Prognostic Significance of Urinary Biomarkers in Patients Hospitalized With COVID-19.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is common in patients with coronavirus disease 2019 (COVID-19) and associated with poor outcomes. Urinary biomarkers have been associated with adverse kidney outcomes in other settings and may provide additional prognostic information in patients with COVID-19. We investigated the association between urinary biomarkers and adverse kidney outcomes among patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Patients hospitalized with COVID-19 (n=153) at 2 academic medical centers between April and June 2020. EXPOSURE: 19 urinary biomarkers of injury, inflammation, and repair. OUTCOME: Composite of KDIGO (Kidney Disease: Improving Global Outcomes) stage 3 AKI, requirement for dialysis, or death within 60 days of hospital admission. We also compared various kidney biomarker levels in the setting of COVID-19 versus other common AKI settings. ANALYTICAL APPROACH: Time-varying Cox proportional hazards regression to associate biomarker level with composite outcome. RESULTS: Out of 153 patients, 24 (15.7%) experienced the primary outcome. Twofold higher levels of neutrophil gelatinase-associated lipocalin (NGAL) (HR, 1.34 [95% CI, 1.14-1.57]), monocyte chemoattractant protein (MCP-1) (HR, 1.42 [95% CI, 1.09-1.84]), and kidney injury molecule 1 (KIM-1) (HR, 2.03 [95% CI, 1.38-2.99]) were associated with highest risk of sustaining primary composite outcome. Higher epidermal growth factor (EGF) levels were associated with a lower risk of the primary outcome (HR, 0.61 [95% CI, 0.47-0.79]). Individual biomarkers provided moderate discrimination and biomarker combinations improved discrimination for the primary outcome. The degree of kidney injury by biomarker level in COVID-19 was comparable to other settings of clinical AKI. There was evidence of subclinical AKI in COVID-19 patients based on elevated injury biomarker level in patients without clinical AKI defined by serum creatinine. LIMITATIONS: Small sample size with low number of composite outcome events. CONCLUSIONS: Urinary biomarkers are associated with adverse kidney outcomes in patients hospitalized with COVID-19 and may provide valuable information to monitor kidney disease progression and recovery.

Elevated HbA1c levels in pre-Covid-19 infection increases the risk of mortality: A sistematic review and meta-analysis.

AIMS: Diabetes is emerging as a risk factor for coronavirus disease (COVID)-19 prognosis. However, contradictory findings have been reported regarding the impact of glycaemic control on COVID-19 outcome. The aim of this meta-analysis was to explore the impact of hospital pre-admission or at-admission values of HbA1c on COVID-19 mortality or worsening in patients with diabetes. MATERIALS AND METHODS: We searched PubMed, Embase and Scopus up to 30th December 2020. Eligibility criteria for study selection were the following: (1)enrolling patients with any form of diabetes mellitus and hospitalized for COVID-19 and (2) reporting data regarding HbA1c values before infection or at hospital admission in relation to COVID-19 mortality or worsening. Descriptive statistics, HbA1c values, odds ratios (ORs) and hazard ratios were extracted from seven observational studies and generic inverse variance (random effects) of OR was used to estimate the effect of HbA1c on COVID-19 outcome. RESULTS: HbA1c was linearly associated with an increased COVID-19 mortality or worsening when considered as a continuous variable (OR 1.01 [1.01, 1.01]; p < 0.00001). Similarly, when analysing studies providing the number of events according to the degree of glycaemic control among various strata, a significantly increased risk was observed with poor glycaemic control (OR 1.15 [1.11, 1.19]; p < 0.00001), a result corroborated by sensitivity analysis. CONCLUSIONS: Notwithstanding the large heterogeneity in study design and patients' characteristics in the few available studies, data suggest that patients with diabetes and poor glycaemic control before infection might have an increased risk of COVID-19 related mortality.

[Anticoagulation in coronavirus disease 2019 (COVID-19): confirmed and controversial aspects]. / Antikoagulation bei "coronavirus disease 2019" (COVID-19) ­ Gesichertes und Kontroverses.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with a high risk of microvascular immunothrombosis as well as symptomatic and incidental thromboembolisms, predominantly in the venous system but also in the arterial system. This explains among other things the high cardiovascular morbidity and mortality of the patients. The present state of knowledge on the pathophysiology of immunothrombosis and the strategies of anticoagulation in patients with coronavirus disease 2019 (COVID-19) are summarized and illuminated in this article. According to the current guidelines moderately to severely ill patients who are being treated in hospital should receive thrombosis prophylaxis with low molecular weight or unfractionated heparin or alternatively with fondaparinux, as long as there is no clearly increased risk of bleeding. Apart from the established indications for treatment, an intensified or therapeutic dose prophylaxis should be considered very cautiously in these critically ill patients, also due to the increased bleeding complications. The routine continuation of prophylactic anticoagulation after discharge from hospital is currently not recommended.

The prognostic significance of vitamin D deficiency in patients with COVID-19 pneumonia.

BACKGROUND: Vitamin D has anti-inflammatory and immunomodulatory effects via the downregulation of pro-inflammatory cytokines. We aimed to demonstrate the effect of vitamin D levels on survival in COVID-19 patients. MATERIALS AND METHODS: 207 COVID-19 patients were included in the study. Serum vitamin D levels were measured, and patients with levels <20 ng/ml or 21 to 30 ng received a single 300.000 IU dose of vitamin D. RESULTS: Of 207 patients, 37 received vitamin D, while 170 did not. Demographic, radiologic and mean laboratory values were similar between the groups. The mean plasma vitamin D level without vitamin D support (n=170) was 50.82±16.12 ng/ml (30.28-81.35) vs. 16.98±6.2 ng/ml (4.20-28.30) in vitamin D group. The most remarkable finding were the mortality rates; while only 1 patient (2.7 %) died in the vitamin D group, 24 patients (14.1 %) died in no vitamin D supplementation group (p=0.038). CONCLUSION: Although a few retrospective studies put forth a relation between vitamin D deficiency and COVID-19 course severity there is still paucity of data about the efficacy of vitamin supplementations in COVID-19 patients. A single 300.000 IU dose of vitamin D seems to represent a useful, practical, and safe adjunctive approach for the treatment or prevention of COVID-19 (Tab. 1, Fig. 1, Ref. 30).

Dynamic CT assessment of disease change and prognosis of patients with moderate COVID-19 pneumonia.

OBJECTIVES: To assess prognosis or dynamic change from initial diagnosis until recovery of the patients with moderate coronavirus disease (COVID-19) pneumonia using chest CT images. MATERIALS AND METHODS: In this retrospective study, 33 patients (18 men, 15 women; median age, 49.0 years) with confirmed with moderate COVID-19 pneumonia in a multicenter hospital were included. The patients underwent at least four chest non-contrast-enhanced computed tomography (CT) scans at approximately 5-day intervals. We analyzed the clinical and CT characteristics of the patients. Moreover, the total CT score and the sum of lung involvement were determined for every CT scan. RESULTS: The most widespread presenting symptoms were fever (32/33, 97.0%) and cough (17/33, 51.5%), which were often accompanied by decreased lymphocyte count (15/33, 45.5%) and increased C-reactive protein levels (18/33, 54.6%). Bilateral, multifocal ground glass opacities (32/33, 97.0%), consolidation (25/33, 75.8%), vascular thickening (23/33, 69.7%), and bronchial wall thickening (21/33, 63.6%) with peripheral distribution were the most frequent CT findings during moderate COVID-19 pneumonia. In patients recovering from moderate COVID-19 pneumonia, four stages (stages 1-4) of evolution were identified on chest CT with average CT scores of 3.4±2.3, 6.0±4.4, 5.6±3.8, and 4.9±3.2, respectively, from the onset of symptoms. For most patients, the peak of average total CT score increased for approximately 8 days after the onset of symptoms, after which it decreased gradually. The mean CT score of all patients was 4.7 at the time of discharge. CONCLUSION: The moderate COVID-19 pneumonia CT score increased rapidly in a short period of time initially, followed by a slow decline over a relatively long time. The peak of the course occurred in stage 2. Complete recovery of patients with moderate COVID-19 pneumonia with high mean CT score at the time of discharge requires longer time.

Acute Myocardial Infarction in COVID-19 Patients-A Review of Literature Data and Two-Case Report Series.

Background/Objectives: The newly emergent COVID-19 pandemic involved primarily the respiratory system and had also major cardiovascular system (CVS) implications, revealed by acute myocardial infarction (AMI), arrhythmias, myocardial injury, and thromboembolism. CVS involvement is done through main mechanisms-direct and indirect heart muscle injury, with high mortality rates, worse short-term outcomes, and severe complications. AMI is the echo of myocardial injury (revealed by increases in CK, CK-MB, and troponin serum markers-which are taken into consideration as possible COVID-19 risk stratification markers). When studying myocardial injury, physicians can make use of imaging studies, such as cardiac MRI, transthoracic (or transesophageal) echocardiography, coronary angiography, cardiac computed tomography, and nuclear imaging (which have been used in cases where angiography was not possible), or even endomyocardial biopsy (which is not always available or feasible). Two-case-series presentations: We present the cases of two COVID-19 positive male patients who were admitted into the Clinical Department of Cardiology in "Sfântul Apostol Andrei" Emergency Clinical Hospital of Galați (Romania), who presented with acute cardiac distress symptoms and have been diagnosed with ST elevation AMI. The patients were 82 and 57 years old, respectively, with moderate and severe forms of COVID-19, and were diagnosed with anteroseptal left ventricular AMI and extensive anterior transmural left ventricular AMI (with ventricular fibrillation at presentation), respectively. The first patient was a non-smoker and non-drinker with no associated comorbidities, and was later discharged, while the second one died due to AMI complications. Conclusions: From this two-case series, we extract the following: old age alone is not a significant risk factor for adverse outcomes in COVID-19-related CVS events, and that the cumulative effects of several patient-associated risk factors (be it either for severe forms of COVID-19 and/or acute cardiac injury) will most probably lead to poor patient prognosis (death). At the same time, serum cardiac enzymes, dynamic ECG changes, along with newly developed echocardiographic modifications are indicators for poor prognosis in acute cardiac injury in COVID-19 patients with acute myocardial injury, regardless of the presence of right ventricular dysfunction (due to pulmonary hypertension).

Altered Plasma microRNA Signature in Hospitalized COVID-19 Patients Requiring Oxygen Support.

To discover potential micro(mi)RNA biomarkers of SARS-CoV-2 infection and disease progression, large-scale deep-sequencing analysis of small RNA expression was performed on plasma samples from 40 patients hospitalized for SARS-CoV-2 infection (median 13.50 [IQR 9-24] days since symptoms initiation) and 21 healthy noninfected individuals. A total of 1218 different miRNAs were identified. When compared with healthy noninfected donors, SARS-CoV-2-infected patients showed significantly (fold change [FC] > 1.2 and adjusted p [padj] < 0.05) altered expression of 190 miRNAs. The top-10 differentially expressed (DE) miRNAs were miR-122-5p, let-7b-5p, miR-146a-5p, miR-342-3p, miR-146b-5p, miR-629-5p, miR-24-3p, miR-12136, let-7a-5p, and miR-191-5p, which displayed FC and padj values ranging from 153 to 5 and 2.51 × 10-32 to 2.21 × 10-21, respectively, which unequivocally diagnosed SARS-CoV-2 infection. No differences in blood cell counts and biochemical plasma parameters, including interleukin 6, ferritin, and D-dimer, were observed between COVID-19 patients on high-flow oxygen therapy, low-flow oxygen therapy, or not requiring oxygen therapy. Notably, 31 significantly deregulated miRNAs were found, when patients on high- and low-flow oxygen therapy were compared. SARS-CoV-2 infection generates a specific miRNA signature in hospitalized patients. Specific miRNA profiles are associated with COVID-19 prognosis in patients requiring oxygen flow.

Predictive biomarkers of COVID-19 prognosis identified in Bangladesh patients and validated in Japanese cohorts.

Despite high vaccination rates globally, countries are still grappling with new COVID infections, and patients diagnosed as mild dying at home during outpatient treatment. Hence, this study aim to identify, then validate, biomarkers that could predict if newly infected COVID-19 patients would subsequently require hospitalization or could recover safely with medication as outpatients. Serum cytokine/chemokine data from 129 COVID-19 patients within 7 days after the onset of symptoms in Bangladesh were used as training data. The majority of patients were infected with the Omicron variant and over 88% were vaccinated. Patients were divided into those with mild symptoms who recovered, and those who deteriorated to moderate or severe illness. Using the Lasso method, 15 predictive markers were identified and used to classify patients into these two groups. The biomarkers were then validated in a cohort of 194 Covid patients in Japan with a predictive accuracy that exceeded 80% for patients infected with Delta and Omicron variants, and 70% for Wuhan and Alpha variants. In an environment of widespread vaccination, these biomarkers could help medical practitioners determine if newly infected COVID-19 patients will improve and can be managed on an out-patient basis, or if they will deteriorate and require hospitalization.

Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing.

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

Utility of plasma nucleocapsid protein in predicting severity and prognosis in severe COVID-19 patients with comorbidities.

OBJECTIVES: The COVID-19 pandemic poses ongoing challenges to global public health systems, emphasizing the critical necessity for efficient diagnostic and prognostic markers. This study evaluates the MAGLUMI® SARS-CoV-2 Ag N protein chemiluminescent immunoassay (MAG-CLIA) for its analytical performance and its role in predicting disease severity and prognosis among severe COVID-19 patients with comorbidities. METHODS: Analytical validation of plasma MAG-CLIA SARS-CoV-2 Ag N protein encompassed precision, interference, LoQ and linearity. Plasma N protein concentrations and other biomarkers were measured within 48 h of admission, tracked until discharge or death. The Mann-Whitney U test explored the association between plasma N protein and COVID-19 severity or prognosis. Longitudinal monitoring of plasma N protein dynamics was conducted in representative patients. RESULTS: MAG-CLIA demonstrated precise quantification of plasma N protein with a CV below 10 % and minimal interference. The LoQ was 0.88 ng/L, with a broad linear range. Plasma N protein showed high diagnostic accuracy for COVID-19, achieving 95.42 % specificity and 78.32 % sensitivity at 2.388 ng/L. Plasma N protein emerged as a valuable prognostic indicator, correlating with mechanical ventilation need and patient survival. Plasma N protein concentrations ≥ 424.3 ng/L (AUC 0.8102, sensitivity 78.38 %, specificity 85.48 %) were associated with poor prognosis in severe COVID-19 patients with comorbidities. CONCLUSIONS: MAG-CLIA's SARS-CoV-2 N protein detection in plasma demonstrates both analytical reliability and clinical relevance in our inaugural evaluation. As a promising prognostic biomarker for severe COVID-19 patients, it offers crucial insights into disease severity and progression, emphasizing the significance of early monitoring and intervention, especially for patients with comorbidities.

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study.

INTRODUCTION: In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. METHODS: This comparative study was performed in 515 patients in the Maria Sklodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. RESULTS: We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. CONCLUSIONS: Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images.

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.

Prognostic value of radiological index and clinical data in patients with COVID-19 infection.

During the Coronavirus-19 pandemic, chest X-ray scoring system have been validated by Al-Smadi and Toussie in this group of patients and even RALE score, previously designed for ARDS, have been used to estimate correlation with mortality. The aim of this study was to evaluate the prognostic value of As-Smadi, Tuossie and RALE scores in predicting death in the same population of patients when associated to clinical data. In this retrospective clinical study, data of patients with COVID-19, admitted to our hospital from 1st October 2020 to 31st December 2020 were collected. CXR images of each patient were analyzed with the three different scores above mentioned. 144 patients (male 96 aged 68.5 years) were included in the study. 93 patients reported a least 1 comorbidity and 36 died. The association with increasing age, presence of comorbidities, and lower hemoglobin was significantly associated with risk of death for all the regression models. When considering the radiological score, a significant effect was found for the Al Smadi and RALE scores, while no evidence of association was found for the Toussie score. The fraction of new information is 16.7% for the Al Smadi score, 12.9% for the RALE and 5.1% for the Toussie score. The improvement in the prognostic usefulness with respect to the base model is particularly interesting for the Al Smadi score. The highest c-index was also obtained by the model with the Al Smadi score.

A Comprehensive Review of Machine Learning Used to Combat COVID-19.

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

Physiological health indexes predict deterioration and mortality in patients with COVID-19: a comparative study.

Old age is a crucial risk factor for severe coronavirus disease 2019 (COVID-19), with serious or fatal outcomes disproportionately affecting older adults compared with the rest of the population. We proposed that the physiological health status and biological age, beyond the chronological age itself, could be the driving trends affecting COVID-19 severity and mortality. A total of 155 participants hospitalized with confirmed COVID-19 aged 26-94 years were recruited for the study. Four different physiological summary indices were calculated: Klemera and Doubal's biological age, PhenoAge, physiological dysregulation (PD; globally and in specific systems), and integrated albunemia. All of these indices significantly predicted the risk of death (p < 0.01) after adjusting for chronological age and sex. In all models, men were 2.4-4.4-times more likely to die than women. The global PD was shown to be a good predictor of deterioration, with the odds of deterioration increasing by 41.7% per 0.5-unit increase in the global PD. As for death, the odds also increased by 68.3% per 0.5-unit increase in the global PD. Our results are partly attributed to common chronic diseases that aggravate COVID-19, but they also suggest that the underlying physiological state could capture vulnerability to severe COVID-19 and serve as a tool for prognosis that would, in turn, help inpatient management.

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images.

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.

Effect of Hyperglycemia on COVID-19 Outcomes: Vaccination Efficacy, Disease Severity, and Molecular Mechanisms.

BACKGROUND/AIMS: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus, a member of the subgenus Sarbecovirus (beta-CoV lineage B) and responsible for the coronavirus disease 2019 (COVID-19). COVID-19 encompasses a large range of disease severity, from mild symptoms to severe forms with Intensive Care Unit admission and eventually death. The severe forms of COVID-19 are usually observed in high-risk patients, such as those with type two diabetes mellitus. Here, we review the available evidence linking acute and chronic hyperglycemia to COVID-19 outcomes, describing also the putative mediators of such interactions. FINDINGS/CONCLUSIONS: Acute hyperglycemia at hospital admission represents a risk factor for poor COVID-19 prognosis in patients with and without diabetes. Acute and chronic glycemic control are both emerging as major determinants of vaccination efficacy, disease severity and mortality rate in COVID-19 patients. Mechanistically, it has been proposed that hyperglycemia might be a disease-modifier for COVID-19 through multiple mechanisms: (a) induction of glycation and oligomerization of ACE2, the main receptor of SARS-CoV-2; (b) increased expression of the serine protease TMPRSS2, responsible for S protein priming; (c) impairment of the function of innate and adaptive immunity despite the induction of higher pro-inflammatory responses, both local and systemic. Consistently, managing acute hyperglycemia through insulin infusion has been suggested to improve clinical outcomes, while implementing chronic glycemic control positively affects immune response following vaccination. Although more research is warranted to better disentangle the relationship between hyperglycemia and COVID-19, it might be worth considering glycemic control as a potential route to optimize disease prevention and management.

Clinical Profile of COVID-19 Patients Admitted in a COVID Designated Hospital.

BACKGROUND: Pandemic of COVID-19 has engulfed Nepal as well. In this paper, we studied the demographic, clinical, laboratory findings as well as the treatment modalities, prognostic factors and outcome of patients admitted with COVID-19. METHODS: This was an observational cross-sectional study that included all patients admitted to the General Medicine Department of College of Medical Sciences, Bharatpur, during the first wave of COVID-19 from April 2020 to February 2021 after obtaining the ethical clearance. Data analysis was done using statistical packages for social sciences version 16. RESULTS: A total of 119 patients with mean age of 61.5 years were admitted. They had a mean duration of onset of symptoms of 7.1 days. Commonest symptoms were fever (70.6%), cough (67.2%) and dyspnea (64.7%). Severe COVID-19 at admission with a median CT severity score of 15 was found in 49.7% of them. Total 83.2% patients required ICU care and 10.9% required mechanical ventilation. ARDS and secondary infection occurred in 17.6% each. Median length of hospital stay was 6 days. In total, 56.3% recovered 27.7% left against medical advice and 16.0% expired. Severity of COVID at admission, CT severity score at presentation and D-dimer at admission were found to be significantly associated with mortality (P<0.05).Neither of the age, duration of illness, CRP at admission nor the use of remdesivir or convalescent plasma had significant relation with the mortality (P>0.05). CONCLUSIONS: Severity of illness at presentation, CT severity score and D-dimer level at admission are significantly associated with mortality of the patients admitted with COVID-19.