Epidemiological and clinical characteristics of coronavirus disease (COVID-19) cases at a screening clinic during the early outbreak period: a single-centre study

Role of ACE2 and TMPRSS2 polymorphisms on COVID-19 outcome and disease severity in adult patients: A prospective cohort study in a tertiary hospital, Indonesia

Coronavirus disease 2019 (COVID-19) has led to a significant number of infections and deaths worldwide, yet its pathogenesis and severity remain incompletely understood. Angiotensin-converting enzyme 2 (ACE2) and transmembrane protease, serine 2 (TMPRSS2), play crucial roles as receptors and molecules responsible for the virus's entry into host cells, initiating the infection process. Their polymorphisms have been extensively studied in relation to COVID-19 severity. The aim of this study was to examine the association of ACE2 (rs2074192) and TMPRSS2 (rs12329760) polymorphisms with COVID-19 outcome and severity. A prospective cohort study was conducted in 2022 at Haji Adam Malik Hospital, Medan, Indonesia. We randomly recruited hospitalized adult patients with COVID-19, confirmed by real-time polymerase chain reaction (RT-PCR). The baseline demographic data, disease severity, underlying disease, comorbidities, and COVID-19 vaccination status were collected. The single-nucleotide polymorphism (SNP) was assessed using TaqMan SNP genotyping assay, and the levels of TMPRSS2 and ACE2 proteins were measured using enzyme-linked immunosorbent assay (ELISA). A total of 151 COVID-19 patients were recruited and there were significant associations between age and severity with mortality outcomes. The age, kidney and lung diseases, and vaccination status were associated with severity levels. The results showed the CC genotype of ACE2 had the highest proportion, followed by TT and CT genotypes among patients, while CT was the most prevalent genotype, followed by CC and TT for TMPRSS2. This study did not find a significant association between ACE2 and TMPRSS2 genetic variants with disease severity and outcome but highlighted a specific association of TMPRSS2 SNP with mortality within the group. In addition, ACE2 concentration was significant different between mild-moderate and severe-critical COVID-19 groups (p=0.033).

COVID-19 profiles in general practice: a latent class analysis

BACKGROUND

General practitioners (GPs) were on the front line of the COVID-19 outbreak. Identifying clinical profiles in COVID-19 might improve patient care and enable closer monitoring of at-risk profiles.

OBJECTIVES

To identify COVID-19 profiles in a population of adult primary care patients, and to determine whether the profiles were associated with negative outcomes and persistent symptoms.

DESIGN, SETTING AND PARTICIPANTS

In a prospective multicentre study, 44 GPs from multiprofessional primary care practices in the Paris area of France recruited 340 consecutive adult patients (median age: 47 years) with a confirmed diagnosis of COVID-19 during the first two waves of the epidemic.

METHOD AND OUTCOME

A latent class (LC) analysis with 11 indicators (clinical signs and symptoms) was performed. The resulting profiles were characterised by a 3-month composite outcome (COVID-19-related hospital admission and/or death) and persistent symptoms three and 6 months after inclusion.

RESULTS

We identified six profiles: 'paucisymptomatic' (LC1, 9%), 'anosmia and/or ageusia' (LC2, 12.9%), 'influenza-like syndrome with anosmia and ageusia' (LC3, 15.5%), 'influenza-like syndrome without anosmia or ageusia' (LC4, 24.5%), 'influenza-like syndrome with respiratory impairment' (LC5) and a 'complete form' (LC6, 17.7%). At 3 months, 7.4% of the patients were hospitalised (with higher rates in LC5), and 18% had persistent symptoms (with higher rates in LC5 and LC6). At 6 months, 6.4% of the patients had persistent symptoms, with no differences between LCs.

CONCLUSION

Our findings might help GPs to identify patients at risk of persistent COVID-19 symptoms and hospital admission and then set up procedures for closer monitoring.

A closer look at the link between cycle threshold, clinical features and biomarkers: An observational study in COVID-19 patients

Background

Symptoms for severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) appear 2-3 days after exposure to the virus. Being a virus, detection is primarily by polymerase chain reaction as this offers superior sensitivity and specificity. There was a misconception that patients with low cycle threshold (Ct) have severe coronavirus disease (COVID), and for individuals with higher Ct, it is the other way around. The prognosis for COVID was derived from various biomarkers and physicians heavily relied on them.

Materials and Methods

A cross-sectional study spanning a duration of 2 years was conducted at a tertiary care centre in western India. A total of 201 individuals were included and the correlation between Ct, clinical features and biomarkers was studied.

Results

In the E-gene, 43.28% had lower Ct values and 40.79% had low Ct values in the RdRp gene. 50% of all patients had diabetes, with 60% being between the ages of 61 and 80. 54.1% of hypertension patients belonged to ages between 61 and 80. 90.54% of COVID-positive individuals had lactose dehydrogenase levels ranging from 440 to 760. 79% of patients had a procalcitonin value of more than one but less than six. 79.1% of patients had an erythrocyte sedimentation rate between 36 and 90.

Conclusion

Ct value though has a research value; it is a poor prognostic marker when compared to the various biomarkers that have been studied earlier. We cannot conclusively state that all our findings are accurate due to a lack of data but further research into the prognostic value of Ct should be conducted which will help in the ongoing scenario.

Neuropsychological Symptoms and Quality of Life during the COVID-19 Pandemic in Children: A Survey in a Pediatric Population in the Abruzzo Region, Italy

BACKGROUND

The SARS-CoV-2 pandemic has significantly affected the pediatric population. Long-term sequelae (Long COVID-19) may particularly involve the central nervous system, with possible effects on psychological well-being and quality of life (QoL), aspects that were already influenced by the restrictive measures and general social impact of the pandemic.

METHODS

We conducted a cross-sectional survey that aims at investigating the neuropsychological effects and the QoL impairment of SARS-CoV-2 on a cohort of children and adolescents in the Abruzzo region (Italy). A questionnaire was submitted to caregivers with the help of the PEDIATOTEM platform. A control group of healthy subjects was also included to distinguish between the effects of infection from the general influence of the pandemic.

RESULTS

A total of 569 subjects responded: 396 COVID-19 patients (99 of whom had Long COVID-19) and 111 controls. After the pandemic, when compared with the COVID-19 group, the controls reported significantly increased appetite, sleeping habits, and time spent remotely with friends and a reduction in physical activity and time spent in person with friends. A significant higher rate of controls asked for psychological/medical support for emotional problems. On the other hand, the Long COVID-19 group showed more fatigue and emotional instability with respect to non-Long-COVID-19 subjects. No differences in QoL results (EuroQOL) were found between the COVID-19 patients and controls, while the Long-COVID-19 subgroup showed significantly higher rates of pain/discomfort and mood instability, as confirmed by the analysis of variation of responses from the pre-COVID-19 to the post-COVID-19 period.

CONCLUSIONS

Among COVID-19 patients, neuropsychological and QoL impairment was more evident in the Long COVID-19 subgroup, although emotional and relational issues were also reported by uninfected patients, with a growing request for specialist support as a possible consequence of social restriction.

Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic

After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63-82%), sensitivity of 52% (18-71%), and specificity of 84% (71-92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.

Assessment of functional fitness impacted by hospital rehabilitation in post-stroke patients who additionally contracted COVID-19

Background

The aim of the study was to assess the effects of rehabilitation in post-stroke patients, or post-stroke patients with simultaneous COVID-19 infection, in relation to: improved locomotion efficiency, improved balance, reduced risk of falling as well as the patients' more effective performance in everyday activities.

Methods

The study involved 60 patients in the early period (2-3 months) after a stroke. Group I consisted of 18 patients (30.0%) who, in addition to a stroke, also contracted COVID-19. Group II consisted of 42 patients (70%) post-stroke, with no SARS-CoV2 infection. The effects were assessed on the basis of: Tinetti test, Timed Up & Go test and Barthel scale.

Results

Both groups achieved a statistically significant improvement in their Barthel score after therapy (p < 0.001). The Tinetti test, assessing gait and balance, showed that participants in Group I improved their score by an average of 4.22 points. ±4.35, and in Group II, on average, by 3.48 points ± 3.45 points. In the Timed Up & Go test over a distance of 3 m, significant improvement was achieved in both groups, as well but the effect was higher in Group I (p < 0.001).

Conclusions

Hospital rehabilitation in the early period after stroke improved locomotion efficiency and balance, and reduced the risk of falls in post-stroke patients, both with and without COVID-19 infection.

Asymptomatic viruses detectable in saliva in the first year of life: a narrative review

Viral infections are common in children. Many can be asymptomatic or have delayed health consequences. In view of increasing availability of point-of-care viral detection technologies, with possible application in newborn screening, this review aimed to (1) identify potentially asymptomatic viruses detectable in infants under one year old, via saliva/nasopharyngeal swab, and (2) describe associations between viruses and long-term health conditions. We systematically searched Embase(Ovid), Medline(Ovid) and PubMed, then further searched the literature in a tiered approach. From the 143 articles included, 28 potentially asymptomatic viruses were identified. Our second search revealed associations with a range of delayed health conditions, with most related to the severity of initial symptoms. Many respiratory viruses were linked with development of recurrent wheeze or asthma. Of note, some potentially asymptomatic viruses are linked with later non-communicable diseases: adenovirus serotype 36 and obesity, Enterovirus-A71 associated Hand, Foot, Mouth Disease and Attention-Deficit Hyperactivity Disorder, Ebstein Barr Virus (EBV) and malignancy, EBV and multiple sclerosis, HHV-6 and epilepsy, HBoV-1 and lung fibrosis and Norovirus and functional gastrointestinal disorders. Our review identified many potentially asymptomatic viruses, detectable in early life with potential delayed health consequences, that could be important to screen for in the future using rapid point-of-care viral detection methods. IMPACT: Novel point-of-care viral detection technologies enable rapid detection of viruses, both old and emerging. In view of increasing capability to screen for viruses, this is the first review to explore which potentially asymptomatic viruses, that are detectable using saliva and/or nasopharyngeal swabs in infants less than one year of age, are associated with delayed adverse health conditions. Further research into detecting such viruses in early life and their delayed health outcomes may pave new ways to prevent non-communicable diseases in the future.

Clinical Manifestations Laboratory Tests Abdominal Ultrasonic Findings and In-hospital Prognosis of COVID-19 in 185 Pediatric Cases in a Tertiary Center

BACKGROUND

Despite the COVID-19 pandemic, there is little information about the different clinical aspects of COVID-19 in children. In this study, we assessed the clinical manifestations, outcome, ultrasound, and laboratory findings of pediatric COVID-19.

METHODS

This retrospective study was conducted on 185 children with definitive diagnosis of COVID-19 between 2021 and 2022. The patients' information was retrieved from hospital records.

RESULTS

The average age of the patients was 5.18 ± 4.55 years, and 61.1% were male. The most frequent clinical manifestation was fever (81.1%) followed by cough (31.9%), vomiting (20.0%), and diarrhea (20.0%). Mesenteric lymphadenitis was common on ultrasound and found in 60% of cases. In-hospital death was identified in 3.8% of cases. The mean length of hospital stay was 8.5 days. Mandating intensive care unit (ICU) stay was found in 19.5% and 5.9% of cases were intubated. Acute respiratory distress syndrome (ARDS), lower arterial oxygen saturation, higher white blood cell (WBC) count, and higher C-reactive protein (CRP) were the main determinants of death. Lower age, respiratory distress, early onset of clinical manifestations, lower arterial oxygen saturation, lower serum hemoglobin (Hb) level, and higher CRP level could predict requiring ICU admission.

CONCLUSION

We recommend close monitoring on CRP, serum Hb level, WBC count, and arterial level of oxygenation as clinical indicators for potential progression to critical illness and severe disease. Mesenteric lymphadenitis is a common sonographic finding in pediatric COVID-19 which can cause abdominal pain. Ultrasound is helpful to avoid unnecessary surgical interventions in COVID-19.

Influencing factors of sleep disorders and sleep quality in healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis

AIM

The aim of this study was to identify the influencing factors of sleep disorders and sleep quality in healthcare workers during the COVID-19 pandemic.

DESIGN

Systematic review and meta-analysis of observational research.

METHODS

The databases of the Cochrane Library, Web of Science, PubMed, Embase, SinoMed database, CNKI, Wanfang Data, and VIP were systematically searched. The quality of studies was assessed using the Agency for Healthcare Research and Quality evaluation criteria and the Newcastle-Ottawa scale.

RESULTS

A total of 29 studies were included, of which 20 were cross-sectional studies, eight were cohort studies, and 1 was a case-control study; 17 influencing factors were finally identified. Greater risk of sleep disturbance was associated with female gender, single relationship status, chronic disease, insomnia history, less exercise, lack of social support, frontline work, days served in frontline work, department of service, night shift, years of work experience, anxiety, depression, stress, received psychological assistance, worried about being infected, and degree of fear with COVID-19.

CONCLUSIONS

During the COVID-19 pandemic, healthcare workers did have worse sleep quality than the general population. The influencing factors of sleep disorders and sleep quality in healthcare workers are multifaceted. Identification and timely intervention of resolvable influencing factors are particularly important for preventing sleep disorders and improving sleep.

PATIENT OR PUBLIC CONTRIBUTION

This is a meta-analysis of previously published studies so there was no patient or public contribution.

Neurological complications and effects of COVID-19: Symptoms and conceivable mechanisms

A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in December 2019 in Wuhan, China. The new coronavirus disease (COVID-19) was declared a global pandemic by the World Health Organization (WHO) in March 2020. SARS-CoV-2 can invade the nervous system aside from infecting the respiratory system as its primary target. The most common nervous system symptoms of COVID-19 are stated as headache, myalgia, fatigue, nausea, vomiting, sudden and unexplained anosmia, and ageusia. More severe conditions such as encephalomyelitis, acute myelitis, thromboembolic events, ischemic stroke, intracerebral hemorrhage, Guillain-Barré-syndrome, Bell's palsy, rhabdomyolysis, and even coma have also been reported. Cohort studies revealed that neurological findings are associated with higher morbidity and mortality. The neurological symptoms and manifestations caused by SARS-CoV-2 and COVID-19 are examined and summarized in this article.

Neurological Manifestations in Hospitalized Geriatric Patients With COVID-19 at King Abdulaziz Medical City in Jeddah, Western Region, Saudi Arabia From 2020 to 2021: A Cross-Sectional Study

Introduction COVID-19 involvement in the nervous system has been reported in many cases. Viral neuroinvasion has multiple routes of entry. Neurological manifestations of COVID-19 can be divided into ones of the central nervous system (CNS), such as headache, dizziness, altered mental status, ataxia, and seizure, and of the peripheral nervous system (PNS), including ageusia, anosmia, acute illness demyelinating polyneuropathy, and neuralgia. Aim and objectives This study aims to observe and report the neurological manifestations in geriatric patients who were diagnosed with COVID-19 at KAMC-J and report the duration of admission to the in-patient and ICU wards. Methods This was a cross-sectional study conducted on admitted geriatric patients with PCR-confirmed COVID-19 from April 1, 2020 to June 30, 2021 at KAMC-J. Using Raosoft®, the sample size was estimated with a CI of 95% and a 36.4% prevalence of neurological symptoms in COVID-19 patients to be 289. Convenience sampling was used, and the data were collected from BESTCare EMRs. IBM SPSS Statistics for Windows, Version 20 (Released 2011) was used for descriptive and inferential statistical analysis. Results In this study, a total of 290 patients' data were collected, 161 (55.5%) of which were males. In addition, the median age was 71 (Q1-Q3: 65-78) years; furthermore, the median body mass index (BMI) was 30(Q1-Q3: 25-34) kg/m2. In descending order, the most prevalent comorbidities were hypertension (HTN) (70.3%), diabetes mellitus (DM) (68.6%), cardiac disease (42.1%), chronic kidney disease (26.6%), neurological disease (23.6%), cancer malignancy (13.1%), and finally chronic respiratory disease (11.4%). Regarding typical COVID-19 manifestations, 181 patients claimed to have experienced cough (62.4%), dyspnea by 164 (56.7%), fever by 154 (53.5%), fatigue by 93 (32.3%), a reading of anoxia by 68 (23.4%), abdominal pain by 58 (20.0%), diarrhea by 56 (19.4%), and finally throat pain by 19 (6.6%). Manifestations and pathologies of the CNS included headache (25.4%), dizziness (21.5%), impaired consciousness (17.2%), delirium (6.6%), ischemic stroke (4.1%), focal cranial nerve dysfunction (2.8%), seizure (2.8%), intracerebral hemorrhage (ICH) (0.3%), and ataxia (0.3%). Moreover, pathologies of the PNS manifested as taste impairment in 46 patients (15.9%), smell impairment in 33 (11.4%), nerve pain in 7 (24%), visual impairment in 5 (1.7%), Bell's palsy in 2 (0.7%), and Guillain-Barre syndrome in 1 (0.3%). Moreover, the majority of patients who developed an ischemic stroke or ICH, or required admission to the ICU had either DM or HTN. In addition, 17 (25.4%) of the 67 patients admitted to the ICU developed impaired consciousness. All-cause mortality in our study was 31 (10.71%) cases. Conclusion Neurological manifestations of COVID-19 are common and can result in serious complications if not detected and managed early, especially in the elderly. These complications are mostly seen in severely ill patients and may be the only symptoms in COVID-19 patients. In addition, patients' clinical conditions could deteriorate rapidly and result in significant morbidity and mortality. Therefore, a high index of suspicion is required among healthcare providers when dealing with such cases. Moreover, we recommend systematically collecting data on the short- and long-term neurological complications of COVID-19 globally and documenting the functional long-term outcomes after these complications.

Prognostic Role of Clinical Features of Moderate Forms of COVID-19 Requiring Hospitalization

INTRODUCTION

We aimed to characterize the clinical features of moderate forms of COVID-19 requiring hospitalization and potentially identify predictors for unfavorable outcomes.

METHODS

Pooled anonymized clinical data from 452 COVID-19 patients hospitalized in two regional Romanian respiratory disease centers during the Alpha and Delta variant outbreaks were included in the analysis.

RESULTS

Cough and shortness of breath were the most common clinical features; older patients exhibited more fatigue and dyspnea and fewer upper airway-related symptoms such as smell loss or sore throat. The presence of confusion, shortness of breath and age over 60 years were significantly associated with worse outcomes (odds ratios 5.73, 2.08 and 3.29, respectively).

CONCLUSION

The clinical picture on admission may have a prognostic role for moderate forms of COVID-19. Clear clinical definitions and developing adequate informational infrastructure allowing complex data sharing and analysis might be useful for fast research response should a similar outbreak occur in the future.

Initial characteristics and course of disease in patients with suspected COVID-19 managed in general practice: a prospective, multicentre cohort study

OBJECTIVES

To describe and compare the initial clinical characteristics of a cohort of patients with suspected COVID-19 managed by general practitioners (GPs); to assess whether 3-month persistent symptoms were more frequent among confirmed cases than among no-COVID cases; and to identify factors predictive of persistent symptoms and adverse outcomes among confirmed cases.

DESIGN AND SETTING

A comparative, prospective, multicentre cohort study in primary care in the Paris region of France.

PARTICIPANTS

521 patients aged ≥18 with suspected COVID-19 were enrolled between March and May 2020.

OUTCOME MEASURES

Initial symptoms, COVID-19 status, persistent symptoms 3 months after inclusion and a composite criterion for potentially COVID-19-related events (hospitalisation, death, emergency department visits). The final COVID-19 status ('confirmed', 'no-COVID' and 'uncertain' cases) was determined by the GP after the receipt of the laboratory test results.

RESULTS

516 patients were analysed; 166 (32.2%) were classified into the 'confirmed COVID' group, 180 (34.9%) into the 'no-COVID' group and 170 (32.9%) in the 'uncertain COVID' group. Confirmed cases were more likely to have persistent symptoms than no-COVID cases (p=0.09); initial fever/feeling feverish and anosmia were independently associated with persistent symptoms. At 3 months, we observed 16 (9.8%) COVID-19-related hospital admissions, 3 (1.8%) intensive care unit admissions, 13 (37.1%) referrals to an emergency department and no death. Age >70 and/or at least one comorbidity (OR 6.53; 95% CI 1.13-37.84; p=0.036), abnormalities in a lung examination (15.39; 95% CI 1.61-146.77; p=0.057) and two or more systemic symptoms (38.61; 95% CI 2.30-647.40; p=0.011) were associated with the composite criterion.

CONCLUSIONS

Although most patients with COVID-19 in primary care had mild disease with a benign course, almost one in six had persistent symptoms at 3 months. These symptoms were more frequent in the 'confirmed COVID' group. Our findings need to be confirmed in a prospective study with longer follow-up.

Epidemiology, clinical characteristics and risk factors of coronavirus disease 2019 (COVID-19) in Casablanca

This is an analytical cross-sectional study of coronavirus disease 2019 (COVID-19) based on data collected between 1 November 2020 and 31 March 2021 in Casablanca focusing on the disease's epidemiological status and risk factors. A total of 4569 samples were collected and analysed by reverse-transcription polymerase chain reaction (RT-PCR); 967 patients were positive, representing a prevalence of 21.2 % for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The mean age was 47.5±18 years, and infection was more common in young adults (<60 years). However, all age groups were at risk of COVID-19, and in terms of disease severity, the elderly were at greater risk because of potential underlying health problems. Among the clinical signs reported in this study, loss of taste and/or smell, fever, cough and fatigue were highly significant predictors of a positive COVID-19 test result (P<0.001). An assessment of the reported symptoms revealed that 27 % of COVID-19-positive patients (n=261) experienced loss of taste and/or smell, whereas only 2 % (n=72) of COVID-19-negative patients did (P<0.001). This result was consistent between univariate (OR=18.125) and multivariate (adjusted OR=10.484) logistic regression analyses, indicating that loss of taste and/or smell is associated with a more than 10-fold higher multivariate adjusted probability of a positive COVID-19 test (adjusted OR=10.48; P<0.001). Binary logistic regression model analysis based on clinical signs revealed that loss of taste and/or smell had a performance index of 0.846 with a P<0.001, confirming the diagnostic utility of this symptom for the prediction of COVID-19-positive status. In conclusion, symptom evaluation and a RT-PCR [taking into account cycle threshold (C t) values of the PCR proxy] test remain the most useful screening tools for diagnosing COVID-19. However, loss of taste/smell, fatigue, fever and cough remain the strongest independent predictors of a positive COVID-19 result.

Pathogenic mechanisms of post-acute sequelae of SARS-CoV-2 infection (PASC)

COVID-19, with persistent and new onset of symptoms such as fatigue, post-exertional malaise, and cognitive dysfunction that last for months and impact everyday functioning, is referred to as Long COVID under the general category of post-acute sequelae of SARS-CoV-2 infection (PASC). PASC is highly heterogenous and may be associated with multisystem tissue damage/dysfunction including acute encephalitis, cardiopulmonary syndromes, fibrosis, hepatobiliary damages, gastrointestinal dysregulation, myocardial infarction, neuromuscular syndromes, neuropsychiatric disorders, pulmonary damage, renal failure, stroke, and vascular endothelial dysregulation. A better understanding of the pathophysiologic mechanisms underlying PASC is essential to guide prevention and treatment. This review addresses potential mechanisms and hypotheses that connect SARS-CoV-2 infection to long-term health consequences. Comparisons between PASC and other virus-initiated chronic syndromes such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome will be addressed. Aligning symptoms with other chronic syndromes and identifying potentially regulated common underlining pathways may be necessary for understanding the true nature of PASC. The discussed contributors to PASC symptoms include sequelae from acute SARS-CoV-2 injury to one or more organs, persistent reservoirs of the replicating virus or its remnants in several tissues, re-activation of latent pathogens such as Epstein-Barr and herpes viruses in COVID-19 immune-dysregulated tissue environment, SARS-CoV-2 interactions with host microbiome/virome communities, clotting/coagulation dysregulation, dysfunctional brainstem/vagus nerve signaling, dysautonomia or autonomic dysfunction, ongoing activity of primed immune cells, and autoimmunity due to molecular mimicry between pathogen and host proteins. The individualized nature of PASC symptoms suggests that different therapeutic approaches may be required to best manage specific patients.

Pathogenic mechanisms of post-acute sequelae of SARS-CoV-2 infection (PASC)

COVID-19, with persistent and new onset of symptoms such as fatigue, post-exertional malaise, and cognitive dysfunction that last for months and impact everyday functioning, is referred to as Long COVID under the general category of post-acute sequelae of SARS-CoV-2 infection (PASC). PASC is highly heterogenous and may be associated with multisystem tissue damage/dysfunction including acute encephalitis, cardiopulmonary syndromes, fibrosis, hepatobiliary damages, gastrointestinal dysregulation, myocardial infarction, neuromuscular syndromes, neuropsychiatric disorders, pulmonary damage, renal failure, stroke, and vascular endothelial dysregulation. A better understanding of the pathophysiologic mechanisms underlying PASC is essential to guide prevention and treatment. This review addresses potential mechanisms and hypotheses that connect SARS-CoV-2 infection to long-term health consequences. Comparisons between PASC and other virus-initiated chronic syndromes such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome will be addressed. Aligning symptoms with other chronic syndromes and identifying potentially regulated common underlining pathways may be necessary for understanding the true nature of PASC. The discussed contributors to PASC symptoms include sequelae from acute SARS-CoV-2 injury to one or more organs, persistent reservoirs of the replicating virus or its remnants in several tissues, re-activation of latent pathogens such as Epstein-Barr and herpes viruses in COVID-19 immune-dysregulated tissue environment, SARS-CoV-2 interactions with host microbiome/virome communities, clotting/coagulation dysregulation, dysfunctional brainstem/vagus nerve signaling, dysautonomia or autonomic dysfunction, ongoing activity of primed immune cells, and autoimmunity due to molecular mimicry between pathogen and host proteins. The individualized nature of PASC symptoms suggests that different therapeutic approaches may be required to best manage specific patients.

SARS-CoV-2 and its impact on the cardiovascular and digestive systems - The interplay between new virus variants and human cells

Since infection with the novel coronavirus SARS-CoV-2 first emerged in Wuhan, China, in December 2019, the world has been battling the pandemic COVID-19. Patients of all ages and genders are now becoming infected with the new coronavirus variant (Omicron) worldwide, and its subvariants continue to pose a threat to health and life. This article provides a literature review of cardiovascular and gastrointestinal complications resulting from SARS-CoV-2 infection. COVID-19 primarily caused respiratory symptoms, but complications can affect many vital organs. SARS-CoV-2 binds to a human cell receptor (angiotensin-converting enzyme 2 - ACE2) that is predominantly expressed primarily in the heart and gastrointestinal tract, which is why we focused on complications in these organs. Since the high transmissibility of Omicron and its ability to evade the immune system have raised worldwide concern, we have tried to summarise the current knowledge about its development from a structural point of view and to highlight the differences in its binding to human receptors and proteases compared to previous VOC.

D-dimer and HbA1c levels findings in COVID-19 Iraqi patients

On March 11, 2020, the World Health Organization (WHO) declared a new coronavirus infection caused by the SARS-CoV-2 virus as a pandemic, making it the 11th pandemic of the 20th and 21st centuries. This study investigated the clinical and laboratory results (D-dimer, conventional coagulation, and HbA1c biomarker concentrations) of 150 patients (75 male and 75 female) with confirmed COVID-19 pneumonia and 50 controls (25 male and 25 female). For disease diagnosis, all COVID-19 patients were given a Real-Time Reverse Transcription Polymerase Chain Reaction Assay (RT-PCR). The findings revealed that D-dimer and HbA1c levels in COVID-19 patients were significantly higher (P 0.001) at the time of admission; In COVID-19 patients, there was also a strong correlation between D-dimer levels and HbA1c levels (P 0.001). In conclusion, COVID-19 patients are more likely to have a poor prognosis if their D-dimer and HbA1c levels remain uncontrolled over a lengthy period. To lower the likelihood of a bad prognosis in COVID-19, patients with higher levels of D-dimer and HbA1c should be continuously monitored.

Evaluation of evidence of prevention and management of facial pressure injuries in medical staff

AIM

This systematic review evaluated the quality of evidence for the prevention and management of facial pressure injuries in medical staff.

DESIGN

This review was presented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

METHODS

We retrieved the relevant studies from 19 databases. Using the literature evaluation standards and evidence grading system of the Australian Joanna Briggs Institute Evidence-Based Health Care Center, we evaluated the quality of the literature encompassing different types of research and assessed their levels of evidence.

RESULTS

A total of 13 studies were included, including seven expert consensuses, two recommended practices, one clinical decision, one best practice information booklet, one systematic review and one randomized controlled trial. In the end, 31 best evidence were summarized, including skin cleaning and care, PPE placement and movement, reasonable use of dressings, treatment measures and education and training.

Evaluation of Time-Varying Biomarkers in Mortality Outcome in COVID-19: an Application of Extended Cox Regression Model

BACKGROUND

COVID-19 pandemic has created many challenges for clinicians. The monitoring trend for laboratory biomarkers is helpful to provide additional information to determine the role of those in the severity status and death outcome.

OBJECTIVE

This article aimed to evaluate the time-varying biomarkers by LOWESS Plot, check the proportional hazard assumption, and use to extended Cox model if it is violated.

METHODS

In the retrospective study, we evaluated a total of 1641 samples of confirmed patients with COVID-19 from October until March 2021 and referred them to the central hospital of Ayatollah Rohani Hospital affiliated with Babol University of medical sciences, Iran. We measured four biomarkers AST, LDH, NLR, and lymphocyte in over the hospitalization to find out the influence of those on the rate of death of COVID-19 patients.

RESULTS

The standard Cox model suggested that all biomarkers were prognostic factors of death (AST: HR=2.89, P<0.001, Lymphocyte: HR=2.60, P=0.004, LDH: HR=2.60, P=0.006, NLR: HR=1.80, P<0.001). The additional evaluation showed that the PH assumption was not met for the NLR biomarker. NLR biomarkers had a significant time-varying effect, and its effect increase over time (HR(t)=exp (0.234+0.261×log(t)), p=0.001). While the main effect of NLR did not show any significant effect on death outcome (HR=1.26, P=0.097).

CONCLUSION

The reversal of results between the Cox PH model and the extended Cox model provides insight into the value of considering time-varying covariates in the analysis, which can lead to misleading results otherwise.

COVID-19 patient characteristics and time to viral clearance: A retrospective observational study in a multiethnic population (United Arab Emirates)

BACKGROUND

SARS-CoV-2 virus is the causing agent of COVID-19. The factors contributing to delayed viral clearance are still unclear.

METHODS

We investigated the factors influencing the time to viral clearance in COVID-19 patients using medical records from 1785 adult patients of various ethnicities treated at NMC Royal Hospital in Abu Dhabi, UAE. The Cox-proportional Hazard Model was utilized to identify risk variables for delayed viral clearance, and the Kaplan-Meier plot was used to measure the time to viral clearance among different groups.

RESULTS

several factors have been associated with an increased risk of delayed viral clearance, including advanced age (p = 0.006), presence of cardiovascular diseases (p = 0.016), presentation with upper respiratory tract infection (URTI) (p = 0.043), and combined gastrointestinal (GIT) and symptoms (URTI) (p = 0.012). ICU admission and severity of COVID-19 also increased the risk for delayed viral clearance (p = 0.006, p < 0.001, respectively). 'The overall median viral clearance time was 24 days. It was 32 days among patients over 60, 21 among those with URTI, GIT symptoms, and asymptomatic, 24 among diabetics, and 46.5 days among cardiovascular patients. The median time till viral clearance was 30 days among severe COVID-19 patients and 39 days among ICU-admitted patients.

CONCLUSIONS

We concluded that advanced age, cardiovascular comorbidities, disease presentation, and severe COVID-19 outcomes increased the risk of delayed viral clearance. Identifying these factors allow decision makers to implement an early and comprehensive management strategy to improve the outcome.

The impact of caregivers on nosocomial transmission during a COVID-19 outbreak in a community-based hospital in South Korea

The COVID-19 pandemic becomes a cause of concern for hospital transmission. Caregivers may play an important role as vectors for nosocomial infections; however, infection control for caregivers often is neglected. A nosocomial COVID-19 outbreak occurred in a 768-bed hospital from March 20, 2020, to April 14, 2020. We conducted a retrospective chart review and epidemiologic investigation on all cases. A total of 54 cases of laboratory-confirmed COVID-19 occurred in the community-based hospital. They included 26 (48.1%) patients, 21 (38.9%) caregivers, and 7 (13.0%) healthcare workers. These 21 caregivers cared for 18 patients, and of these, 9 were positive for COVID-19, 6 were negative, and 3 died before testing. Of the 6 negative patients, 3 had no exposure because the caregiver began to show symptoms at least 5 days after their discharge. Of the 9 positive patients, 4 cases of transmission took place from patient to caregiver (one patient transmitted COVID-19 to two caregivers), and 6 cases of transmission occurred from caregiver to patient. Of the 54 hospital-acquired cases, 38 occurred in the 8th-floor ward and 8 occurred in the 4th-floor ward. The index case of each ward was a caregiver. Counting the number of cases where transmission occurred only between patients and their own caregivers, 9 patients were suspected of having exposure to COVID-19 from their own caregivers. Six patients (66.7%) were infected by COVID-19-confirmed caregivers, and 3 patients were uninfected. Fewer patients among the infected were able to perform independent activities compared to uninfected patients. Not only patients and healthcare workers but also caregivers groups may be vulnerable to COVID-19 and be transmission sources of nosocomial outbreaks. Therefore, infection control programs for caregivers in addition to patients and healthcare workers can be equally important.

Jupytope: computational extraction of structural properties of viral epitopes

Epitope residues located on viral surface proteins are of immense interest in immunology and related applications such as vaccine development, disease diagnosis and drug design. Most tools rely on sequence-based statistical comparisons, such as information entropy of residue positions in aligned columns to infer location and properties of epitope sites. To facilitate cross-structural comparisons of epitopes on viral surface proteins, a python-based extraction tool implemented with Jupyter notebook is presented (Jupytope). Given a viral antigen structure of interest, a list of known epitope sites and a reference structure, the corresponding epitope structural properties can quickly be obtained. The tool integrates biopython modules for commonly used software such as NACCESS, DSSP as well as residue depth and outputs a list of structure-derived properties such as dihedral angles, solvent accessibility, residue depth and secondary structure that can be saved in several convenient data formats. To ensure correct spatial alignment, Jupytope takes a list of given epitope sites and their corresponding reference structure and aligns them before extracting the desired properties. Examples are demonstrated for epitopes of Influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) viral strains. The extracted properties assist detection of two Influenza subtypes and show potential in distinguishing between four major clades of SARS-CoV2, as compared with randomized labels. The tool will facilitate analytical and predictive works on viral epitopes through the extracted structural information. Jupytope and extracted datasets are available at https://github.com/shamimarashid/Jupytope.

Identification of Suitable Drug Combinations for Treating COVID-19 Using a Novel Machine Learning Approach: The RAIN Method

Simple Summary

This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended.

Abstract

COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer.

Association between smoking and COVID-19 severity: A multicentre retrospective observational study

The relationship between smoking and coronavirus disease 2019 (COVID-19) severity remains unclear. This study aimed to investigate the effect of smoking status (current smoking and a smoking history) on the clinical severity of COVID-19. Data of all enrolled 588 patients, who were referred to 25 hospitals in Jiangsu province between January 10, 2020 and March 14, 2020, were retrospectively reviewed. Univariate and multivariate regression, random forest algorithms, and additive interaction were used to estimate the importance of selective predictor variables in the relationship between smoking and COVID-19 severity. In the univariate analysis, the proportion of patients with a current smoking status in the severe group was significantly higher than that in the non-severe group. In the multivariate analysis, current smoking remained a risk factor for severe COVID-19. Data from the interaction analysis showed a strong interaction between the number of comorbidities in patients with COVID-19 and smoking. However, no significant interaction was found between smoking and specific comorbidities, such as hypertension, diabetes, etc. In the random forest model, smoking history was ranked sixth in mean decrease accuracy. Active smoking may be significantly associated with an enhanced risk of COVID-19 progression towards severe disease. However, additional prospective studies are needed to clarify the complex relationship between smoking and COVID-19 severity.

Differential diagnosis of COVID-19 and influenza

This study uses two existing data sources to examine how patients' symptoms can be used to differentiate COVID-19 from other respiratory diseases. One dataset consisted of 839,288 laboratory-confirmed, symptomatic, COVID-19 positive cases reported to the Centers for Disease Control and Prevention (CDC) from March 1, 2019, to September 30, 2020. The second dataset provided the controls and included 1,814 laboratory-confirmed influenza positive, symptomatic cases, and 812 cases with symptomatic influenza-like-illnesses. The controls were reported to the Influenza Research Database of the National Institute of Allergy and Infectious Diseases (NIAID) between January 1, 2000, and December 30, 2018. Data were analyzed using case-control study design. The comparisons were done using 45 scenarios, with each scenario making different assumptions regarding prevalence of COVID-19 (2%, 4%, and 6%), influenza (0.01%, 3%, 6%, 9%, 12%) and influenza-like-illnesses (1%, 3.5% and 7%). For each scenario, a logistic regression model was used to predict COVID-19 from 2 demographic variables (age, gender) and 10 symptoms (cough, fever, chills, diarrhea, nausea and vomiting, shortness of breath, runny nose, sore throat, myalgia, and headache). The 5-fold cross-validated Area under the Receiver Operating Curves (AROC) was used to report the accuracy of these regression models. The value of various symptoms in differentiating COVID-19 from influenza depended on a variety of factors, including (1) prevalence of pathogens that cause COVID-19, influenza, and influenza-like-illness; (2) age of the patient, and (3) presence of other symptoms. The model that relied on 5-way combination of symptoms and demographic variables, age and gender, had a cross-validated AROC of 90%, suggesting that it could accurately differentiate influenza from COVID-19. This model, however, is too complex to be used in clinical practice without relying on computer-based decision aid. Study results encourage development of web-based, stand-alone, artificial Intelligence model that can interview patients and help clinicians make quarantine and triage decisions.

Detection, prevention and treatment of COVID-19 and opportunities for nanobiotechnology

Since the outbreak of COVID-19, the number of confirmed cases and deaths has increased globally at a dramatic speed. In view of the serious health threat to humans, this review discusses the state-of-the-art studies about fighting this disease. It summarizes the current strategies and recent advances in detecting, preventing, and treating COVID-19 and interprets the underlying mechanisms in detail. Detection of COVID-19 can be successfully achieved by multiple techniques such as polymerase chain reaction, computed tomography imaging, and nano-biosensing. Inactivated virus vaccine, nucleic acid vaccine, and different nanoparticles have been employed to effectively prevent COVID-19. A variety of agents such as antiviral agents, neutralizing antibodies, and nanotherapeutics have been developed to treat COVID-19 with exciting efficacy. Although nanobiotechnology has shown great potential in the diagnosis, prevention, and treatment of COVID-19, efforts should be made to explore new biocompatible nano-biomaterials to advance this field to clinical applications. Hence, nanobiotechnology paves a new way to detect, prevent, and treat COVID-19 effectively.

An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms

Background

Artificial intelligence-based disease prediction models have a greater potential to screen COVID-19 patients than conventional methods. However, their application has been restricted because of their underlying black-box nature.

Objective

To addressed this issue, an explainable artificial intelligence (XAI) approach was developed to screen patients for COVID-19.

Methods

A retrospective study consisting of 1,737 participants (759 COVID-19 patients and 978 controls) admitted to San Raphael Hospital (OSR) from February to May 2020 was used to construct a diagnosis model. Finally, 32 key blood test indices from 1,374 participants were used for screening patients for COVID-19. Four ensemble learning algorithms were used: random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). Feature importance from the perspective of the clinical domain and visualized interpretations were illustrated by using local interpretable model-agnostic explanations (LIME) plots.

Results

The GBDT model [area under the curve (AUC): 86.4%; 95% confidence interval (CI) 0.821–0.907] outperformed the RF model (AUC: 85.7%; 95% CI 0.813–0.902), AdaBoost model (AUC: 85.4%; 95% CI 0.810–0.899), and XGBoost model (AUC: 84.9%; 95% CI 0.803–0.894) in distinguishing patients with COVID-19 from those without. The cumulative feature importance of lactate dehydrogenase, white blood cells, and eosinophil counts was 0.145, 0.130, and 0.128, respectively.

Conclusions

Ensemble machining learning (ML) approaches, mainly GBDT and LIME plots, are efficient for screening patients with COVID-19 and might serve as a potential tool in the auxiliary diagnosis of COVID-19. Patients with higher WBC count, higher LDH level, or higher EOT count, were more likely to have COVID-19.

A hospital cluster of COVID-19 associated with a SARS-CoV-2 superspreading event

BACKGROUND/PURPOSE

Superspreading events (SSEs) are pivotal in the spread of SARS-CoV-2. This study aimed to investigate an SSE of COVID-19 in a hospital and explore the transmission dynamics and heterogeneity of SSE.

METHODS

We performed contact tracing for all close contacts in a cluster. We did nasopharyngeal or throat swabbing for SARS-CoV-2 by real-time RT-PCR. Environmental survey was performed. The epidemiological and clinical characteristics of the SSE were studied.

RESULTS

Patient 1 with congestive heart failure and cellulitis, who had onset of COVID-19 two weeks after hospitalization, was the index case. Patient 1 led to 8 confirmed cases, including four health care workers (HCW). Persons tested positive for SARS-CoV-2 were HCW (n = 4), patient 1's family (n = 2), an accompanying person of an un-infected in-patient (n = 1), and an in-patient admitted before the SSE (n = 1). The attack rate among the HCW was 3.2 % (4/127). Environmental survey confirmed contamination at the bed rails, mattresses, and sink in the room patient 1 stayed, suggesting fomite transmission. The index case's sputum remained positive on illness day 35. Except one asymptomatic patient, at least three patients acquired the infection from the index case at the pre-symptomatic period. The effective reproduction number (Rt) was 0.9 (8/9).

CONCLUSION

The host factor (heart failure, longer viral shedding), transmissibility of SARS-CoV-2 (Rt, pre-symptomatic transmission), and possible multiple modes of transmission altogether contributed to the SSE. Rapid response and advance deployment of multi-level protection in hospitals could mitigate COVID-19 transmission to one generation, thereby reducing its impact on the healthcare system.

COVID-19 sample management: experiences of Harare City, 2021

COVID-19 sample management is pivotal in controlling the pandemic. Results of 28/45 (62.2%) cases identified at a high school COVID-19 outbreak in Harare's Northern district were not reported within the recommended 24 hours of notification. This leads to delayed patient management. We evaluated the sample management system for COVID-19 in Harare City. A descriptive cross-sectional study was conducted in Harare City. Health care workers involved in COVID-19 sample management at a high school outbreak in the Northern district namely clinicians, laboratory, environmental and administrative personnel were purposively sampled. Interviewer-administered questionnaires were used to collect data. Quantitative data were analyzed using Epi info version 7. Medians and proportions were generated. A 3-point Likert Scale was used to measure knowledge levels of health care workers on COVID-19 sample management. Thirty health care workers were interviewed and females were 20/30 (66%). Participants had not been trained in sample management. Overall knowledge level was good for 7/30 (23%) of the participants. Of the samples collected, 156/451 (34.6%) were wrongly sent to the national microbiology laboratory and 53/156 (34.0%) of the results were received. Sample management of COVID-19 samples in Harare City was found to affect patient management because of poor knowledge of healthcare workers, lack of transportation and communication means. The need for training cadres involved in the management process and availing adequate resources can improve turnaround time of results hence patient management.

Clinical characteristics of patients with COVID-19 admitted to the COVID-19 Emergency Field Hospital of Bangkalan, Indonesia

Background: Following the surge of coronavirus disease 2019 (COVID-19) cases in the epicenter of East Java Province, this study aimed to determine the clinical characteristics of patients with COVID-19 at one of the emergency field hospitals in Indonesia. Methods: This was a single-centered, retrospective descriptive study of 763 patients admitted to the COVID-19 Emergency Field Hospital of Bangkalan from July 5 2021 to September 30 2021. The demographic data, clinical signs and symptoms, pre-existing comorbidities, therapy, and clinical outcomes of the patients were analyzed using SPSS. Results: The clinical characteristics of patients with COVID-19 at the emergency hospital were varied. A total of 763 patients were included. The most common age was between 40 and 49 years (31.1%), a slight majority were women (51.5%), and most had travelled abroad in the last 14 days (99.1%). Of the 763 patients, 70.9% had no comorbidities. Half of the patients were asymptomatic (49.4%), 46% were mild cases, 4.1% were moderate, and 0.5% severe. The most common symptoms were productive cough (15.7%) and headache (15.3%). Supportive and comorbidity therapy were given which showed excellent clinical outcomes. Conclusions: The majority of COVID-19 patients were asymptomatic, female, middle aged and had recently been overseas. Therapy without antibiotics or antivirals showed positive outcomes in COVID-19 patients.

Clinical characteristics of patients with COVID-19 admitted to the COVID-19 Emergency Field Hospital of Bangkalan, Indonesia

Background: Following the surge of coronavirus disease 2019 (COVID-19) cases in the epicenter of East Java Province, this study aimed to determine the clinical characteristics of patients with COVID-19 at one of the emergency field hospitals in Indonesia. Methods: This was a single-centered, retrospective descriptive study of 763 patients admitted to the COVID-19 Emergency Field Hospital of Bangkalan from July 5 2021 to September 30 2021. The demographic data, clinical signs and symptoms, pre-existing comorbidities, therapy, and clinical outcomes of the patients were analyzed using SPSS. Results: The clinical characteristics of patients with COVID-19 at the emergency hospital were varied. A total of 763 patients were included. The most common age was between 40 and 49 years (31.1%), a slight majority were women (51.5%), and most had travelled abroad in the last 14 days (99.1%). Of the 763 patients, 70.9% had no comorbidities. Half of the patients were asymptomatic (49.4%), 46% were mild cases, 4.1% were moderate, and 0.5% severe. The most common symptoms were productive cough (15.7%) and headache (15.3%). Supportive and comorbidity therapy were given which showed excellent clinical outcomes. Conclusions: This study presents the description of the clinical characteristics of COVID-19 patients during high surge cases of COVID-19 that are mostly dominated by Indonesian migrant workers in a field hospital. majority of COVID-19 patients were asymptomatic and therapy without antibiotics or antivirals showed positive outcomes in COVID-19 patients.

Targeting Specific Checkpoints in the Management of SARS-CoV-2 Induced Cytokine Storm

COVID-19-infected patients require an intact immune system to suppress viral replication and prevent complications. However, the complications of SARS-CoV-2 infection that led to death were linked to the overproduction of proinflammatory cytokines known as cytokine storm syndrome. This article reported the various checkpoints targeted to manage the SARS-CoV-2-induced cytokine storm. The literature search was carried out using PubMed, Embase, MEDLINE, and China National Knowledge Infrastructure (CNKI) databases. Journal articles that discussed SARS-CoV-2 infection and cytokine storm were retrieved and appraised. Specific checkpoints identified in managing SARS-CoV-2 induced cytokine storm include a decrease in the level of Nod-Like Receptor 3 (NLRP3) inflammasome where drugs such as quercetin and anakinra were effective. Janus kinase-2 and signal transducer and activator of transcription-1 (JAK2/STAT1) signaling pathways were blocked by medicines such as tocilizumab, baricitinib, and quercetin. In addition, inhibition of interleukin (IL)-6 with dexamethasone, tocilizumab, and sarilumab effectively treats cytokine storm and significantly reduces mortality caused by COVID-19. Blockade of IL-1 with drugs such as canakinumab and anakinra, and inhibition of Bruton tyrosine kinase (BTK) with zanubrutinib and ibrutinib was also beneficial. These agents' overall mechanisms of action involve a decrease in circulating proinflammatory chemokines and cytokines and or blockade of their receptors. Consequently, the actions of these drugs significantly improve respiration and raise lymphocyte count and PaO₂/FiO₂ ratio. Targeting cytokine storms' pathogenesis genetic and molecular apparatus will substantially enhance lung function and reduce mortality due to the COVID-19 pandemic.

Cardiovascular disturbances in COVID-19: an updated review of the pathophysiology and clinical evidence of cardiovascular damage induced by SARS-CoV-2

Severe acute respiratory coronavirus-2 (SARS-Co-2) is the causative agent of coronavirus disease-2019 (COVID-19). COVID-19 is a disease with highly variable phenotypes, being asymptomatic in most patients. In symptomatic patients, disease manifestation is variable, ranging from mild disease to severe and critical illness requiring treatment in the intensive care unit. The presence of underlying cardiovascular morbidities was identified early in the evolution of the disease to be a critical determinant of the severe disease phenotype. SARS-CoV-2, though a primarily respiratory virus, also causes severe damage to the cardiovascular system, contributing significantly to morbidity and mortality seen in COVID-19. Evidence on the impact of cardiovascular disorders in disease manifestation and outcome of treatment is rapidly emerging. The cardiovascular system expresses the angiotensin-converting enzyme-2, the receptor used by SARS-CoV-2 for binding, making it vulnerable to infection by the virus. Systemic perturbations including the so-called cytokine storm also impact on the normal functioning of the cardiovascular system. Imaging plays a prominent role not only in the detection of cardiovascular damage induced by SARS-CoV-2 infection but in the follow-up of patients' clinical progress while on treatment and in identifying long-term sequelae of the disease.

COVID-19: A systematic review and update on prevention, diagnosis, and treatment

Since the rapid onset of the COVID-19 or SARS-CoV-2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the virus in order to determine the best ways to diagnosis of virus and thereof formulate effective drugs or vaccines to combat the disease. The emergence of novel diagnostic and therapeutic techniques considering the multiplicity of reports from one side and contradictions in assessments from the other side necessitates instantaneous updates on the progress of clinical investigations. There is also growing public anxiety from time to time mutation of COVID-19, as reflected in considerable mortality and transmission, respectively, from delta and Omicron variants. We comprehensively review and summarize different aspects of prevention, diagnosis, and treatment of COVID-19. First, biological characteristics of COVID-19 were explained from diagnosis standpoint. Thereafter, the preclinical animal models of COVID-19 were discussed to frame the symptoms and clinical effects of COVID-19 from patient to patient with treatment strategies and in-silico/computational biology. Finally, the opportunities and challenges of nanoscience/nanotechnology in identification, diagnosis, and treatment of COVID-19 were discussed. This review covers almost all SARS-CoV-2-related topics extensively to deepen the understanding of the latest achievements (last updated on January 11, 2022).

Detection of SARS-CoV-2 infection by exhaled breath spectral analysis: Introducing a ready-to-use point-of-care mass screening method

BACKGROUND

The current SARS-CoV-2 pandemic created an urgent need for rapid, infection screening applied to large numbers of asymptomatic individuals. To date, nasal/throat swab polymerase chain reaction (PCR) is considered the "gold standard". However, this is inconducive to mass, point-of-care (POC) testing due to person discomfort during sampling and a prolonged result turnaround. Breath testing for disease specific organic compounds potentially offers a practical, rapid, non-invasive, POC solution. The study compares the Breath of Health, Ltd. (BOH) breath analysis system to PCR's ability to screen asymptomatic individuals for SARS-CoV-2 infection. The BOH system is mobile and combines Fourier-transform infrared (FTIR) spectroscopy with artificial intelligence (AI) to generate results within 2 min and 15 s. In contrast to prior SARS-CoV-2 breath analysis research, this study focuses on diagnosing SARS-CoV-2 via disease specific spectrometric profiles rather than through identifying the disease specific molecules.

METHODS

Asymptomatic emergency room patients with suspected SARS-CoV-2 exposure in two leading Israeli hospitals were selected between February through April 2021. All were tested via nasal/throat-swab PCR and BOH breath analysis. In total, 297 patients were sampled (mean age 57·08 ± SD 18·86, 156 males, 139 females, 2 unknowns). Of these, 96 were PCR-positive (44 males, 50 females, 2 unknowns), 201 were PCR-negative (112 males, 89 females). One hundred samples were used for AI identification of SARS-CoV-2 distinguishing spectroscopic wave-number patterns and diagnostic algorithm creation. Algorithm validation was tested in 100 proof-of-concept samples (34 PCR-positive, 66 PCR-negative) by comparing PCR with AI algorithm-based breath-test results determined by a blinded medical expert. One hundred additional samples (12 true PCR-positive, 85 true PCR-negative, 3 confounder false PCR-positive [not included in the 297 total samples]) were evaluated by two blinded medical experts for further algorithm validation and inter-expert correlation.

FINDINGS

The BOH system identified three distinguishing wave numbers for SARS-CoV-2 infection. In the first phase, the single expert identified the first 100 samples correctly, yielding a 1:1 FTIR/AI:PCR correlation. The two-expert second-phase also yielded 1:1 FTIR/AI:PCR correlation for 97 non-confounders and null correlation for the 3 confounders. Inter-expert correlation was 1:1 for all results. In total, the FTIR/AI algorithm demonstrated 100% sensitivity and specificity for SARS-CoV-2 detection when compared with PCR.

INTERPRETATION

The SARS-CoV-2 method of breath analysis via FTIR with AI-based algorithm demonstrated high PCR correlation in screening for asymptomatic individuals. This is the first practical, rapid, POC breath analysis solution with such high PCR correlation in asymptomatic individuals. Further validation is required with a larger sample size.

FUNDING

Breath of Health Ltd, Rehovot, Israel provided study funding.

The Relationship Between Insulin Use And Increased Mortality In Patients With COVID-19 And Diabetes: A Meta-Analysis

PURPOSE

The purpose of this study is to review observational studies on the effect of insulin use and mortality in patients with COVID-19 and diabetes.

METHODS

A systematic literature search was performed using the PubMed, Medline, EMBASE, and Cochrane Library databases. The meta-analysis was performed using a random effects model, and I² was applied to evaluate heterogeneity. Sensitivity and subgroup analyses were also performed.

RESULTS

Overall, 1,338 patients over six studies were ultimately included. Insulin use was related to a higher risk of death in diabetic patients with COVID-19 compared to those who did not use insulin (odds ratio: 2.59, 95% confidence interval: 1.66-4.05; P < .0001; I²: 57%).

CONCLUSIONS

This meta-analysis revealed a correlation between insulin usage and increased mortality in diabetic patients with COVID-19. These results showed that insulin requirement in patients with COVID-19 and diabetes might indicate a poor prognosis.

A cross-sectional study of gastrointestinal manifestations in COVID-19 Egyptian patients

BACKGROUND

The latest novel corona virus disease (COVID-19) pandemic shows a significant health concern. We aimed to study the prevalence of gastrointestinal symptoms among COVID-19 Egyptian patients.

METHODS

A cross-sectional study was carried out on 860 patients with COVID-19 infection classified according to Ministry of Health Program (MOHP) into three groups (280 patients with mild infection, 258 patients with moderate disease and 322 patients with severe disease). All patients were subjected to medical history, clinical examination, laboratory investigations, high-resolution computed tomography chest (HRCT chest) and other investigations when needed in some patients e.g., upper gastro-intestinal (GI) endoscopy, abdomino-pelvic ultrasound and ECHO.

RESULTS

Gastro-intestinal symptoms were present in 27.2% of the studied patients. The most common reported GIT symptoms were vomiting, diarrhea, abdominal/gastric pain, followed by nausea. GIT symptoms presence was significantly higher in severe cases in comparison to mild or moderate cases. C-reactive protein (CRP), serum ferritin, Aspartate aminotransferase (AST), bilirubin, and creatinine were significantly associated with the presence of GI symptoms.

CONCLUSIONS

GI symptoms are prevalent among COVID-19 patients, the most common were vomiting and diarrhea and were associated with COVID-19 severity.

Prevalence and Biochemical Associations of Fever in Adults With Reverse Transcription Polymerase Chain Reaction Proven Coronavirus Disease Presenting at Tertiary Care Hospitals in Rawalpindi

Background

Clinically most apparent symptoms of COVID-19 include fever and cough, which in some patients show a worsening trend but are completely non-apparent in patients who present with an asymptomatic course of the disease. The aim of this study was to identify clinical and biochemical differences among polymerase chain reaction (PCR) positive patients who are either febrile or afebrile.

Methods

This study was conducted in Rawalpindi Medical University and Allied Hospitals between September and December 2020. All patients who tested positive for reverse transcription polymerase chain reaction (RT-PCR) COVID-19 were included in the study. After evaluation of 146 patients, 100 were selected, and with a response rate of 97%, a total of 97 patients were included in the final analysis. Depending on the presence of fever, the participants were divided into two groups. Both groups were then compared for baselines vitals and laboratory investigations. Data was entered and analyzed in SPSS v23.0 (IBM Inc., Armonk, New York).

Results

Among the 97 patients, 66 (68%) of the participants were male, and 31 (32%) were females. The mean age of the study participants was 45.23±18.08 years. Fever was present in 39 (40.2%) of the participants. When compared with patients with no fever, the patients with fever had greater severity of disease (p<0.001), higher heart rate (p<0.001), decreased oxygen saturation (p<0.001). Among the laboratory investigations, the fever group had a greater tendency of having deranged alanine aminotransferase (ALT) (70.82±29.23 vs. 32.83±16.22, p=0.010), Lymphocytes (1.56±0.54 vs. 2.12±0.94, p=0.003) and serum total bilirubin (1.06±0.36 vs. 0.55±0.21, p=0.009). Based on multiple regression analysis, the presence of fever is a predictor of derangement in ALT (OR=1.034, CI=1.001-1.068 p=0.025) and total bilirubin (OR=4.38, CI=2.14-6.78, p=0.021).

Conclusion

Fever may not be present among all patients presenting with COVID-19 infection, but those who have a fever have a greater risk of having deranged liver function tests. Hence, it is important to monitor liver function tests (LFTs) in COVID-19 patients presenting with fever. 

Comparative Analysis of Five Multiplex RT-PCR Assays in the Screening of SARS-CoV-2 Variants

The rapid and presumptive detection of SARS-CoV-2 variants may be performed using multiplex RT-PCR assays. The aim of this study was to evaluate the diagnostic performance of five qualitative RT-PCR tests as compared with next-generation sequencing (NGS). We retrospectively examined a multi-variant panel (n = 72) of SARS-CoV-2-positive nasopharyngeal swabs categorized as variants of concern (Alpha, Beta, Gamma and Delta), variants under monitoring (Iota and Kappa) and wild-type strains circulating in Liguria (Italy) from January to August 2021. First, NGS libraries of study samples were prepared and mapped to the reference genome. Then, specimens were screened for the detection of L452R, W152C, K417T, K417N, E484Q, E484K and N501Y mutations using the SARS-CoV-2 Variants II Assay Allplex, UltraGene Assay SARS-CoV-2 452R & 484K & 484Q Mutations V1, COVID-19 Ultra Variant Catcher, SARS-CoV-2 Extended ELITe MGB and Simplexa SARS-CoV-2 Variants Direct. The overall accuracy of these assays ranged from 96.9% to 100%. Specificity and sensitivity were 100% and 96-100%, respectively. We highly recommend the use of these assays as second-level tests in the routine workflow of SARS-CoV-2 laboratory diagnostics, as they are accurate, user friendly, low cost, may identify specific mutations in about 2-3 h and, therefore, optimize the surveillance of SARS-CoV-2 variants.

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

Serum amyloid A, ferritin and carcinoembryonic antigen as biomarkers of severity in patients with COVID-19

In view of the rapid spread of COVID-19 and the high mortality rate of severe cases, reliable risk stratifying indicators of prognosis are necessary to decrease morbidity and mortality. The aim of the present study was to evaluate the value of serum amyloid A (SAA) and carcinoembryonic antigen (CEA) as prognostic biomarkers in comparison to other predictors, including C-reactive protein (CRP) and ferritin levels. This study included 124 patients diagnosed with COVID-19, and they were assigned to one of two groups: Mild and severe, based on the severity of the infection. Radiological and laboratory investigations were performed, including evaluation of CRP, ferritin, D-Dimer, SAA and CEA levels. Significantly higher levels of CRP, ferritin, D-Dimer, SAA and CEA were observed in severe cases. SAA was significantly correlated with CRP (r=0.422, P<0.001), ferritin (r=0.574, P<0.001), CEA (r=0.514, P<0.001) and computed tomography severity score (CT-SS; r=0.691, P<0.001). CEA was correlated with CRP (r=0.441, P<0.001), ferritin (r=0.349, P<0.001) and CT-SS (r=0.374, P<0.001). Receiver operator characteristic (ROC) curves for performance of SAA, CEA, ferritin, CRP and SAA showed the highest AUC value of 0.928, with a specificity of 93.1%, and a sensitivity of 98.5% at a cut-off of 16 mg/l. The multi-ROC curve for SAA and ferritin showed 100% specificity, 100% sensitivity and 100% efficiency, with an AUC of 1.000. Thus, combining SAA and ferritin may have guiding significance for predicting COVID-19 severity. SAA alone showed the highest prognostic significance. Both SAA and CEA were positively correlated with the CT-SS. Early monitoring of these laboratory markers may thus provide significant input for halting disease progression and reducing mortality rates.

The Association Between FT3 With the Outcome and Inflammation/Coagulopathy/Fibrinolysis of COVID-19

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has caused substantial threats to people's physical health and lives, claiming the lives of over 5 million people worldwide. It is imperative to identify the disease severity and intervene with effective therapy as early as possible. Previous studies have shown that low free triiodothyronine (FT3) may possess the predictive value on COVID-19 prognosis.

METHODS

In this retrospective cohort study, 15-day clinical and laboratory data of 186 hospitalized patients of COVID-19 after admission were analyzed. Groups were based on the disease severity of COVID-19, survival or non-survival, and presence or absence of euthyroid sick syndrome (ESS). Categorical variables were compared with the chi-square test or Fisher's exact test. Continuous variables were tested by Wilcoxon rank-sum test for the non-normal distribution. Spearman correlations were used to assess the correlations between FT3 with clinic parameters of multiple time points.

RESULTS

The non-survival patients had significant lower levels of FT3 (3.24 ± 0.42 vs. 4.19 ± 0.08 pmol/L, p < 0.05) and thyroid-stimulating hormone (TSH) (0.69 ± 0.19 vs. 2.32 ± 0.2 uIU/ml, p < 0.05), and the FT3 of severe patients was significantly lower than that of non-severe patients (3.67 ± 0.14 vs. 4.33 ± 0.09 pmol/L, p < 0.05). Fifty-nine cases of COVID-19 patients were diagnosed with ESS. Compared with non-ESS patients, those with ESS were older and had higher proportions of fever, shortness of breath, hypertension, diabetes, severe disease, and mortality. In addition, the correlation analysis between FT3 and clinical parameters showed that FT3 were positively related to the lymphocyte count and albumin and negatively correlated with C-reactive protein, erythrocyte sedimentation rate, and D-dimer at all time points in the first 15 days after admission.

CONCLUSION

Low FT3 had a significant predictive value on the prognosis of COVID-19 patients, and FT3 was significantly related with clinic parameters of inflammation/coagulopathy/fibrinolysis.

An Epidemiological Study of COVID-19 Cases in Al-Leith, Saudi Arabia

Background and objective

Ever since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in December 2019, more than 0.550 million cases of coronavirus disease 2019 (COVID-19) have been reported in the kingdom of Saudi Arabia (KSA) and the number is on the rise. In this study, we aimed to perform an epidemiological analysis of COVID-19 cases in Al-Leith, KSA.

Methods

A community-based descriptive study was carried out to assess the epidemiology of COVID-19 in Al-Leith, KSA. The relevant data were collected using a questionnaire designed for this study, which included questions on basic information and characteristics in addition to data on COVID-19. Data were analyzed using the SPSS Statistics software (IBM, Armonk, NY).

Results

The significant findings were as follows: people aged between 26-40 years were more affected (55.7%) than other age groups, and more than half (74.1%) of COVID-19 patients were female; most of them were employed (85, 48,9%), and most of those got infected through direct contact (137, 78.7%) with other infected people. About 163 (93.7%) cases were asymptomatic, and 168 (96.6%) cases were notified to the health authorities. The findings also illustrated that 78 (44.8%) COVID-19 cases suffered from psychological impact due to infection; 83 (47.7%) cases had at least one case in the family before they became infected. The majority of cases (93.7%) suffered from symptomatic COVID-19. A considerable number of COVID-19 patients did not follow precautions before and after infection.

Conclusions

The study concluded that various age groups were susceptible to developing COVID-19, and direct contact was the main mode of transmission. Moreover, a considerable number of infected people did not adhere to precautionary measures.

Characteristics and Factors Associated with SARS-CoV-2 Infections in Individuals That Attended Referral Hospitals from Southern Region of Bahia State, Brazil: A Surveillance Network Retrospective Study

In December 2019, a novel coronavirus was detected in Wuhan, China, and rapidly spread worldwide. In Brazil, to date, there have been more than 20,000,000 confirmed cases of COVID-19 and more than 550,000 deaths. The purpose of the current study was to determine the clinical and epidemiological profile of the population affected by COVID-19 that have attended referral hospitals in Southern region of Bahia State, to better understand the disease and its risk factors in order to enable more appropriate conduct for patients. An observational, descriptive, cross-sectional, exploratory study was conducted using secondary data collected from the Laboratório de Farmacogenômica e Epidemiologia Molecular, Universidade Estadual de Santa Cruz (LAFEM/UESC). Chi-squared and Fisher's exact tests were applied to determine the association between clinical symptoms and laboratory results, and to identify risk factors associated with SARS-CoV-2 infection. A total of 3135 individuals with suspected severe respiratory illness were analyzed and 41.4% of them tested positive for SARS-CoV-2 infection. Male individuals and having comorbidities were risk factors significantly associated with SARS-CoV-2 infection (OR = 1.17 and OR = 1.37, respectively). Interestingly, being a healthcare professional was a significantly protective factor (OR = 0.81, p < 0.001). Our findings highlight the importance of routinely testing the population for early identification of infected individuals, and also provide important information to health authorities and police makers to improve control measures, management, and screening protocols.

Spectrum of COVID-19 clinical characteristics among patients presenting to the primary healthcare in Qatar during the early stages of the pandemic: a retrospective multicentre cross-sectional study

OBJECTIVES

To describe clinical characteristics and laboratory investigations of patients with COVID-19 diagnosed in primary care in Qatar and to assess predictors of hospitalisation.

DESIGN

A retrospective cross-sectional study.

SETTING AND PARTICIPANTS

3515 confirmed patients with COVID-19 diagnosed in any of the 27 primary healthcare centres in Qatar between 9 April 2020 and 30 June 2020.

MAIN OUTCOME MEASURES

Demographic characteristics, comorbidities, contact tracing, clinical and laboratory data, in addition to patient disposition at the time of diagnosis RESULTS: Mean age of patients was 35.5 years (±14.7). 2285 patients (65.0%) were males, 961 patients (27.3%) had a history of concomitant comorbidity and 640 patients (18.2%) were asymptomatic. Adult patients (19-64 years old) were more likely to report symptoms than children or elderly. Fever and cough were the most frequently documented symptoms affecting 1874 patients (46.7%) and 1318 patients (37.5%), respectively. Most patients had normal vital signs at presentation; however, patients who were subsequently hospitalised had higher median temperature than non-hospitalised patients (37.7°C, IQR: 37.0°C-38.4°C, and 37.2°C, IQR: 36.8°C-37.8°C, respectively). Hospitalised patients had significantly higher C reactive protein (CRP) (median CRP: 20 mg/L, IQR: 5.0-61.2 mg/L) than non-hospitalised patients (median CRP: 4.6 mg/L, IQR: 1.7-11.50 mg/L), and lower median absolute lymphocyte count (1.5×103/µL, IQR: 1.1×103/µL-2.1×103/µL, and 1.8×103/µL, IQR: 1.3×103/µL-2.4×103/µL, respectively). Predictors of hospitalisation were increasing age (adjusted OR (AOR): 2.614, 95% CI 1.281 to 5.332 for age between 50 years and 64 years, and AOR: 3.892, 95% CI 1.646 to 9.204 for age ≥65 years), presence of two or more comorbidities (AOR: 2.628; 95% CI 1.802 to 3.832) and presence of symptoms (AOR: 1.982: 95% CI 1.342 to 2.928).

CONCLUSION

The majority of COVID-19 cases diagnosed in primary healthcare in Qatar were symptomatic. Most cases had normal vital signs and laboratory results at presentation. Predictors of hospitalisation were increasing age, the presence of symptoms and having two or more comorbidities.

Analysis of Covid-19 Hospital Admissions in Ivano-Frankivsk, Ukraine

The Covid-19 pandemic has a substantial impact on socioeconomic, political, demographic, and other aspects of life. Effective healthcare is, however, a primary determinant of successful fighting against Covid-19. The analysis of local healthcare indicators serves as a source for estimating pandemic magnitude and the adaptation of healthcare at a national level. In this study, the rates of Covid-19 hospital admissions to the Ivano-Frankivsk City Hospital, Ukraine, from April 2020 to May 2021 were analysed. All cases were grouped by age, sex, and the type of admission; data were analyzed monthly and seasonally. The peaks of hospital admissions were observed in November 2020 and March 2021; however, the highest mortality rates were seen from August to November 2020. The analysis of age- and sex-disaggregated Covid-19 mortality data showed the predominance of elderly males (61.9%, 66.6 ± 3.9 years) over females (38.1%, 71.8 ± 2.3 years). The ratio of hospital admissions was unstable: the percentage of emergency, GP-referred and self-referred admissions was similar from April to May 2020; however, GP-referred admissions prevailed, and the number of self-referral patients decreased twice between March and May 2021. In conclusions, the trends in hospital admissions were similar to those reported in other studies. However, the differences in time frames and socio-demographic characteristics were observed that highlights the importance of considering regional, social and geographic aspects of the population when improving the capacity of healthcare system and establishing effective preventive measures against the pandemic at the local level.

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit

Background

Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 hours of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died.

Methods

Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture.

Results

The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p<0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%).

Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients.

Conclusions

Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.