Comorbidity In COVID-19 Patients Research Articles

Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units.

Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)—hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys—asthma, cough and fever; class 3, mean age 74.5 ys—hypertension, diabetes and cough; class 4, mean age 67.8 ys—hypertension and no symptoms; class 5, mean age 53 ys—cough and no comorbidities; class 6, mean age 60 ys—without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson’s disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.

Factors Affecting the Outcomes of COVID-19 Patients Treated at Dr. M. Djamil Padang General Hospital

Background: COVID-19 has spread rapidly throughout the world with high morbidity and mortality. This study aimed to determine the factors that influenced the outcomes of COVID-19 patients treated at RSUP. Dr. M. Djamil Padang.Methods: This was an observational analytic study conducted with a retrospective cohort design on COVID-19 patients at RSUP Dr. M. Djamil Padang. Data were taken from medical records from January to March 2021. Association between comorbidities and the outcome of COVID-19 patients were analyzed using Chi- Square/Fisher Exact Test.Results: Majority of the patients were female (56.4%) and aged above 50 years (64.3%). Hypertension was the most common comorbidity (41.1%). Diabetes mellitus affected the final outcome of treatment. The number of comorbidities the patients had was associated with a worse outcome for COVID-19.Conclusion: Most of COVID-19 patients at RSUP Dr. M. Djamil Padang were male and more than 50 years old. There was a correlation between age, gender, and comorbidities in COVID-19 patients with the outcomes.

C-peptide levels and glycemic indices in COVID-19 patients

Type 2 diabetes (T2DM) is a common comorbidity in COVID-19 patients, which could favor disease progression. Our study aimed to evaluate the impact of some glycemic biomarkers on characteristic features of COVID-19. Electronic medical records of patients consecutively admitted to a COVID-19 ward were analyzed. Demographic and anthropometric as well as clinical and laboratory parameters were obtained. T2DM was present in 47% of the patients, with 22.58% of diabetic patients being newly diagnosed at admission. The most important predictors of severe COVID-19 were age (OR 1.214 [1.078-1.366], p = 0.001), creatinine levels (OR 1.018 [1.003-1.034], p = 0.017), glucose above 7.0 mmol/L at admission (OR 7.800 [2.232-27.255], p = 0.001) and HbA1c ≥ 6.5% (OR 4.840 [1.428-16.405], p = 0.011) irrespective of the presence of DM. C-peptide levels correlated positively with age, creatinine level, the severity of hypoxia and ferritin levels of patients (p < 0.05 for all) and appeared to be a significant predictor of leukocytosis. Suboptimal glycemic indices and impaired kidney function might predict COVID-19 disease worsening. Early detection of DM and optimization of glycemic control in diabetic patients should be among the priorities of the public health systems during COVID-19 pandemics.

Effect of Comorbid Diabetes on Clinical Characteristics of COVID-19 Patients Infected by the Wild-Type or Delta Variant of SARS-CoV-2.

BackgroundDiabetes is one of the most common comorbidities in COVID-19 patients that pertains to disease severity, but the causal mechanism regarding its negative impact on COVID-19 outcome has yet been uncovered.MethodsWe retrospectively analyzed 459 COVID-19 patients admitted in early 2020 and 336 COVID-19 patients admitted in August 2021, with their demographic information, medical history, vaccination status (if applied), and laboratory data reported.ResultsAmong COVID-19 patients, compared to the non-diabetic group, the diabetic group exhibited elder age, higher proportion of patients with other major comorbidities, more severe dysfunction of innate immune cells, more refractory blood coagulopathy and more detrimental organ damage. For the wild-type SARS-CoV-2 infection, diabetic comorbidity was associated with COVID-19 severity but not mortality, and the glycemic levels in the non-diabetic group upon infection experienced high and analogous to those in the diabetic group. Besides, infected by the delta variant of SARS-CoV-2, the non-diabetic patients did not demonstrate hyperglycemia, and despite different vaccination statuses, the diabetic patients exhibited comparable antibody responses to non-diabetic, showing the robustness of acquired immunity.ConclusionsSARS-CoV-2 infection may superimpose the deterioration of innate immune systems in diabetic patients, which contributes to their worsened disease outcome, but timely COVID-19 immunization could provide adequate protection in diabetic population that leads to favored prognosis.

2D MXenes for combatting COVID-19 Pandemic: A perspective on latest developments and innovations

The COVID-19 pandemic has adversely affected the world, causing enormous loss of lives. A greater impact on the economy was also observed worldwide. During the pandemic, the antimicrobial aprons, face masks, sterilizers, sensor processed touch-free sanitizers, and highly effective diagnostic devices having greater sensitivity and selectivity helped to foster the healthcare facilities. Furthermore, the research and development sectors are tackling this emergency with the rapid invention of vaccines and medicines. In this regard, two-dimensional (2D) nanomaterials are greatly explored to combat the extreme severity of the pandemic. Among the nanomaterials, the 2D MXene is a prospective element due to its unique properties like greater surface functionalities, enhanced conductivity, superior hydrophilicity, and excellent photocatalytic and/or photothermal properties. These unique properties of MXene can be utilized to fabricate face masks, PPE kits, face shields, and biomedical instruments like efficient biosensors having greater antiviral activities. MXenes can also cure comorbidities in COVID-19 patients and have high drug loading as well as controlled drug release capacity. Moreover, the remarkable biocompatibility of MXene adds a feather in its cap for diverse biomedical applications. This review briefly explains the different synthesis processes of 2D MXenes, their biocompatibility, cytotoxicity and antiviral features. In addition, this review also discusses the viral cycle of SARS-CoV-2 and its inactivation mechanism using MXene. Finally, various applications of MXene for combatting the COVID-19 pandemic and their future perspectives are discussed.

Measuring of D-dimer and other biochemical parameters as an early markers of severity in COVID-19 Patients with/without Diabetes Mellitus type II in Al Muthanna Province

Backgrounds: Since December 2019, when the coronavirus 2019 (Covid-19) first appeared in Wuhan and quickly spread throughout China, data on the clinical characteristics of the infected patients have been needed (Guan et al. 2020). Diabetes mellitus (DM) is a metabolic alteration characterized by chronic hyperglycemia. DM is considered one of the most common comorbidities in COVID-19 patients (Conget 2002). Aim: To identify the clinical variance between Covid-19 patients with and without DM (type II). Methods: The study contain 132 patients divided into four groups, there was 66 patients of them infected by COVID-19 with or without diabetic. The study was started from September 2021 to February 2022. Results: The results of the study appeared elevation the values of the most parameters measured for patients with/without diabetic such as D-dimer, ALT, AST, Blood Glucose, and Ferritin. But no change in Bilirubin and Alkaline phosphate (ALP). Conclusion: The COVID-19 patients with DM (type II) may be have the highest risk factors, which can increase the mortality, elevate the inflammation markers and affect the life enzymes (ALT, AST).

IDF21-0567 Analysis of mortality and cardio-metabolic comorbidities in Covid-19 patients from Eastern European region

Background: Individual cases of Covid-19 patients who had traveled from China were diagnosed in Europe in mid-February 2020 in several different western countries, like Italy, before the virus quickly began spreading. In the Eastern European countries, the first cases were detected in the end of February 2020 (in Croatia, North Macedonia, Romania), mostly as imported cases from the Western countries. Cardio-metabolic disorders, including diabetes and obesity are considered as high-impact pre-existing conditions and have been found to be predictors of mortality for SARS-CoV-2 infections.

Risk Factors for Mortality Among COVID-19 Patients in Asia: A Literature Review

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. This virus was first discovered in Wuhan, China, in December 2019, and was declared an international public health emergency by WHO on January 30, 2020. The Asian region, especially Southeast Asia, is the second-ranked region with the highest deaths of COVID-19 patients. This study aimed to determine risk factors for mortality among COVID-19 patients in the Asian region. This study was a literature review. The articles were searched using the keywords "death COVID-19 in Asia" and "mortality factor COVID-19 Asia" in the electronic databases of Science Direct and PubMed. Results of this review showed that there were two main factors of mortality among COVID-19 patients in Asia: individual characteristics and comorbidities. COVID-19 patients with older age had a higher risk of COVID-19 death. Comorbidities of COVID-19 patients that caused the most deaths in COVID-19 patients were hypertension, diabetes mellitus and heart disease.

Ethnicity-Specific Features of COVID-19 Among Arabs, Africans, South Asians, East Asians, and Caucasians in the United Arab Emirates.

BackgroundDubai (United Arab Emirates; UAE) has a multi-national population which makes it exceptionally interesting study sample because of its unique demographic factors.ObjectiveTo stratify the risk factors for the multinational society of the UAE.MethodsA retrospective chart review of 560 patients sequentially admitted to inpatient care with laboratory confirmed COVID-19 was conducted. We studied patients’ demographics, clinical features, laboratory results, disease severity, and outcomes. The parameters were compared across different ethnic groups using tree-based estimators to rank the ethnicity-specific disease features. We trained ML classification algorithms to build a model of ethnic specificity of COVID-19 based on clinical presentation and laboratory findings on admission.ResultsOut of 560 patients, 43.6% were South Asians, 26.4% Middle Easterns, 16.8% East Asians, 10.7% Caucasians, and 2.5% are under others. UAE nationals represented half of the Middle Eastern patients, and 13% of the entire cohort. Hypertension was the most common comorbidity in COVID-19 patients. Subjective complaint of fever and cough were the chief presenting symptoms. Two-thirds of the patients had either a mild disease or were asymptomatic. Only 20% of the entire cohort needed oxygen therapy, and 12% needed ICU admission. Forty patients (~7%) needed invasive ventilation and fifteen patients died (2.7%). We observed differences in disease severity among different ethnic groups. Caucasian or East-Asian COVID-19 patients tended to have a more severe disease despite a lower risk profile. In contrast to this, Middle Eastern COVID-19 patients had a higher risk factor profile, but they did not differ markedly in disease severity from the other ethnic groups. There was no noticeable difference between the Middle Eastern subethnicities—Arabs and Africans—in disease severity (p = 0.81). However, there were disparities in the SOFA score, D-dimer (p = 0.015), fibrinogen (p = 0.007), and background diseases (hypertension, p = 0.003; diabetes and smoking, p = 0.045) between the subethnicities.ConclusionWe observed variations in disease severity among different ethnic groups. The high accuracy (average AUC = 0.9586) of the ethnicity classification model based on the laboratory and clinical findings suggests the presence of ethnic-specific disease features. Larger studies are needed to explore the role of ethnicity in COVID-19 disease features.

Immunoregulatory Intestinal Microbiota and COVID-19 in Patients with Type Two Diabetes: A Double-Edged Sword.

Coronavirus disease 2019, or COVID-19, is a major challenge facing scientists worldwide. Alongside the lungs, the system of organs comprising the GI tract is commonly targeted by COVID-19. The dysbiotic modulations in the intestine influence the disease severity, potentially due to the ability of the intestinal microbiota to modulate T lymphocyte functions, i.e., to suppress or activate T cell subpopulations. The interplay between the lungs and intestinal microbiota is named the gut–lung axis. One of the most usual comorbidities in COVID-19 patients is type 2 diabetes, which induces changes in intestinal microbiota, resulting in a pro-inflammatory immune response, and consequently, a more severe course of COVID-19. However, changes in the microbiota in this comorbid pathology remain unclear. Metformin is used as a medication to treat type 2 diabetes. The use of the type 2 diabetes drug metformin is a promising treatment for this comorbidity because, in addition to its hypoglycemic action, it can increase amount of intestinal bacteria that induce regulatory T cell response. This dual activity of metformin can reduce lung damage and improve the course of the COVID-19 disease.

Differential COVID-19 Symptoms Given Pandemic Locations, Time, and Comorbidities During the Early Pandemic.

BackgroundCOVID-19 pandemic is disaster to public health worldwide. Better perspective on COVID's features early in its course–prior to the development of vaccines and widespread variants–may prove useful in the understanding of future pandemics. Ontology provides a standardized integrative method for knowledge modeling and computer-assisted reasoning. In this study, we systematically extracted and analyzed clinical phenotypes and comorbidities in COVID-19 patients found at different countries and regions during the early pandemic using an ontology-based bioinformatics approach, with the aim to identify new insights and hidden patterns of the COVID-19 symptoms.ResultsA total of 48 research articles reporting analysis of first-hand clinical data from over 40,000 COVID-19 patients were surveyed. The patients studied therein were diagnosed with COVID-19 before May 2020. A total of 18 commonly-occurring phenotypes in these COVID-19 patients were first identified and then classified into different hierarchical groups based on the Human Phenotype Ontology (HPO). This meta-analytic approach revealed that fever, cough, and the loss of smell and taste were ranked as the most commonly-occurring phenotype in China, the US, and Italy, respectively. We also found that the patients from Europe and the US appeared to have more frequent occurrence of many nervous and abdominal symptom phenotypes (e.g., loss of smell, loss of taste, and diarrhea) than patients from China during the early pandemic. A total of 22 comorbidities, such as diabetes and kidney failure, were found to commonly exist in COVID-19 patients and positively correlated with the severity of the disease. The knowledge learned from the study was further modeled and represented in the Coronavirus Infectious Disease Ontology (CIDO), supporting semantic queries and analysis. Furthermore, also considering the symptoms caused by new viral variants at the later stages, a spiral model hypothesis was proposed to address the changes of specific symptoms during different stages of the pandemic.ConclusionsDifferential patterns of symptoms in COVID-19 patients were found given different locations, time, and comorbidity types during the early pandemic. The ontology-based informatics provides a unique approach to systematically model, represent, and analyze COVID-19 symptoms, comorbidities, and the factors that influence the disease outcomes.

Clinical Profile of COVID-19 Patients in Nangarhar University Teaching Hospital

Background: The coronavirus COVID-19, causing severe acute respiratory syndrome and patients’ mortality in considerable proportion, has affected 220 countries. There was no description of the clinical profiles such as demography (sex, age), and co-morbidities of COVID-19 patients in Eastern Region of Afghanistan, comprising Nangarhar, Nuristan, Kunar, and Laghman provinces. A vast majority of patients from these areas refer to Nangarhar University Teaching Hospital for health issues. Therefore, the researchers compiled a descriptive profile of the patients from this particular area. Materials and Methods: It was a descriptive record based study of Medical Ward, Nangarhar University Teaching Hospital. The number of profiled patients was 50, with age over 18 years old. The patient's samples were sent to Nangarhar Public Health Hospital for confirmation in Real-Time Reverse Transcriptase Polymerase Chain Reaction Assay for SARS-CoV-2 examination. Findings: Out of 50 COVID-19 affected patients, 68% were females and 32% were males. The mean age was (53±17). According to the clinical profiles, 8% had no symptoms and most common ones were fever (80%), cough (60%) and dyspnea (10%). The major comorbidities were respiratory disease (56%), hypertension (38%), Diabetes Mellitus (16%), Heart Failure (12%), obesity (10%) and chronic kidney disease (4%). Conclusion: The study concluded that the event was more common in females and aged persons than males and young patients. Fever was obviously common among all the identified patients. COVID-19 was severe in patients with respiratory diseases and hypertension.

Does the Charlson comorbidity index help predict the risk of death in COVID-19 patients?

Objective:Comorbidities are diseases that coexist with a disease of interest or an index disease, which can directly affect the prognosis of the disease of interest or indirectly affect the choice of treatment. The Charlson comorbidity index (CCI) is the most widely used comorbidity index. In this study, it was aimed to examine the predictive role of the CCI score on the mortality of patients with COVID-19.Methods:We have retrospectively analyzed COVID-19 patients whose diagnosis was confirmed by PCR and who were hospitalized in two centers between April 2020 and December 2020. The severity of comorbidity of the patients was categorized into five groups according to the CCI score: CCI score 0, CCI score 1–2, CCI score 3–4, CCI score 5–6, and CCI score ≥7. Factors affecting mortality and differences between groups classified by CCI were determined by logistic regression analysis and one-way analysis of variance.Results:A total of 1,559 COVID-19 patients were included in the study and 70 (4.49%) patients had deceased. Half of the study population (n=793, 50.9%) had different comorbidities. The CCI score was 3.8±2.7 in deceased patients and 1.3±1.9 in surviving individuals. There was a positive correlation between CCI scores and mortality in COVID-19 patients, with each point increase in the CCI score increasing the risk of death by 2.5%. CCI score of 4 and above predicted mortality with 87.2% sensitivity and 97.9% negative predictive value. Five (0.6%) of 766 patients with CCI scores of 0, 16 (3.6%) of 439 patients with CCI scores of 1–2, 13 (6.9%) of 189 patients with CCI scores of 3–4, and a CCI score of 5, 13 (15.7%) of 83 patients with -6 and 23 (28.0%) of 82 patients with a CCI score of ≥7 died.Conclusion:CCI is a simple, easily applicable, and valid method for classifying comorbidities and estimating COVID-19 mortality. The close relationship between the CCI score and mortality reveals the reality of how important vaccination is, especially in this group of patients. Increasing awareness of potential comorbidities in COVID-19 patients can provide insight into the disease and to improve outcomes by identifying and treating patients earlier and more effectively.

Intervensi Keperawatan pada Pasien Terkonfirmasi COVID-19 dengan Komorbid DM Tipe 2 dan Hipertensi: Studi Kasus

Comorbidities in COVID-19 patients with the highest percentage are hypertension and diabetes mellitus. Patients with comorbid hypertension and DM have high morbidity and mortality. This study aims to determine the interventions in COVID-19 patients with comorbid DM and hypertension. The method used is a case study with data collection through patient and family interviews, medical record studies and direct observation or through CCTV. The studies showed that the patient was diagnosed with severe COVID-19 by oxygen therapy (NRM 15 L/min), treating COVID-19 and managing comorbid hypertension and DM with medication and monitoring and reducing anxiety. During treatment, the patient was desaturated, blood pressure and blood sugar level had not been controlled, and the anxiety was quite high. It can be said that interventions that can be given to COVID-19 patients with comorbid hypertension and DM are oxygen therapy, medication, anxiety reduction, and comorbid management. Comorbid hypertension and DM were controlled with medication and regular monitoring. the patient's blood pressure and blood sugar level had not been controlled, it is possible that the patient's anxiety is still experienced. So that as health workers in providing care are expected to pay attention to the biopsychosociospiritual aspects so that the patient's problems are resolved.

The effect of obesity on in-hospital mortality among patients with COVID-19 receiving corticosteroids

Background and aimsObesity has been reported to be one of the most frequent comorbidities in COVID-19 patients and associated with higher rates of in-hospital mortality compared to non-obese patients. Acute kidney injury (AKI) is also known to be a complication associated with obesity in critically-ill COVID-19 patients. We aimed to investigate whether obesity was associated with increased risk of in-hospital mortality and AKI among patients with COVID-19 treated with corticosteroids. MethodsWe utilized 9965 hospitalized COVID-19 patient data and divided patients who were treated with corticosteroids into 6 groups by body mass index (BMI) (less than 18.5, 18.5–25, 25–30, 30–35, 35–40, 40 kg/m2 or greater). The association between BMI and in-hospital mortality and between BMI and incidence rate of AKI during admission among COVID-19 patients receiving corticosteroids were retrospectively investigated. ResultsThere were 4587 study participants receiving corticosteroids (mean age 66.5 ± 15.5 years, men 56.6%, mean BMI 29.0 ± 7.2 kg/m2). The smooth spline curve suggested a J-shape association between BMI and in-hospital mortality. Patients with BMI above 40 kg/m2 exhibited a higher in-hospital mortality and higher incidence rate of AKI during admission compared to patients with BMI between 25 and 30 kg/m2. The differences in in-hospital mortality and the rate of AKI were larger among patients with severe COVID-19. ConclusionsClass III obesity was associated with high in-hospital mortality and AKI in patients with COVID-19 treated by corticosteroids. Clinicians must stay vigilant on the impact of class III obesity and development of AKI to disease trajectory of COVID-19 patients.

Case Report: Rhino-orbital Mucormycosis Related to COVID-19: A Case Series Exploring Risk Factors

ABSTRACT.There has been a surge of rhino-orbital mucormycosis cases in India in the wake of the second wave of the COVID-19 pandemic. It has been widely suggested that dysglycemia resulting from diabetes which is a common comorbidity in COVID-19 patients, and indiscriminate steroid use has resulted in this surge. We report a series of 13 cases of rhino-orbital mucormycosis in COVID-19 patients admitted to our center between mid-April and early June 2021. The cases showed a male preponderance, two patients had loss of vision, and four of them showed intracranial extension of disease. Twelve patients had received steroids and 12 had preexisting or newly diagnosed diabetes, both steroid use and diabetes being the most common identified risk factors. Considering other possible risk factors, immunosuppressed state, antiviral or ayurvedic (Indian traditional) medications, and oxygen therapy were not associated with a definite risk of mucormycosis, because they were not present uniformly in the patients. We propose that COVID-19 itself, through molecular mechanisms, predisposes to mucormycosis, with other factors such as dysglycemia or steroid use increasing the risk.

Prognosis of COVID-19 patients with type 2 diabetes

Background: The pandemic of COVID-19, a disease caused by novel coronavirus SARS-CoV-2, is associated with significant morbidity and mortality. Recent data showed that hypertension, diabetes mellitus, cardiovascular diseases, and chronic obstructive pulmonary disease were the most prevalent comorbidities in COVID-19 patients. Additionally, data indicate that hypertension, diabetes and cardiovascular diseases are important risk factors for progression and unfavourable outcome in COVID-19 patients. Poorly controlled Type 2 diabetes mellitus was associated with severe progression of disease. Hence, large studies with comprehensive analysis of all risk factors and longer follow-up are necessary. Methods and analysis: A single-centre retrospective cross-sectional study of 300 patients that were SARS-CoV-2 positive from May to October 2020 was done. Data was entered into Microsoft excel data sheet and was analysed using SPSS 22 version software. Chi-square test or Fischer’s exact test (for 2x2 tables only) was used as test of significance for qualitative data. Independent t test was used as test of significance to identify the mean difference between two quantitative variables. ANOVA (Analysis of Variance) was used as test of significance to identify the mean difference between more than two quantitative variables. P value (Probability that the result is true) of &lt;0.05 was considered as statistically significant after assuming all the rules of statistical tests. Data collected was analyzed in a group of patients who were sub divided into 2 groups diabetics and Non diabetics. The parameters studied included laboratory markers: D dimer, CRP, Lactate Dehydrogenase, Ferritin levels. To assess the glycemic control HbA1C levels were assessed. The outcome parameters considered were Oxygen requirement, assessing the requirement of intensive care and duration of stay in the hospital Conclusion: The patients with higher HbA1c values were found to have higher CRP and D-dimer values and required ICU shift and prolonged hospital stay. Hence, good control of diabetes will reduce the worsening of disease. In a country like India, where most of the population do not have health insurance cover proper control of diabetes, can reduce the burden on family. Hence, large studies with comprehensive analysis of all risk factors and longer follow-up are necessary.

Characteristics and severity of COVID-19 among Sudanese patients during the waves of the pandemic

COVID-19 is associated with marked morbidity and mortality. COVID-19 symptoms ranged from mild symptoms to severe pneumonia requiring admission to intensive care unit. Imperial College reported that, this second wave in Khartoum is expected to be similar to the first wave. The aim of our study was to study the characteristics and severity of COVID-19 among Sudanese patients during the waves of the pandemic.A hospital-based analytical Cross-Sectional study assessed the characteristics of COVID-19 patients who attended Imperial Hospital since November 2020, Khartoum, Sudan. Data were collected, retrospectively, from the medical records of COVID-19 patients. The characteristics of the patients were recorded. The statistical package for social sciences (SPSS-23) was used to describe and analyze the data. Descriptive statistics and chi-square test were used; tests were considered statistically significant when p < 0.05.Of the 138 participants, 75.4% were aged above 60 years. Males were 61.6%. Severe cases were 37.7%, with higher rate in the third wave (p = 0.000). Duration of hospitalization was ≤ 5 days in 60.9% of the cases, majority were from the 2nd wave (p = 0.001). Comorbidities of COVID-19 patients were more among non-severe cases (p = 0.001). COVID-19 symptoms were more prevalent in non-severe cases. Complications were more in severe cases (p = 0.000). Odds ratio of having ischemic stroke was 6.833 [95%CI: 2.092–22.321] based on COVID-19 severity.The elderly population were the majority. More males were getting admitted. Atypical symptoms were reported beside the common symptoms. Wider multi-center studies are recommended in order to complete the big picture of COVID-19 in Sudan.

COVID-19 in Multimorbid Patients-A Controlled Microcost Description Analysis of Diagnosis Related Group (DRG)-Case Series in Acute Care without Non-Invasive Ventilation.

Diagnosis-related cost analyzes are important for health economic planning and decision-making. They form the basis for further developing of remuneration systems for health services. The rapid increase in hospital stays by COVID-19 patients requires a valid and exact calculation of the treatment costs. COVID-19 patients with many accompanying illnesses increase the requirements for a cost calculation. The focus of this work is to carry out a DRG-related micro-cost analysis, considering the age, length of stay and comorbidities of COVID-19 patients. So far, there is little information about treatment costs for multimorbid patients with COVID-19 who have not received invasive ventilation. The method is based on a standardized cost unit calculation for determining the treatment costs in a German hospital. The costs (€) of inpatients treated with COVID-19 were compared with a control group of the same DRGs of patients without COVID-19. The average total costs for inpatient treatment were €2866. The highest share of costs falls on nursing, personnel, and material costs of the non-medical infrastructure. Frequent comorbidities were heart failure, diabetes mellitus, other respiratory diseases, dizziness, and impairment of the musculoskeletal system.

Association of Vitamin D receptor gene polymorphisms and clinical/severe outcomes of COVID-19 patients.

IntroductionGrowing evidence documented the critical impacts of vitamin D (VD) in the prognosis of COVID-19 patients. The functions of VD are dependent on the vitamin D receptor (VDR) in the VD/VDR signaling pathway. Therefore, we aimed to assess the association of VDR gene polymorphisms with COVID-19 outcomes.MethodsIn the present study, eight VDR single nucleotide polymorphisms (SNPs) were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 500 COVID-19 patients in Iran, including 160 asymptomatic, 250 mild/moderate, and 90 severe/critical cases. The association of these polymorphisms with severity, clinical outcomes, and comorbidities were evaluated through the calculation of the Odds ratio (OR).ResultsInterestingly, significant associations were disclosed for some of the SNP-related alleles and/or genotypes in one or more genetic models with different clinical data in COVID-19 patients. Significant association of VDR-SNPs with signs, symptoms, and comorbidities was as follows: ApaI with shortness of breath (P ˂ 0.001) and asthma (P = 0.034) in severe/critical patients (group III); BsmI with chronic renal disease (P = 0.010) in mild/moderate patients (group II); Tru9I with vomiting (P = 0.031), shortness of breath (P = 0.04), and hypertension (P = 0.030); FokI with fever and hypertension (P = 0.027) in severe/critical patients (group III); CDX2 with shortness of breath (P = 0.022), hypertension (P = 0.036), and diabetes (P = 0.042) in severe/critical patients (group III); EcoRV with diabetes (P ˂ 0.001 and P = 0.045 in mild/moderate patients (group II) and severe/critical patients (group III), respectively). However, the association of VDR TaqI and BglI polymorphisms with clinical symptoms and comorbidities in COVID-19 patients was not significant.ConclusionVDR gene polymorphisms might play critical roles in the vulnerability to infection and severity of COVID-19, probably by altering the risk of comorbidities. However, these results require further validation in larger studies with different ethnicities and geographical regions.