Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Determinants of mortality in hypertensive patients admitted with COVID-19: a single-centre retrospective study at a tertiary hospital in South Africa

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic has significantly impacted global health, with successive outbreaks leading to substantial morbidity and mortality. Hypertension, a leading cause of cardiovascular disease globally, has been identified as a critical comorbidity in patients with severe COVID-19, exacerbating the risk of adverse outcomes. This study aimed to elucidate the impact of hypertension on COVID-19 outcomes within the South African context.

METHODS

A retrospective analysis was conducted at King Edward VIII Hospital, KwaZulu-Natal, South Africa, encompassing patients aged 13 years and above admitted with laboratory-confirmed SARS-CoV-2 infection between June 2020 and December 2021. The study investigated the association between hypertension and COVID-19 outcomes, analysing demographic, clinical, and laboratory data. Statistical analysis involved univariate and multivariate logistic regression to identify predictors of mortality among the hypertensive cohort.

RESULTS

The study included 420 participants-encompassing 205 with hypertension. Hypertensive patients demonstrated significantly greater requirements for oxygen and steroid therapy (p < 0.001), as well as higher mortality rates (44.88%, p < 0.001)) compared to their non-hypertensive counterparts. Key findings demonstrated that a lower oxygen saturation (adjusted odds ratio (aOR) 0.934, p = 0.006), higher pulse pressure (aOR 1.046, p = 0.021), elevated CRP (aOR 1.007, p = 0.004) and the necessity for mechanical ventilation (aOR 5.165, p = 0.004) were independent risk factors for mortality in hypertensive COVID-19 patients. Notably, the study highlighted the pronounced impact of hypertension-mediated organ damage (HMOD) on patient outcomes, with ischemic heart disease being significantly associated with increased mortality (aOR 8.712, p = 0.033).

CONCLUSION

Hypertension significantly exacerbates the severity and mortality risk of COVID-19 in the South African setting, underscoring the need for early identification and targeted management of hypertensive patients. This study contributes to the understanding of the interplay between hypertension and COVID-19 outcomes, emphasising the importance of considering comorbidities in the management and treatment strategies for COVID-19. Enhanced pandemic preparedness and healthcare resource allocation are crucial to mitigate the compounded risk presented by these concurrent health crises.

The laboratory parameters in predicting the severity and death of COVID-19 patients: Future pandemic readiness strategies

The range of clinical manifestations associated with the infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encompasses a broad spectrum, ranging from flu-like symptoms to the occurrence of multiple organ failure and death. The severity of the coronavirus disease 2019 (COVID-19) is categorized based on clinical presentation and is divided into three distinct levels of severity identified as non-severe, severe, and critical. Although individuals of all age groups are susceptible to SARS-CoV-2 infection, middle-aged and older adults are more frequently impacted, with the latter being more likely to develop severe illness. Various laboratory characteristics observed in hospitalized COVID-19 patients have been correlated with adverse outcomes. These include elevated levels of D-dimer, liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, prothrombin time, and troponin, as well as decreased lymphocyte and platelets counts. This review investigated the relationship between baseline clinical characteristics, initial laboratory parameters upon hospital admission, and the severity of illness and mortality rates among COVID-19 patients. Although the COVID-19 pandemic has concluded, understanding the laboratory predictors of virus severity and mortality remains crucial, and examining these predictors can have long-term effects. Such insights can help healthcare systems manage resources more effectively and deliver timely and appropriate care by identifying and targeting high-risk individuals. This knowledge can also help us better prepare for future pandemics. By examining these predictors, we can take steps to protect public health and mitigate the impact of future pandemics.

Risk factors for death in hospitalized COVID-19 patients in Africa: A systematic review and meta-analysis

BACKGROUND

The coronavirus disease 2019 has quickly spread worldwide since it first appeared in Wuhan, China, in late 2019. The most affected country in Africa was South Africa. This study aimed to identify the risk factors for death in hospitalized COVID-19 patients in Africa.

METHODS

We conducted a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We searched articles from the following database: PubMed, Embase, Cochrane Library, Medline, and COVID-19 Research Database. We used Google Scholar for gray literature. The language used in this article was English. The last search was conducted on January 15, 2023. Pooled HRs, or ORs, and 95% confidence intervals, were calculated separately to identify the risk factors for death in hospitalized COVID-19 patients. Heterogeneity was assessed by Cochran's Q statistic and the I2 test. The Egger test was used to assess publication bias. Subgroup analysis was performed to determine the source of heterogeneity. Data analysis was performed using Stata version 17. A P value < .05 was considered significant.

RESULTS

A total of 16,600 articles were obtained from the database search; finally, 16 articles met the inclusion criteria and were eligible for data extraction. The analysis revealed that the pooled prevalence of mortality in hospitalized COVID-19 patients was 13.9%. Advanced age was a significant risk factor for death in hospitalized COVID-19 patients, with the pooled coronavirus mortality HR and OR being 3.73 (95% CI: 2.27-5.19) and 1.04 (95% CI: 1.02-1.06), respectively. In addition, male gender (pOR 1.23; 95% CI: 1.07-1.40), patients with diabetes mellitus (DM) (pOR 1.26; 95% CI: 1.01-1.51), hypertension (HTN) (pOR 1.56; 95% CI: 1.27-1.85), chronic kidney disease (CKD) (pHR 5.43; 95% CI: 0.18-10.67), severe or critical conditions (pOR 9.04; 95% CI: 3.14-14.94) had a significantly increased risk of coronavirus-related mortality. The main limitations of the present study stem from the predominant use of published studies, which could introduce publication bias.

CONCLUSION

According to this study, advanced age, male gender, hypertension, diabetes mellitus, chronic kidney disease, and severe or critical condition were clinical risk factors associated with death outcomes in hospitalized COVID-19 patients in Africa.

Importance of Demographic and Clinical Features in Evaluating the Severity of COVID-19 in Hospitalized Patients: A Serbian Retrospective Study in the First Pandemic Year

The aim of this study is to determine the demographic and initial clinical characteristics of patients with COVID-19 and their importance in evaluating the severity of the disease. A retrospective study included patients suffering from COVID-19 who were hospitalized at The Department of Infectious Disease of the Clinical Hospital Center Pristina-Gracanica from the beginning of the pandemic until the end of 2020. We compared the symptoms of the disease, radiographic findings of pneumonia, laboratory parameters, duration of symptoms before admission, the difference in the need for certain therapies, and the presence of comorbidities between non-severe and severe groups of patients. Patients with a severe disease were statistically significantly older. Hypertension was significantly associated with severe clinical conditions. Radiographic findings of bilateral pneumonia on admission were much more frequent among the severe group, and these patients' need for oxygen support was significantly higher. Lower neutrophil and higher lymphocyte counts were statistically significant in the non-severe group. Biochemical parameters at admission also showed statistical significance between the examined groups. Based on our research, we can conclude that a complete overview of the patient, including demographic and laboratory parameters as perhaps the most significant attributes, can help doctors in the timely clinical assessment of patients and, thus, in the timely application of adequate therapeutic protocols in the treatment of COVID-19.

Treatment of severe COVID-19: an evolving paradigm

INTRODUCTION

The pathogenesis of severe COVID-19 is due, in part, to dysregulation of the human immune system in response to SARS-CoV-2 infection. Immune cells infected with SARS-CoV-2 can trigger a hyperinflammatory response of both the adaptive and innate immune system that has been associated with severe disease, hospitalization, and death, and better treatment options are urgently needed.

AREAS COVERED

A mainstay of therapy for COVID-19 involves an antiviral agent, remdesivir, in combination with a systemic corticosteroid, dexamethasone.

EXPERT OPINION

The addition of a second immunomodulator, such as an interleukin-6 inhibitor or a Janus kinase inhibitor, has been associated with clinical benefit in a subset of patients with moderate-to-severe disease, but their use remains controversial. This manuscript reviews what is known about the approach to treatment of severe COVID-19 and examines how immunomodulators such as infliximab and abatacept may alter clinical management and COVID-19 research in the years ahead based on the results of randomized, controlled trials.

A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2

Although the coronavirus epidemic spread rapidly with the Omicron variant, it lost its lethality rate with the effect of vaccine and immunity. The hospitalization and intense demand decreased. However, there is no definite information about when this disease will end or how dangerous the different variants could be. In addition, it is not possible to end the risk of variants that will continue to circulate among animals in nature. After this stage, drug-virus interactions should be examined in order to be able to prepare against possible new types of viruses and variants and to rapidly-produce drugs or vaccines against possible viruses. Despite experimental methods that are expensive, laborious, and time-consuming, geometric deep learning(GDL) is an alternative method that can be used to make this process faster and cheaper. In this study, we propose a new model based on geometric deep learning for the prediction of drug-virus interaction against COVID-19. First, we use the antiviral drug data in the SMILES molecular structure representation to generate too many features and better describe the structure of chemical species. Then the data is converted into a molecular representation and then into a graphical structure that the GDL model can understand. The node feature vectors are transferred to a different space with the Message Passing Neural Network (MPNN) for the training process to take place. We develop a geometric neural network architecture where the graph embedding values are passed through the fully connected layer and the prediction is actualized. The results indicate that the proposed method outperforms existing methods with 97% accuracy in predicting drug-virus interactions.