Risk Factors and Clinical Outcomes Associated With Acute Respiratory Distress Syndrome in Pregnant and Non-pregnant Women Diagnosed With COVID-19: A Comparative Analysis

OBJECTIVE

We aim to compare risk factors and clinical outcomes of acute respiratory distress syndrome (ARDS) associated with coronavirus disease 2019 (COVID-19) in pregnant versus non-pregnant women of reproductive age.

MATERIALS AND METHODS

This retrospective study included all women (18-45 years) with ARDS and confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection admitted during the study period from May 2020 to July 2021. Pregnant women were considered as case and non-pregnant women as control. Primary outcomes included ventilatory support, the need for high-flow nasal oxygenation (HFNO), severe ARDS, and death. Secondary outcomes included intensive care unit (ICU) admission, length of hospital stay, and requirement of oxygen at discharge.

RESULTS

We included 59 women diagnosed with ARDS and confirmed SARS-CoV-2 infection, of them 12 were pregnant and 47 were non-pregnant. The non-pregnant women were significantly older than pregnant women (28.7±5 versus 35.5±8.2, p=0.008). Presenting symptoms were comparable among the groups. Diabetes was significantly higher in the non-pregnant group (8.3% versus 31.9%, p<0.02). Pregnant women had a significantly higher range of D-dimer (5.8±7.2 versus 1.8±1.9, p<0.01) and interleukin-6 (IL-6) (212.0±300.8 versus 49.7±57.7, p<0.011) and lower platelet count (129.4±120.1 versus 197.6±92.9, p<0.05) compared to non-pregnant women. Pregnant women were more likely to experience primary outcomes including the need for HFNO (33% versus 8.5%, odds ratio (OR): 5.3, p<0.02) and death (50% versus 31.9%, OR: 2.1, p<0.04) compared to non-pregnant women.

CONCLUSION

Pregnant women with severe COVID-19 and ARDS were at an increased risk for experiencing ICU admission, intubation, and mechanical ventilation compared to age-matched non-pregnant women, although comorbidities such as diabetes were higher among the non-pregnant cohort. These findings suggest that pregnancy itself is a potential risk factor for complications and morbidities among women with severe COVID-19.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

COVID-19 Protective Behaviors of Iranian Healthcare Workers: A Psychological Theoretical Design Analysis

Background: This study aimed to investigate COVID-19 protective behaviors of Iranian healthcare workers (HCWs) using an extended parallel process model (EPPM). Methods: This cross-sectional study was conducted online on a convenience sample of 418 Iranian HCWs in 2020. Data were gathered using a standardized electronic questionnaire published on the Porsline website, designed based on the EPPM, to assess the relationships between different constructs of EPPM and COVID-19 protective behaviors. For data analysis, statistical tests, such as descriptive tests, independent t-test, ANOVA, Pearson's correlation test, and multiple regression analysis, were performed in SPSS version 18. Results: Most participants (40.4%) were in the age group of 30-40 years, and most were female (70.6%). Significant associations were found between COVID-19 protective behaviors and EPPM constructs of perceived self-efficacy (r = 0.373, P = 0.000), perceived response efficacy (r = 0.120, P = 0.014), and intention (r = 0.462, P = 0.000). Perceived self-efficacy (P = 0.000, beta = 0.398) and behavioral intention (P = 0.000; Beta = 0.283) were predictors of protective behaviors. Also, 34.4% of the HCWs were involved in the danger control process. Conclusions: Only one-third of the HCWs contributed to the process of COVID-19 danger control. The HCWs' motivation for protection against COVID-19 depended on their perception of self-efficacy and their perceived efficacy of COVID-19 preventive behaviors. The present results can be used to design and implement training programs to improve the protective behaviors of HCWs.

Clinical characteristics and Co-morbidities among patients admitted with COVID-19

Background

During the Coronavirus Disease-2019 (COVID-19) pandemic, several characteristics of COVID-19 patients, based on demographics, clinical symptoms, and the presence of comorbidities, were found to be associated with the complications developed. COVID-19 symptoms vary greatly and are more prominent with comorbid diseases. Therefore, the aim of this study to find the clinical characteristics and its association with different comorbidities.

Methods

This is a retrospective study that was performed on the data obtained from medical records of 3999 patients in Riyadh. Demographic data, clinical symptoms and comorbidities were noted on the day of hospital admission. Complications developed during the COVID -19 infection were observed.

Results

The average age of patients were 49.55 years old. Fever was the most common symptom among the patients (85.85%), followed by cough (85.85%), and shortness of breath (83.25%). The most common comorbidities were diabetes mellitus (39.51%), hypertension (33.91%), and asthma (9.45%), with chronic rhinosinusitis being the least common (0.5%). Pneumonia affected 61.90% of the patients admitted to the hospital. Furthermore, 8.73% got acute respiratory distress syndrome (ARDS), and 7.25% acquired pneumonia and Acute ARDS simultaneously. Co-morbidities were significantly correlated with complications developed during COVID-19.

Conclusion

Hypertension and diabetes mellitus were two of the most common symptoms observed. Clinical symptoms, comorbidities, and complications are higher in female patients compared to male patients and most of the patients’ developed complications.

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Longitudinal data of hospitalized COVID-19 patient is beneficial for future pandemics.

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Comorbidities is key factor in any kind of outbreaks, in our study, females were at more risk of developing complications.

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Comorbidities can differ regarding rates of hospitalization and infection.

Factors associated with differential COVID-19 mortality rates in the SEAR nations: a narrative review

Objectives

Since December 2019, the world has been grappling with the COVID-19 pandemic, which has caused severe loss of lives, the breakdown of health infrastructure, and disruption of the global economy. There is growing evidence on mortality patterns in high-income countries. However, similar evidence from low/middle-income nations is lacking. Our review aimed to describe COVID-19 mortality patterns in the WHO-SEAR nations, and explore the associated factors in order to explain such trends.

Methods

A systematic and comprehensive search was undertaken in PubMed and Google Scholar to obtain maximum hits on COVID-19 mortality and its determinants in the SEAR, using a combination of MeSH terms and Boolean operators. The data were narratively synthesized in detail under appropriate themes.

Results

Our search identified 6411 unique records. Mortality patterns were described in terms of important demographical and epidemiological indicators. Gaps in available evidence and paucity of adequate research in this area were also highlighted.

Conclusions

This review examined significant contributors to COVID-19 mortality across SEAR nations, while emphasizing issues relating to insufficient studies and data quality, and reporting challenges and other concerns in resource-constrained settings. There is a compelling need for more work in this area, to help inform decision making and improve public-health response.

Clinical Manifestations of Hospitalized COVID-19 Patients in Bangladesh: A 14-day Observational Study

Objectives

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is currently a significant public health concern and causing a pandemic in the world. Despite immense attention to the coronavirus disease 2019 (COVID-19), very little attention has been given to the kinetics of disease progression in infected patients. Therefore, in this study, we present a 14-day clinical observation of hospital-admitted COVID-19 patients.

Methods

After recording the demography of 42 COVID-19 patients on day 1, we observed the clinical progression for 14 days by investigating the hematological and biochemical responses of patients' blood and serum, respectively.

Results

Approximately, 62% of the hospital-admitted COVID-19 patients presented cough, followed by fever (∼52%). The top comorbidities of these patients were hypertension (30%) and diabetes mellitus (19%). The average blood hemoglobin (Hb) level was slightly low among the patients in the early days of infection and went up to the normal level on the later days. A substantial increase in the level of ALT or SGPT [up to 106 IU/L; standard error of the mean (SEM): 12.64] and AST or SGOT (up to 64.35 IU/L; SEM: 5.013) in COVID-19 patients was observed, which may suggest that infection with coronavirus is associated with the functionality of other organs of COVID-19 patients.

Conclusion

This 14-day observational study may help clinicians to decide the choice of treatment for COVID-19 patients.

How to cite this article

Shaha M, Islam MA, Huq F, et al. Clinical Manifestations of Hospitalized COVID-19 Patients in Bangladesh: A 14-day Observational Study. Euroasian J Hepato-Gastroenterol 2021;11(1):14-20.