for most when restrictions eased from April 2021 onwards. Beyond the pandemic, concerns included pain and access to exercise resources.

CONCLUSION

Self-reported PA throughout the pandemic was variable, whilst there were no significant differences in objectively measured PA between assessment periods, despite reports of increased self-awareness and desires to be physically active at follow-up. Further qualitative research is needed to design personalised PA and health interventions, capturing perspectives of patients, their families, and multi-disciplinary haemophilia healthcare providers.

Comparison of COVID-19 Severity and Mortality Rates in the First Four Epidemic Waves in Hungary in a Single-Center Study with Special Regard to Critically Ill Patients in an Intensive Care Unit

Different variants of coronavirus 2 (SARS-CoV-2), a virus responsible for severe acute respiratory syndrome, caused several epidemic surges in Hungary. The severity of these surges varied due to the different virulences of the variants. In a single-center, retrospective, observational study, we aimed to assess and compare morbidities and mortality rates across the epidemic waves I to IV with special regard to hospitalized, critically ill patients. A significant difference was found between the surges with regard to morbidity (p < 0.001) and ICU mortality (p = 0.002), while in-hospital mortality rates (p = 0.503) did not differ significantly. Patients under invasive ventilation had a higher incidence of bloodstream infection (aOR: 8.91 [4.43–17.95] p < 0.001), which significantly increased mortality (OR: 3.32 [2.01–5.48]; p < 0.001). Our results suggest that Waves III and IV, caused by the alpha (B.1.1.7) and delta (B.1.617.2) variants, respectively, were more severe in terms of morbidity. The incidence of bloodstream infection was high in critically ill patients. Our results suggest that clinicians should be aware of the risk of bloodstream infection in critically ill ICU patients, especially when invasive ventilation is used.

Associations between chronic conditions and death in hospital among adults (aged 20+ years) during first acute care hospitalizations with a confirmed or suspected COVID-19 diagnosis in Canada

PURPOSE

We aimed to quantify life course-specific associations between death in hospital and 30 chronic conditions, and comorbidity among them, in adults (aged 20+ years) during their first acute care hospitalization with a confirmed or suspected COVID-19 diagnosis in Canada.

METHODS

We identified 35,519 first acute care hospitalizations with a confirmed or suspected COVID-19 diagnosis in the Discharge Abstract Database as of March 31, 2021. For each of five life-course age groups (20-34, 35-49, 50-64, 65-79, and 80+ years), we used multivariable logistic regression to examine associations between death in hospital and 30 chronic conditions, comorbidity, period of admission, and pregnant status, after adjusting for sex and age.

RESULTS

About 20.9% of hospitalized patients with COVID-19 died in hospital. Conditions most strongly associated with in-hospital death varied across the life course. Chronic liver disease, other nervous system disorders, and obesity were statistically significantly associated (α = 0.05) with in-hospital death in the 20-34 to 65-79 year age groups, but the magnitude of the associations decreased as age increased. Stroke (aOR = 5.24, 95% CI: 2.63, 9.83) and other inflammatory rheumatic diseases (aOR = 4.37, 95% CI: 1.64, 10.26) were significantly associated with in-hospital death among 35 to 49 year olds only. Among 50+ year olds, more chronic conditions were significantly associated with in-hospital death, but the magnitude of the associations were generally weaker except for Down syndrome in the 50 to 64 (aOR = 8.49, 95% CI: 4.28, 16.28) and 65 to 79 year age groups (aOR = 5.19, 95% CI: 1.44, 20.91). Associations between comorbidity and death also attenuated with age. Among 20 to 34 year olds, the likelihood of death was 19 times greater (aOR = 18.69, 95% CI: 7.69, 48.24) in patients with three or more conditions compared to patients with none of the conditions, while for 80+ year olds the likelihood of death was two times greater (aOR = 2.04, 95% CI: 1.70, 2.45) for patients with six or more conditions compared to patients with none of the conditions.

CONCLUSION

Conditions most strongly associated with in-hospital death among hospitalized adults with COVID-19 vary across the life course, and the impact of chronic conditions and comorbidity attenuate with age.

COVID-19 ethnic inequalities in mental health and multimorbidities: protocol for the COVEIMM study

PURPOSE

The COVID-19 pandemic may have exacerbated ethnic health inequalities, particularly in people with multiple long-term health conditions, the interplay with mental health is unclear. This study investigates the impact of the pandemic on the association of ethnicity and multimorbidity with mortality/service use among adults, in people living with severe mental illnesses (SMI).

METHODS

This study will utilise secondary mental healthcare records via the Clinical Record Interactive Search (CRIS) and nationally representative primary care records through the Clinical Practice Interactive Research Database (CPRD). Quasi-experimental designs will be employed to quantify the impact of COVID-19 on mental health service use and excess mortality by ethnicity, in people living with severe mental health conditions. Up to 50 qualitative interviews will also be conducted, co-produced with peer researchers; findings will be synthesised with quantitative insights to provide in-depth understanding of observed associations.

RESULTS

81,483 people in CRIS with schizophrenia spectrum, bipolar or affective disorder diagnoses, were alive from 1st January 2019. Psychiatric multimorbidities in the CRIS sample were comorbid somatoform disorders (30%), substance use disorders (14%) and personality disorders (12%). In CPRD, of 678,842 individuals with a prior probable diagnosis of COVID-19, 1.1% (N = 7493) had an SMI diagnosis. People in the SMI group were more likely to die (9% versus 2% in the non-SMI sample) and were more likely to have mental and physical multimorbidities.

CONCLUSION

The effect of COVID-19 on people from minority ethnic backgrounds with SMI and multimorbidities remains under-studied. The present mixed methods study aims to address this gap.

Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record

Background

As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient.

Objective

This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms.

Methods

The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups (i.e., 4-group classification) and using two groups after regrouping the four severity level into two (i.e., binary classification). Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE.

Results

Of the four maximum severity groups, the moderate group had the highest percentage (1,115 patients; 49.5%). As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (http://statgen.snu.ac.kr/software/nomogramDaeguCovid/).

Conclusions

We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field.

Outcomes following SARS-CoV-2 infection in patients with primary and secondary immunodeficiency in the UK

In March 2020, the United Kingdom Primary Immunodeficiency Network (UKPIN) established a registry of cases to collate the outcomes of individuals with PID and SID following SARS-CoV-2 infection and treatment. A total of 310 cases of SARS-CoV-2 infection in individuals with PID or SID have now been reported in the UK. The overall mortality within the cohort was 17.7% (n = 55/310). Individuals with CVID demonstrated an infection fatality rate (IFR) of 18.3% (n = 17/93), individuals with PID receiving IgRT had an IFR of 16.3% (n = 26/159) and individuals with SID, an IFR of 27.2% (n = 25/92). Individuals with PID and SID had higher inpatient mortality and died at a younger age than the general population. Increasing age, low pre-SARS-CoV-2 infection lymphocyte count and the presence of common co-morbidities increased the risk of mortality in PID. Access to specific COVID-19 treatments in this cohort was limited: only 22.9% (n = 33/144) of patients admitted to the hospital received dexamethasone, remdesivir, an anti-SARS-CoV-2 antibody-based therapeutic (e.g. REGN-COV2 or convalescent plasma) or tocilizumab as a monotherapy or in combination. Dexamethasone, remdesivir, and anti-SARS-CoV-2 antibody-based therapeutics appeared efficacious in PID and SID. Compared to the general population, individuals with PID or SID are at high risk of mortality following SARS-CoV-2 infection. Increasing age, low baseline lymphocyte count, and the presence of co-morbidities are additional risk factors for poor outcome in this cohort.

The effects of systemic immunomodulatory treatments on COVID-19 outcomes in patients with atopic dermatitis: Results from the global SECURE-AD registry

BACKGROUND

Limited data are available on the effects of systemic immunomodulatory treatments on COVID-19 outcomes in patients with atopic dermatitis (AD).

OBJECTIVE

To investigate COVID-19 outcomes in patients with AD treated with or without systemic immunomodulatory treatments, using a global registry platform.

METHODS

Clinicians were encouraged to report cases of COVID-19 in their patients with AD in the Surveillance Epidemiology of Coronavirus Under Research Exclusion for Atopic Dermatitis (SECURE-AD) registry. Data entered from 1 April 2020 to 31 October 2021 were analysed using multivariable logistic regression. The primary outcome was hospitalization from COVID-19, according to AD treatment groups.

RESULTS

442 AD patients (mean age 35.9 years, 51.8% male) from 27 countries with strongly suspected or confirmed COVID-19 were included in analyses. 428 (96.8%) patients were treated with a single systemic therapy (n = 297 [67.2%]) or topical therapy only (n = 131 [29.6%]). Most patients treated with systemic therapies received dupilumab (n = 216). Fourteen patients (3.2%) received a combination of systemic therapies. Twenty-six patients (5.9%) were hospitalized. No deaths were reported. Patients treated with topical treatments had significantly higher odds of hospitalization, compared with those treated with dupilumab monotherapy (odds ratio (OR) 4.65 [95%CI 1.71-14.78]), including after adjustment for confounding variables (adjusted OR (aOR) 4.99 [95%CI 1.4-20.84]). Combination systemic therapy which did not include systemic corticosteroids was associated with increased odds of hospitalization, compared with single agent non-steroidal immunosuppressive systemic treatment (OR 8.09 [95%CI 0.4-59.96], aOR 37.57 [95%CI 1.05-871.11]). Hospitalization was most likely in patients treated with combination systemic therapy which included systemic corticosteroids (OR 40.43 [95%CI 8.16-207.49], aOR 45.75 [95%CI 4.54-616.22]).

CONCLUSIONS

Overall, the risk of COVID-19 complications appears low in patients with AD, even when treated with systemic immunomodulatory agents. Dupilumab monotherapy was associated with lower hospitalization than other therapies. Combination systemic treatment, particularly combinations including systemic corticosteroids, was associated with the highest risk of severe COVID-19.

Effects of SARS-CoV-2 infections in patients with cancer on mortality, ICU admission and incidence: a systematic review with meta-analysis involving 709,908 participants and 31,732 cancer patients

Background

Cancer patients constitute one of the highest-risk patient groups during the COVID-19 pandemic. In this study, it was aimed to perform a systematic review and meta-analysis to determine both the incidence and ICU (Intensive Care Unit) admission rates and mortality in SARS-CoV-2 infected cancer patients.

Methods

The PRISMA guidelines were closely followed during the design, analysis, and reporting of this systematic review and meta-analysis. A comprehensive literature search was performed for the published papers in PubMed/Medline, Scopus, medRxiv, Embase, and Web of Science (WoS) databases. SARS-CoV-2 infection pooled incidence in the cancer populations and the risk ratio (RR) of ICU admission rates/mortality in cancer and non-cancer groups, with 95% confidence intervals (CIs), were calculated using the random-effects model.

Results

A total of 58 studies, involving 709,908 participants and 31,732 cancer patients, were included in this study. The incidence in cancer patients was calculated as 8% (95% CI: 8–9%). Analysis results showed that mortality and ICU admission rate was significantly higher in patients with cancer (RR = 2.26, 95% CI: 1.94–2.62, P < 0.001; RR = 1.45, 95% CI: 1.28–1.64, p < 0.001, respectively).

Conclusion

As a result, cancer was an important comorbidity and risk factor for all SARS-CoV-2 infected patients. This infection could result in severe and even fatal events in cancer patients. Cancer is associated with a poor prognosis in the COVID-19 pandemic. Cancer patients should be assessed more sensitively in the COVID-19 outbreak.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04191-y.

Risk Factors for Admission Within a Hospital-Based COVID-19 Home Monitoring Program

Background

Despite increasing vaccination rates, coronavirus disease 2019 (COVID-19) continues to overwhelm heath systems worldwide. Few studies follow outpatients diagnosed with COVID-19 to understand risks for subsequent admissions. We sought to identify hospital admission risk factors in individuals with COVID-19 to guide outpatient follow-up and prioritization for novel therapeutics.

Methods

We prospectively designed data collection templates and remotely monitored patients after a COVID-19 diagnosis, then retrospectively analyzed data to identify risk factors for 30-day admission for those initially managed outpatient and for 30-day re-admissions for those monitored after an initial COVID-19 admission. We included all patients followed by our COVID-19 follow-up monitoring program from April 2020 to February 2021.

Results

Among 4070 individuals followed by the program, older age (adjusted odds ratio [aOR], 1.05; 95% CI, 1.03-1.06), multiple comorbidities (1-2: aOR, 5.88; 95% CI, 2.07-16.72; ≥3: aOR, 20.40; 95% CI, 7.23-57.54), presence of fever (aOR, 2.70; 95% CI, 1.65-4.42), respiratory symptoms (aOR, 2.46; 95% CI, 1.53-3.94), and gastrointestinal symptoms (aOR, 2.19; 95% CI, 1.53-3.94) at initial contact were associated with increased risk of COVID-19-related 30-day admission among those initially managed outpatient. Loss of taste/smell was associated with decreased admission risk (aOR, 0.46; 95% CI, 0.25-0.85). For postdischarge patients, older age was also associated with increased re-admission risk (aOR, 1.04; 95% CI, 1.01-1.06).

Conclusions

This study reveals that in addition to older age and specific comorbidities, the number of high-risk conditions, fever, respiratory symptoms, and gastrointestinal symptoms at diagnosis all increased odds of COVID-19-related admission. These data could enhance patient prioritization for early treatment interventions and ongoing surveillance.

Significant association between HIV infection and increased risk of COVID-19 mortality: a meta-analysis based on adjusted effect estimates

To investigate the relationship between human immunodeficiency virus (HIV) infection and the risk of mortality among coronavirus disease 2019 (COVID-19) patients based on adjusted effect estimate by a quantitative meta-analysis. A random-effects model was used to estimate the pooled effect size (ES) with corresponding 95% confidence interval (CI). I² statistic, sensitivity analysis, Begg’s test, meta-regression and subgroup analyses were also conducted. This meta-analysis presented that HIV infection was associated with a significantly higher risk of COVID-19 mortality based on 40 studies reporting risk factors-adjusted effects with 131,907,981 cases (pooled ES 1.43, 95% CI 1.25–1.63). Subgroup analyses by male proportion and setting yielded consistent results on the significant association between HIV infection and the increased risk of COVID-19 mortality. Allowing for the existence of heterogeneity, further meta-regression and subgroup analyses were conducted to seek the possible source of heterogeneity. None of factors might be possible reasons for heterogeneity in the further analyses. Sensitivity analysis indicated the robustness of this meta-analysis. The Begg’s test manifested that there was no publication bias (P = 0.2734). Our findings demonstrated that HIV infection was independently associated with a significantly increased risk of mortality in COVID-19 patients. Further well-designed studies based on prospective study estimates are warranted to confirm our findings.

Abnormal exercise adaptation after varying severities of COVID-19: A controlled cross-sectional analysis of 392 survivors

The multisystem impairment promoted by COVID-19 may be associated with a reduction in exercise capacity. Cardiopulmonary abnormalities can change across the acute disease severity spectrum. We aimed to verify exercise physiology differences between COVID-19 survivors and SARS-CoV-2-naïve controls and how illness severity influences exercise limitation. A single-center cross-sectional analysis of prospectively collected data from COVID-19 survivors who underwent cardiopulmonary exercise testing (CPET) in their recovery phase (x =50[36;72] days). Patients with COVID-19 were stratified according to severity as mild [M-Cov (outpatient)] vs severe/critical [SC-Cov(inpatients)] and were compared with SARS-CoV-2-naïve controls (N-Cov). Collected information included demographics, anthropometrics, previous physical exercise, comorbidities, lung function test and CPET parameters. A multivariate logistic regression analysis was performed to identify low aerobic capacity (LAC) predictors post COVID-19. Of the 702 included patients, 310 (44.2%), 305 (43.4%) and 87 (12.4%) were N-Cov, M-Cov and SC-Cov, respectively. LAC was identified in 115 (37.1%), 102 (33.4%), and 66 (75.9%) of N-CoV, M-CoV and SC-CoV, respectively (p < 0.001). SC-Cov were older, heavier with higher body fat, more sedentary lifestyle, more hypertension and diabetes, lower forced vital capacity, higher prevalence of early anaerobiosis, ventilatory inefficiency and exercise-induced hypoxia than N-Cov. M-Cov had lower weight, fat mass, and coronary disease prevalence and did not demonstrate more CEPT abnormalities than N-Cov. After adjustment for covariates, SC-Cov was an independent predictor of LAC (OR = 2.7; 95% CI, 1.3-5.6). Almost two months after disease onset, SC-CoV presented several exercise abnormalities of oxygen uptake, ventilatory adaptation and gas exchange, including a high prevalence of LAC.

A descriptive study of the surge response and outcomes of ICU patients with COVID-19 during first wave in Nordic countries

Background

We sought to provide a description of surge response strategies and characteristics, clinical management and outcomes of patients with severe COVID‐19 in the intensive care unit (ICU) during the first wave of the pandemic in Denmark, Finland, Iceland, Norway and Sweden.

Methods

Representatives from the national ICU registries for each of the five countries provided clinical data and a description of the strategies to allocate ICU resources and increase the ICU capacity during the pandemic. All adult patients admitted to the ICU for COVID‐19 disease during the first wave of COVID‐19 were included. The clinical characteristics, ICU management and outcomes of individual countries were described with descriptive statistics.

Results

Most countries more than doubled their ICU capacity during the pandemic. For patients positive for SARS‐CoV‐2, the ratio of requiring ICU admission for COVID‐19 varied substantially (1.6%–6.7%). Apart from age (proportion of patients aged 65 years or over between 29% and 62%), baseline characteristics, chronic comorbidity burden and acute presentations of COVID‐19 disease were similar among the five countries. While utilization of invasive mechanical ventilation was high (59%–85%) in all countries, the proportion of patients receiving renal replacement therapy (7%–26%) and various experimental therapies for COVID‐19 disease varied substantially (e.g. use of hydroxychloroquine 0%–85%). Crude ICU mortality ranged from 11% to 33%.

Conclusion

There was substantial variability in the critical care response in Nordic ICUs to the first wave of COVID‐19 pandemic, including usage of experimental medications. While ICU mortality was low in all countries, the observed variability warrants further attention.

Mental State of Inpatients With COVID-19: A Computational Psychiatry Approach

BACKGROUND

The overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with coronavirus disease 2019 (COVID-19). The relationship between severe acute respiratory syndrome with the coronavirus 2 (SARS-CoV-2) infection and specific mental disturbances remains poorly understood.

AIM

To reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena.

MATERIALS AND METHODS

Descriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables coded from the main psychopathological symptoms. Dispersion analysis with p level 0.05 was used to reveal the clusters differences in demography, parameters of inflammation, and respiration function collected on the basis of the original medical records.

RESULTS

Three resulting clusters of patients were identified: (1) persons with anxiety; disorders of fluency and tempo of thinking, mood, attention, and motor-volitional sphere; reduced insight; and pessimistic plans for the future (n = 11); (2) persons without psychopathology (n = 37); and (3) persons with disorientation; disorders of memory, attention, fluency, and tempo of thinking; and reduced insight (n = 7). The development of a certain type of impaired mental state was specifically associated with the following: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, and platelet count.

CONCLUSION

Anxiety and/or mood disturbances with psychomotor retardation as well as symptoms of impaired consciousness, memory, and insight may be considered as neuropsychiatric manifestations of COVID-19 and should be used for clinical risk assessment.

Comorbidities and COVID-19 hospitalization, ICU admission and hospital mortality in Austria : A retrospective cohort study

BACKGROUND

The protection of vulnerable populations is a central task in managing the Coronavirus disease 2019 (COVID-19) pandemic to avoid severe courses of COVID-19 and the risk of healthcare system capacity being exceeded. To identify factors of vulnerability in Austria, we assessed the impact of comorbidities on COVID-19 hospitalization, intensive care unit (ICU) admission, and hospital mortality.

METHODS

A retrospective cohort study was performed including all patients with COVID-19 in the period February 2020 to December 2021 who had a previous inpatient stay in the period 2015-2019 in Austria. All patients with COVID-19 were matched to population controls on age, sex, and healthcare region. Multiple logistic regression was used to estimate adjusted odds ratios (OR) of included factors with 95% confidence intervals (CI).

RESULTS

Hemiplegia or paraplegia constitutes the highest risk factor for hospitalization (OR 1.61, 95% CI 1.44-1.79), followed by COPD (OR 1.48, 95% CI 1.43-1.53) and diabetes without complications (OR 1.41, 95% CI 1.37-1.46). The highest risk factors for ICU admission are renal diseases (OR 1.76, 95% CI 1.61-1.92), diabetes without complications (OR 1.57, 95% CI 1.46-1.69) and COPD (OR 1.53, 95% CI 1.41-1.66). Hemiplegia or paraplegia, renal disease and COPD constitute the highest risk factors for hospital mortality, with ORs of 1.5. Diabetes without complications constitutes a significantly higher risk factor for women with respect to all three endpoints.

CONCLUSION

We contribute to the literature by identifying sex-specific risk factors. In general, our results are consistent with the literature, particularly regarding diabetes as a risk factor for severe courses of COVID-19. Due to the observational nature of our data, caution is warranted regarding causal interpretation. Our results contribute to the protection of vulnerable populations and may be used for targeting further pharmaceutical interventions.

An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan

BACKGROUND

Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient's clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient's risk of hospital admission, deterioration, and death.

OBJECTIVE

This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization.

METHODS

After the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection.

RESULTS

As of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform.

CONCLUSIONS

The methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/30083.

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

BACKGROUND

Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases.

METHODS

This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set.

RESULTS

Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/.

CONCLUSION

The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

The Association of Asthma With COVID-19 Mortality: An Updated Meta-Analysis Based on Adjusted Effect Estimates

Background

The association of asthma with the risk for mortality among coronavirus disease 2019 (COVID-19) patients is not clear.

Objective

To investigate the association between asthma and the risk for mortality among COVID-19 patients.

Methods

We performed systematic searches through electronic databases including PubMed, EMBASE, and Web of Science to identify potential articles reporting adjusted effect estimates on the association of asthma with fatal COVID-19. A random-effects model was conducted to estimate pooled effects. Sensitivity analysis, subgroup analysis, meta-regression, Begg's test and Egger's test were also performed.

Results

Based on 62 studies with 2,457,205 cases reporting adjusted effect estimates, COVID-19 patients with asthma had a significantly reduced risk for mortality compared with those without it (15 cohort studies: 829,670 patients, pooled hazard ratio [HR] = 0.88, 95% confidence interval [CI], 0.82-0.95, I² = 65.9%, P < .001; 34 cohort studies: 1,008,015 patients, pooled odds ratio [OR] = 0.88, 95% CI, 0.82-0.94, I² = 39.4%, P = .011; and 11 cross-sectional studies: 1,134,738 patients, pooled OR = 0.87, 95% CI, 0.78-0.97, I² = 41.1%, P = .075). Subgroup analysis based on types of adjusted factors indicated that COVID-19 patients with asthma had a significantly reduced risk for mortality among studies adjusting for demographic, clinical, and epidemiologic variables (pooled OR = 0.87, 95% CI, 0.83-0.92, I² = 36.3%, P = .013; pooled HR = 0.90, 95% CI, 0.83-0.97, I² = 69.2%, P < .001), but not among studies adjusting only for demographic variables (pooled OR = 0.88, 95% CI, 0.70-1.12, I² = 40.5%, P = .097; pooled HR = 0.82, 95% CI, 0.64-1.06, I² = 0%, P = .495). Sensitivity analysis proved that our results were stable and robust. Both Begg's test and Egger's test indicated that potential publication bias did not exist.

Conclusions

Our data based on adjusted effect estimates indicated that asthma was significantly related to a reduced risk for COVID-19 mortality.

COVID-19: Epidemiology, Case Fatalities and the Adversaries within

How to cite this article: Kumar A. COVID-19: Epidemiology, Case Fatalities and the Adversaries within. Indian J Crit Care Med 2021;25(6):603-605.