Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: A pooled analysis

Disease progression associated cytokines in COVID-19 patients with deteriorating and recovering health conditions

Understanding the immune response to COVID-19 is challenging due to its high variability among individuals. To identify differentially expressed cytokines between the deteriorating and recovering phases, we analyzed the Electronic Health Records (EHR) and cytokine profile data in a COVID-19 cohort of 444 infected patients and 145 non-infected healthy individuals. We categorized each patient's progression into Deterioration Phase (DP) and Recovery Phase (RP) using longitudinal neutrophil, lymphocyte and lactate dehydrogenase levels. A random forest model was built using healthy and severe patients to compute the contribution of each cytokine toward disease progression using Shapley Additive Explanations (SHAP). SHAP values were used for supervised clustering to identify DP and RP-related samples and their associated cytokines. The identified clusters effectively discriminated DP and RP samples, suggesting that the cytokine profiles differed between deteriorating and recovering health conditions. Especially, CXCL10, GDF15, PTX3, and TNFSF10 were differentially expressed between the DP and RP samples, which are involved in the JAK-STAT, NF- κ B, and MAPK signaling pathways contributing to the inflammatory response. Collectively, we characterized the immune response in terms of disease progression of COVID-19 with deteriorating and recovering health conditions.

SARS-CoV-2 uses Spike glycoprotein to control the host's anaerobic metabolism by inhibiting LDHB

The SARS-CoV-2 pandemic, responsible for approximately 7 million deaths worldwide, highlights the urgent need to understand the molecular mechanisms of the virus in order to prevent future outbreaks. The Spike glycoprotein of SARS-CoV-2, which is critical for viral entry through its interaction with ACE2 and other host cell receptors, has been a focus of this study. The present research goes beyond receptor recognition to explore Spike's influence on cellular metabolism. AP-MS interactome analysis revealed an interaction between the Spike S1 domain and lactate dehydrogenase B (LDHB), which was further confirmed by co-immunoprecipitation and immunofluorescence, indicating colocalisation in cells expressing the S1 domain. The study showed that Spike inhibits the catalytic activity of LDHB, leading to increased lactate levels in HEK-293T cells overexpressing the S1 subunit. In the hypothesised mechanism, Spike deprives LDHB of NAD+, facilitating a metabolic switch from aerobic to anaerobic energy production during infection. The Spike-NAD+ interacting region was characterised and mainly involves the W436 within the RDB domain. This novel hypothesis suggests that the Spike protein may play a broader role in altering host cell metabolism, thereby contributing to the pathophysiology of viral infection.

Immuno-inflammatory and organ dysfunction markers in severe COVID-19 patients

Infection with the SARS-CoV-2 virus may induce some complications among people who experience mild to moderate respiratory illness and some of them recover without requiring special treatment. Albeit, some individuals become seriously reached risk points and require special medical attention especially older people and people who suffer from chronic diseases. Serum and whole blood samples were collected from confirmed infected persons with SARS CoV-2 by real-time PCR and the control group. All lab. Investigations were performed using Cobas 6000. Significant differences were noted between patients compared to the control group in the Mean ± SD of IL-6 (76.06 ± 7.60 vs 3.61 ± 0.296 pg/ml), Procalcitonin (0.947 ± 0.117 vs 0.061 ± 0.007 ng/ml), CRP (125.3 ± 7.560 vs 4.027 ± 0.251 mg/dl), ALT (154.8 ± 30.47 vs 49.75 ± 2.977 IU/L) and AST (70.83 ± 9.215 vs 27.23 ± 1.767) respectively. While other parameters were also showed significant differences were noted between patients compared to the control group for D-Dimmer, PT, PTT, LDH, Ferritin, WBC, Lymphocyte and Creatinine. The results reached that the effect of SARS CoV-2 and cytokine storm was clear on the body's organs through vital biomarker investigations that were performed in this study.

Revisiting the COVID-19 Pandemic: Mortality and Predictors of Death in Adult Patients in the Intensive Care Unit

COVID-19 has generated a global impact due to its contagiousness and high lethality rates, with a large number of deaths occurring in intensive care units (ICUs). This study aimed to verify the occurrence of and understand the factors related to mortality in adult patients with COVID-19 admitted to the ICU in a tertiary hospital. This is a retrospective cohort study, which included COVID-19 patients admitted between March 2020 and December 2021. A total of 588 patients were included, of whom the majority (55.27%) did not survive. Invasive mechanical ventilation was the strongest predictor of the risk of death in the ICU with OR = 97.85 (95% CI = 39.10-244.86; p < 0.001), along with age and Simplified Acute Physiology Score 3 (SAPS3). The length of the ICU stay was protective. Evaluating patients on invasive mechanical ventilation in isolation, using an adjusted model, we found the following risk factors: use of vasopressin, renal replacement therapy, red cell distribution width > 15, use of hydrocortisone, and age in years. Protective factors included the days of mechanical ventilation use, being admitted from another service, and being of female sex. Identifying early predictors of mortality in patients with COVID-19 who require hospitalization is essential in the search for actions to prevent and manage complications, which can increase the survival of these patients and reduce the impact on health services.

A Retrospective Study: Evaluating the Impact of the COVID-19 Pandemic on Inflammatory Markers in Hospitalized Patients

BACKGROUND

The COVID-19 pandemic has had a significant impact globally, and understanding the relationship between inflammatory markers and disease progression is crucial for effective management. This retrospective study aimed to examine the association between various inflammatory markers, such as C-reactive protein (CRP), the erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), D-dimer, ferritin, and procalcitonin (PCT), and the characteristics of disease progression and outcomes in individuals affected by COVID-19.

METHODS

This study collected raw data from 470 patients who tested positive for SARS-CoV-2 using RT-PCR.

RESULTS

The logistic regression analysis revealed that elevated LDH levels were associated with male gender, ICU admission, low oxygen saturation (O2 < 93%), the need for mechanical ventilation, death, and the presence of lung infiltrates. Higher D-dimer levels were associated with older age, diabetes mellitus, cardiac disease, and low oxygen saturation. Ferritin levels were significantly associated with older age, ICU admission, low oxygen saturation, mechanical ventilation, and lung infiltrates. In contrast, CRP was only significant regarding lung infiltrates and procalcitonin levels were not significantly associated with any of the examined factors.

CONCLUSION

This study highlights the importance of monitoring key inflammatory markers, such as LDH, D-dimer, and ferritin, as they are significantly associated with the severity of COVID-19 illness. These findings can inform clinical decision-making and guide the development of targeted interventions to improve patient outcomes.

Coronavirus disease 2019 and its impact on the cognition of older adults: Unraveling the role of inflammation

Background

The Coronavirus Disease 2019 (COVID-19) pandemic significantly impacted the older adult population globally. This study aimed to investigate cognitive function and its relationship with inflammation in older COVID-19 survivors over a three-month follow-up to address concerns about cognitive impairment and its risk factors.

Methods

In this descriptive-analytical study, 177 hospitalized COVID-19 patients aged >60 were assessed from July 2021 to February 2022. Psychiatric, global cognitive assessments and activities of daily living were conducted at discharge, 1 month, and 3 months post-discharge. Statistical analyses were conducted using SPSS Version 24. The evolution of cognitive status over time was evaluated using the Repeated Measures Test. The study probed into the association between inflammatory markers and cognitive function through the Pearson correlation test and the Mann-Whitney U test. Additionally, the link between anxiety/depression and cognitive performance was examined using the Pearson correlation.

Results

Results indicated that higher levels of C-reactive protein (CRP), D-dimer, and Lactate Dehydrogenase (LDH) were correlated to reduced cognitive performance. Conversely, Erythrocyte Sedimentation Rate (ESR) and Creatine Phosphokinase (CPK) did not exhibit a significant relationship with cognitive scores. A positive correlation was observed between improved cognitive function (reflected by higher GPCOG scores) and lower levels of anxiety and depression (indicated by lower scores on the Hospital Anxiety and Depression Scale). Over the study period, cognitive function and anxiety scores showed an upward trend, whereas symptoms of depression and challenges in daily activities remained consistent.

Conclusions

The study highlights the enduring effects and detrimental role of inflammation on overall cognitive abilities among older survivors of COVID-19. It underscores the urgent need for specialized interventions and rehabilitative strategies to facilitate sustained cognitive recuperation among these individuals.

COVID-19 and silent hypoxemia in a minimal closed-loop model of the respiratory rhythm generator

Silent hypoxemia, or "happy hypoxia," is a puzzling phenomenon in which patients who have contracted COVID-19 exhibit very low oxygen saturation ( SaO 2 < 80%) but do not experience discomfort in breathing. The mechanism by which this blunted response to hypoxia occurs is unknown. We have previously shown that a computational model of the respiratory neural network (Diekman et al. in J Neurophysiol 118(4):2194-2215, 2017) can be used to test hypotheses focused on changes in chemosensory inputs to the central pattern generator (CPG). We hypothesize that altered chemosensory function at the level of the carotid bodies and/or the nucleus tractus solitarii are responsible for the blunted response to hypoxia. Here, we use our model to explore this hypothesis by altering the properties of the gain function representing oxygen sensing inputs to the CPG. We then vary other parameters in the model and show that oxygen carrying capacity is the most salient factor for producing silent hypoxemia. We call for clinicians to measure hematocrit as a clinical index of altered physiology in response to COVID-19 infection.

Taming the storm: potential anti-inflammatory compounds targeting SARS-CoV-2 MPro

In severe COVID-19 cases, an exacerbated inflammatory response triggers a cytokine storm that can worsen the prognosis. Compounds with both antiviral and anti-inflammatory activities show promise as candidates for COVID-19 therapy, as they potentially act against the SARS-CoV-2 infection regardless of the disease stage. One of the most attractive drug targets among coronaviruses is the main protease (MPro). This enzyme is crucial for cleaving polyproteins into non-structural proteins required for viral replication. The aim of this review was to identify SARS-CoV-2 MPro inhibitors with both antiviral and anti-inflammatory properties. The interactions of the compounds within the SARS-CoV-2 MPro binding site were analyzed through molecular docking when data from crystallographic structures were unavailable. 18 compounds were selected and classified into five different superclasses. Five of them exhibit high potency against MPro: GC-376, baicalein, naringenin, heparin, and carmofur, with IC50 values below 0.2 μM. The MPro inhibitors selected have the potential to alleviate lung edema and decrease cytokine release. These molecules mainly target three critical inflammatory pathways: NF-κB, JAK/STAT, and MAPK, all previously associated with COVID-19 pathogenesis. The structures of the compounds occupy the S1/S2 substrate binding subsite of the MPro. They interact with residues from the catalytic dyad (His41 and Cys145) and/or with the oxyanion hole (Gly143, Ser144, and Cys145), which are pivotal for substrate recognition. The MPro SARS-CoV-2 inhibitors with potential anti-inflammatory activities present here could be optimized for maximum efficacy and safety and be explored as potential treatment of both mild and severe COVID-19.

Initiator cell death event induced by SARS-CoV-2 in the human airway epithelium

Virus-induced cell death is a key contributor to COVID-19 pathology. Cell death induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is well studied in myeloid cells but less in its primary host cell type, angiotensin-converting enzyme 2 (ACE2)-expressing human airway epithelia (HAE). SARS-CoV-2 induces apoptosis, necroptosis, and pyroptosis in HAE organotypic cultures. Single-cell and limiting-dilution analysis revealed that necroptosis is the primary cell death event in infected cells, whereas uninfected bystanders undergo apoptosis, and pyroptosis occurs later during infection. Mechanistically, necroptosis is induced by viral Z-RNA binding to Z-DNA-binding protein 1 (ZBP1) in HAE and lung tissues from patients with COVID-19. The Delta (B.1.617.2) variant, which causes more severe disease than Omicron (B1.1.529) in humans, is associated with orders of magnitude-greater Z-RNA/ZBP1 interactions, necroptosis, and disease severity in animal models. Thus, Delta induces robust ZBP1-mediated necroptosis and more disease severity.

Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients

Amidst the global COVID-19 pandemic, the urgent need for timely and precise patient prognosis assessment underscores the significance of leveraging machine learning techniques. In this study, we present a novel predictive model centered on routine clinical laboratory test data to swiftly forecast patient survival outcomes upon admission. Our model integrates feature selection algorithms and binary classification algorithms, optimizing algorithmic selection through meticulous parameter control. Notably, we developed an algorithm coupling Lasso and SVM methodologies, achieving a remarkable area under the ROC curve of 0.9277 with the use of merely 8 clinical laboratory parameters collected upon admission. Our primary contribution lies in the utilization of straightforward laboratory parameters for prognostication, circumventing data processing intricacies, and furnishing clinicians with an expeditious and precise prognostic assessment tool.

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.

Malnutrition elevates delirium and ICU stay among critically ill older adult COVID-19 patients

Background and aim

Malnutrition among intensive care unit (ICU) patients is associated with a higher risk of mortality and prolonged hospitalization. However, the influence of malnutrition on severe outcomes of ICU patients with coronavirus disease 2019 (COVID-19) is unclear. By evaluating the effect of malnutrition on the outcomes of COVID-19 in the ICU in older adult patients, this study will contribute to new knowledge of assessing the nutritional status and recovery of these patients.

Methods

In the current single center prospective study, 310 critically ill COVID-19 patients with an age range of ≥65 years were recruited. Data on demographic characteristics, laboratory parameters, comorbidities, medications, and types of mechanical ventilation were collected in the first 24 h of ICU admission. Malnutrition was defined based on the two-step approach of the Global Leadership Initiative on Malnutrition (GLIM) scale at baseline. During the 45 days after the baseline, we collected data on delirium incidence, mortality, invasive mechanical ventilation (IMV) requirement, length of ICU and hospital admission, and re-hospitalization.

Results

In this study, the prevalence of malnutrition was 63.4% among critically ill COVID-19 patients. During the 45-day follow-up, 190 (61.3%) COVID-19 deaths were recorded among the baseline 310 patients. After controlling for potential confounders, malnutrition was associated with an increased risk of delirium so malnourished COVID-19 patients had a significantly 35% higher risk of delirium than those without malnutrition (HR: 1.35, 95% CI: 1.01-1.83). Such a significant association was not for COVID-19 mortality and IMV requirement. In addition, malnutrition was associated with a significantly 84% greater odds of prolonged ICU admission (OR: 1.84, 95% CI: 1.09-3.10). No significant association was seen between malnutrition and re-hospitalization and also prolonged hospital admission.

Conclusion

Malnutrition was associated with an increased risk of delirium and prolonged ICU admission among critically ill older adult COVID-19 patients. Prevention, diagnosis, and treatment of malnutrition could be a key component in improving outcomes in these patients.

Modulation in serum and hematological parameters as a prognostic indicator of COVID-19 infection in hypertension, diabetes mellitus, and different cardiovascular diseases

SARS-CoV-2 infection affects and modulates serum as well as hematological parameters. However, whether it modifies these parameters in the existing disease conditions, which help in the erection of specific treatments for the disease, is under investigation. Here, we aimed to determine whether serum and hematological parameters alteration in various diseases, diabetes mellitus (DM), hypertension (HTN), ischemic heart disease (IHD) and myocardial infarction (MI) conditions correlate and signal SARS-CoV-2 infection, which could be used as a rapid diagnosis tool for SARS-CoV-2 infection in disease conditions. To assess the projected goals, we collected blood samples of 1,113 male and female patients with solo and multiple disease conditions of DM/HTN/IHD/MI with severe COVID-19, followed by biochemical analysis, including COVID-19 virus detection by RT-qPCR. Furthermore, blood was collected from age-matched disease and healthy individuals 502 and 660 and considered as negative control. In our results, we examined higher levels of serum parameters, including D-dimer, ferritin, hs-CRP, and LDH, as well as hematological parameters, including TLC in sole and multiple diseases (DM/HTN/IHD/MI) conditions compared to the control subjects. Besides, the hematological parameters, including Hb, RBC, and platelet levels, decreased in the patients. In addition, we found declined levels of leukocyte count (%), lymphocyte (%), monocyte (%), and eosinophil (%), and elevated level of neutrophil levels (%) in all the disease patients infected with SARS-CoV-2. Besides, NLR and NMR ratios were also statistically significantly (p < 0.05) high in the patients with solo and multiple disease conditions of DM/HTN/IHD/MI infected with the SARS-CoV-2 virus. In conclusion, rapid alteration of sera and hematological parameters are associated with SARS-CoV-2 infections, which could help signal COVID-19 in respective disease patients. Moreover, our results may help to improve the clinical management for the rapid diagnosis of COVID-19 concurrent with respective diseases.

The relationship between lactate dehydrogenase and Apolipoprotein A1 levels in patients with severe pneumonia

Background

To investigate the relationship between lactate dehydrogenase and apolipoprotein A1 levels and the condition and prognosis of patients with severe pneumonia.

Methods

Data was collected from 204 patients with severe pneumonia who were hospitalized from January 1, 2019 to December 1, 2021 in Zhaotong First People's Hospital (respiratory intensive care unit (RICU)), and divided into survival group (160 patients) and death group (44 patients) according to their hospitalization outcome. The relationship between lactate dehydrogenase and apolipoprotein A1 levels and general information, disease, and treatment needs of patients with severe pneumonia was analyzed, and lactate dehydrogenase, apolipoprotein A1, neutrophil-to-lymphocyte ratio, hematocrit, C-reactive protein, calcitoninogen, D-dimer, Acute Physiology and Chronic Health Status Rating System II, and Pneumonia Severity Index scores were compared between the survival and death groups. The value of these indicators in determining the prognosis of patients was analyzed using subject operating characteristic (ROC) curves. Logistic regression was used to analyze the risk factors for death from severe pneumonia.

Effectiveness of upper arm and breathing exercises to improve inflammatory markers in severe COVID-19 patients

Studies have suggested associations between inflammatory markers with the severity of coronavirus disease 2019 (COVID-19). Therefore, exercises that could reduce the level of inflammatory markers might be beneficial. The aim of this study was to determine the effect of upper arm and breathing exercises on inflammatory markers such as ferritin, lactate dehydrogenase (LDH), and C-reactive protein (CRP) in severe COVID-19 patients. A quasi-experimental with pre-test and post-test control group design was conducted among severe COVID-19 aged 18-70 years old, with or without comorbidities. Baseline data of inflammatory markers (ferritin, LDH, and CRP) were measured before the exercises and repeated post-exercise. The upper arm and breathing exercises were performed for ten days, twice a day (morning and evening) for ten minutes. A paired Student t-test was used to assess the changes in the inflammatory markers' levels. Our data indicated that levels of ferritin and CRP were not significantly different between pre- and post-exercise. However, the level of LDH decreased significantly from 481.35 U/L to 331.80 U/L (p=0.001). This study highlights that pulmonary rehabilitation exercises might be beneficial to enhance the recovery process in severe COVID-19 patients.

Severe pediatric COVID-19: a review from the clinical and immunopathophysiological perspectives

BACKGROUND

Coronavirus disease 2019 (COVID-19) tends to have mild presentations in children. However, severe and critical cases do arise in the pediatric population with debilitating systemic impacts and can be fatal at times, meriting further attention from clinicians. Meanwhile, the intricate interactions between the pathogen virulence factors and host defense mechanisms are believed to play indispensable roles in severe COVID-19 pathophysiology but remain incompletely understood.

DATA SOURCES

A comprehensive literature review was conducted for pertinent publications by reviewers independently using the PubMed, Embase, and Wanfang databases. Searched keywords included "COVID-19 in children", "severe pediatric COVID-19", and "critical illness in children with COVID-19".

RESULTS

Risks of developing severe COVID-19 in children escalate with increasing numbers of co-morbidities and an unvaccinated status. Acute respiratory distress stress and necrotizing pneumonia are prominent pulmonary manifestations, while various forms of cardiovascular and neurological involvement may also be seen. Multiple immunological processes are implicated in the host response to COVID-19 including the type I interferon and inflammasome pathways, whose dysregulation in severe and critical diseases translates into adverse clinical manifestations. Multisystem inflammatory syndrome in children (MIS-C), a potentially life-threatening immune-mediated condition chronologically associated with COVID-19 exposure, denotes another scientific and clinical conundrum that exemplifies the complexity of pediatric immunity. Despite the considerable dissimilarities between the pediatric and adult immune systems, clinical trials dedicated to children are lacking and current management recommendations are largely adapted from adult guidelines.

CONCLUSIONS

Severe pediatric COVID-19 can affect multiple organ systems. The dysregulated immune pathways in severe COVID-19 shape the disease course, epitomize the vast functional diversity of the pediatric immune system and highlight the immunophenotypical differences between children and adults. Consequently, further research may be warranted to adequately address them in pediatric-specific clinical practice guidelines.

Comparison of influenza- and COVID-19-associated pulmonary aspergillosis in China

PURPOSE

We conducted a monocentric retrospective study using the latest definitions to compare the demographic, clinical, and biological characteristics of influenza-associated pulmonary aspergillosis (IAPA) and COVID-19-associated pulmonary aspergillosis (CAPA).

METHODS

The study retrospectively enrolled 180 patients, including 70 influenza/IPA patients (with positive influenza A/B and Aspergillus) and 110 COVID-19/IPA patients (with positive SARS-CoV-2 and Aspergillus). Among them, 42 (60%) and 30 (27.3%) patients fulfilled the definitions of IAPA and CAPA, respectively.

RESULTS

The CAPA patients had significantly higher in-hospital mortality (13/31, 41.9%) than IAPA patients (8/42, 19%) with a P-value of 0.033. Kaplan-Meier survival curve also showed significantly higher 30-day mortality for CAPA patients (P = 0.025). Additionally, the CAPA patients were older, though insignificantly, than IAPA patients (70 (60-80) vs. 62 (52-72), P = 0.075). A lower percentage of chronic pulmonary disease (12.9 vs. 40.5%, P = 0.01) but higher corticosteroids use 7 days before and after ICU admission (22.6% vs. 0%, P = 0.002) were found in CAPA patients. Notably, there were no significant differences in the percentage of ICU admission or ICU mortality between the two groups. In addition, the time from observation to Aspergillus diagnosis was significantly longer in CAPA patients than in IAPA patients (7 (2-13) vs. 0 (0-4.5), P = 0.048).

CONCLUSION

Patients infected with SARS-CoV-2 and Aspergillus during the concentrated outbreak of COVID-19 in China had generally higher in-hospital mortality but a lower percentage of chronic pulmonary disease than those infected with influenza and Aspergillus. For influenza-infected patients who require hospitalization, close attention should be paid to the risk of invasive aspergillosis upfront.

Dysphagia in patients with severe COVID-19: a retrospective study

To investigate dysphagia after extubation in patients with severe coronavirus disease 2019 (COVID-19). We retrospectively examined patients with severe COVID-19 treated in our hospital between August 2021 and March 2022. Feeding outcomes were categorized into two groups-(1) total oral intake, and (2) difficulty in oral intake. To assess the feeding outcome, we used modified water-swallowing test (MWST) for all patients. However, in cases where aspiration or recurrent laryngeal nerve palsy was suspected, we conducted the fiberoptic endoscopic evaluation of swallowing after MWST. Patient data were collected from medical records. Forty-six patients with severe COVID-19 were included. Among the 46 patients, 14 (30.4%) experienced difficulties with oral intake. Older age, longer length of hospitalization, duration of mechanical ventilation, tracheostomy, diabetes, and higher serum levels of C-reactive protein (CRP) and procalcitonin (PCT) at the time of intubation were associated with difficulty in oral intake. The rate of difficulty with oral intake in patients with severe COVID-19 was 30.4%, which is not as high as reported in previous studies. Older age, longer duration of mechanical ventilation, tracheostomy, diabetes, and higher levels of CRP and PCT were associated with the prevalence of oral intake difficulty, suggesting that early attention should be paid to high-risk patients who have preexisting deterioration of swallowing function due to aging and comorbidities, or who have prolonged intubation or tracheostomy to prevent aspiration pneumonia.

Urinary cadmium concentration is associated with the severity and clinical outcomes of COVID-19: a bicenter observational cohort study

BACKGROUND

Cadmium and nickel exposure can cause oxidative stress, induce inflammation, inhibit immune function, and therefore has significant impacts on the pathogenesis and severity of many diseases. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can also provoke oxidative stress and the dysregulation of inflammatory and immune responses. This study aimed to assess the potential associations of cadmium and nickel exposure with the severity and clinical outcomes of patients with coronavirus disease 2019 (COVID-19).

METHODS

We performed a retrospective, observational, bicenter cohort analysis of patients with SARS-CoV-2 infection in Taiwan between June 2022 and July 2023. Cadmium and nickel concentrations in blood and urine were measured within 3 days of the diagnosis of acute SARS-CoV-2 infection, and the severity and clinical outcomes of patients with COVID-19 were analyzed.

RESULTS

A total of 574 patients were analyzed and divided into a severe COVID-19 group (hospitalized patients) (n = 252; 43.9%), and non-severe COVID-19 group (n = 322; 56.1%). The overall in-hospital mortality rate was 11.8% (n = 68). The severe COVID-19 patients were older, had significantly more comorbidities, and significantly higher neutrophil/lymphocyte ratio, C-reactive protein, and interleukin-6 than the non-severe COVID-19 patients (all p < 0.05). Blood and urine cadmium and urine nickel concentrations were significantly higher in the severe COVID-19 patients than in the non-severe COVID-19 patients. Among the severe COVID-19 patients, those in higher urine cadmium/creatinine quartiles had a significantly higher risk of organ failure (i.e., higher APACHE II and SOFA scores), higher neutrophil/lymphocyte ratio, lower PaO2/FiO2 requiring higher invasive mechanical ventilation support, higher risk of acute respiratory distress syndrome, and higher 60-, 90-day, and all-cause hospital mortality (all p < 0.05). Multivariable logistic regression models revealed that urine cadmium/creatinine was independently associated with severe COVID-19 (adjusted OR 1.643 [95% CI 1.060-2.547], p = 0.026), and that a urine cadmium/creatinine value > 2.05 μg/g had the highest predictive value (adjusted OR 5.349, [95% CI 1.118-25.580], p = 0.036).

CONCLUSIONS

Urine cadmium concentration in the early course of COVID-19 could predict the severity and clinical outcomes of patients and was independently associated with the risk of severe COVID-19.

Risk Factor Analysis and Nomogram for Early Progression of COVID-19 Pneumonia in Older Adult Patients in the Omicron Era

Background and Objective

Timely recognition of risk factors for early progression in older adult patients with COVID-19 is of great significance to the following clinical management. This study aims to analyze the risk factors and create a nomogram for early progression in older adult patients with COVID-19 in the Omicron era.

Methods

A total of 272 older adults infected with COVID-19 admitted from December 2022 to February 2023 were retrospectively recruited. Risk factor selection was determined using the logistic and the least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then created to predict early progression, followed by the internal validation and assessment of its performance through plotting the receiver operating characteristic (ROC), calibration, and decision curves.

Results

A total of 83 (30.5%) older adult patients presented an early progression on chest CT after 3-5 days of admission under standard initiate therapy. Six independent predictive factors were incorporated into the nomogram to predict the early progression, including CRP > 10 mg/L, IL-6 > 6.6 pg/mL, LDH > 245 U/L, CD4+ T-lymphocyte count <400/µL, the Activities of Daily Living (ADL) score ≤40 points, and the Mini Nutritional Assessment Scale-Short Form (MNA-SF) score ≤7 points. The area under the curve (AUC) of the nomogram in discriminating older adult patients who had risk factors in the training and validation cohort was 0.857 (95% CI 0.798, 0.916) and 0.774 (95% CI 0.667, 0.881), respectively. The calibration and decision curves demonstrated a high agreement in the predicted and observed risks, and the acceptable net benefit in predicting the early progression, respectively.

Conclusion

We created a nomogram incorporating highly available laboratory data and the Comprehensive Geriatric Assessment (CGA) findings that effectively predict early-stage progression in older adult patients with COVID-19 in the Omicron era.

Point-of-care lung ultrasound predicts hyperferritinemia and hospitalization, but not elevated troponin in SARS-CoV-2 viral pneumonitis in children

SARS-CoV-2 often causes viral pneumonitis, hyperferritinemia, elevations in D-dimer, lactate dehydrogenase (LDH), transaminases, troponin, CRP, and other inflammatory markers. Lung ultrasound is increasingly used to diagnose and stratify viral pneumonitis severity. We retrospectively reviewed 427 visits in patients aged 14 days to 21 years who had had a point-of-care lung ultrasound in our pediatric emergency department from 30/November/2019 to 14/August/2021. Lung ultrasounds were categorized using a 6-point ordinal scale. Lung ultrasound abnormalities predicted increased hospitalization with a threshold effect. Increasingly abnormal laboratory values were associated with decreased discharge from the ED and increased admission to the ward and ICU. Among patients SARS-CoV-2 positive patients ferritin, LDH, and transaminases, but not CRP or troponin were significantly associated with abnormalities on lung ultrasound and also with threshold effects. This effect was not demonstrated in SARS-CoV-2 negative patients. D-Dimer, CRP, and troponin were sometimes elevated even when the lung ultrasound was normal.

Investigation of Serum Endocan Levels in SARS-CoV-2 Patients

Endocan is an endothelial-cell-specific proteoglycan (ESM-1) and has emerged as an endothelial dysfunction and inflammatory marker in recent years. Endocan can be used as a marker of inflammatory endothelial dysfunction in endothelium-dependent disease: cardiovascular disease, sepsis, lung and kidney disease and malignancies. Recent data suggest that endothelial dysfunction is a key mechanism in COVID-19 pathogenesis. Endotheliitis and thrombo-inflammation are associated with severe forms of SARS-CoV-2 infection, and endocan is currently under investigation as a potential diagnostic and prognostic marker. The aim of this study was to determine serum endocan levels in patients with COVID-19 to evaluate the correlation between endocan levels and clinical disease diagnosis and prognosis. This study enrolled 56 patients, divided into three groups depending on disease severity: mild (15), moderate (25) and severe (16). The biochemical, demographic, clinical and imagistic data were collected and evaluated in correlation with the endocan levels. Serum endocan levels were significantly higher in the COVID-19 patients compared to the control group; also, endocan concentration correlated with vaccination status. The results revealed significantly elevated serum endocan levels in COVID-19 patients compared to the control group, with a correlation observed between endocan concentration and vaccination status. These findings suggest that endocan may serve as a novel biomarker for detecting inflammation and endothelial dysfunction risk in COVID-19 patients. There was no significant relationship between serum endocan levels and disease severity or the presence of cardiovascular diseases. Endocan can be considered a novel biomarker for the detection of inflammation and endothelial dysfunction risk in COVID-19 patients.

Development of Predictive Models Based on Biochemical Parameters to Triage COVID-19 Patients: A Study Conducted in a Tertiary Care Hospital

Background The COVID-19 disease continues to cause severe mortality and morbidity. Biochemical parameters are being used to predict the severity of the infection. This study aims to predict disease severity and mortality to help reduce mortality through timely intervention in a cost-effective way. Methods A total of 324 COVID-19 cases admitted at our hospital (All India Institute of Medical Sciences, Patna, BR, India) between June 2020 to December 2020 (phase 1: 190 patients) and April 2021 to May 2021 (phase 2: 134 patients) were recruited for this study. Statistical analysis was done using SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA) and model prediction using Python (The Python Software Foundation, Wilmington, DE, USA). Results There were significant differences in biochemical parameters at the time of admission among COVID-19 patients between phases 1 and 2, ICU and non-ICU admissions, and expired and discharged patients. The receiver operating characteristic (ROC) curves predicted mortality solely based on biochemical parameters. Using multiple logistic regression in Python, a total of four models (two each) were developed to predict ICU admission and mortality. A total of 92 out of 96 patients were placed into the correct management category by our model. This model would have allowed us to preserve 17 of the 21 patients we lost. Conclusions We developed predictive models for admission (ICU or non-ICU) and mortality based on biochemical parameters at the time of admission. A predictive model with a significant predictive capability for IL-6 and procalcitonin values using normal biochemical parameters was proposed. Both can be used as machine learning tools to prognosticate the severity of COVID-19 infections. This study is probably the first of its kind to propose triage for admission in the ICU or non-ICU at the medical emergency department during the first presentation for the necessary optimal treatment of COVID-19 based on a predictive model.

Impact of Coronavirus Disease 2019 on Unresectable Hepatocellular Carcinoma Treated with Atezolizumab/Bevacizumab

(1) Background: The coronavirus disease 2019 (COVID-19) pandemic has proven challenging to the management of patients with cancer, particularly those receiving systemic therapy. This study aimed to evaluate the impact of COVID-19 on patients with unresectable hepatocellular carcinoma (HCC) treated with atezolizumab/bevacizumab. (2) Methods: Patients with unresectable HCC who started atezolizumab/bevacizumab treatment between June 2020 and December 2021 at a tertiary cancer center in Korea were included (n = 241) and classified according to their COVID-19 status and severity. (3) Results: Thirty-five (14.5%) patients with unresectable HCC were diagnosed with COVID-19 during atezolizumab/bevacizumab treatment; 26 (74.2%) and nine (25.7%) in the low- and high-severity groups, respectively. The high-severity group showed higher neutrophil-to-lymphocyte ratios and lactate dehydrogenase levels. Liver and kidney injuries were observed in 31.4% and 17.1% of total patients, respectively. Liver injury was more prominent in patients with pre-existing liver dysfunction at baseline, who were more prevalent in the high-severity group. Atezolizumab/bevacizumab treatment was delayed by a median of 0 (range, 0-21) day in the low-severity group and 12 (range, 0-35) days in the high-severity group. The high-severity group showed worse post-infection progression-free survival (1.1 vs. 4.8 months, p = 0.017) and overall survival (2.2 months vs. not reached, p = 0.004). (4) Conclusions: Patients with impaired liver function at baseline are more susceptible to high-severity COVID-19, which affects atezolizumab/bevacizumab treatment outcomes.

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates

BACKGROUND

The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models.

METHODS

We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index.

RESULTS

The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.

CONCLUSION

Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.

CoVProt: Toward a Mass Spectrometry Data Portal for COVID-19 Proteomics Research and Development

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc globally. Beyond the pandemic, the long-term effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in multiple organ systems are yet to be deciphered. This calls for continued systems science research. Moreover, the host response to SARS-CoV-2 varies person-to-person and gives rise to different degrees of morbidity and mortality. Mass spectrometry (MS) has been a proven asset in studies of the SARS-CoV-2 from an omics systems science lens. To strengthen the proteomics research dedicated to COVID-19, we introduce here a web-based portal, CoVProt. The portal is work in progress and aims for a comprehensive curation of MS-based proteomics data of COVID-19 clinical samples for deep proteomic investigations, data visualization, and easy data accessibility for life sciences innovations and planetary health research community. Currently, CoVProt contains information on 2725 different proteins and 37,125 different peptides from six data sets covering a total of 202 clinical samples. Moreover, all pertinent data sets extracted from the literature have been reanalyzed using a common analysis pipeline developed by combining multiple tools. Going forward, we anticipate that the CoVProt portal will also provide access to the clinical parameters of the patients. The CoVProt (v1.0) portal addresses an existing significant gap to study COVID-19 host proteomics, which, to the best of our knowledge, is the first effort in this direction. We believe that CoVProt is poised to make contributions as a community resource for proteomic applications and aims to broadly support clinical studies to facilitate the discovery of COVID-19 biomarkers and therapeutics with translational potential.

Impact of comorbidities and inflammatory markers on mortality of COVID-19 patients

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes coronavirus disease 2019 (COVID-19) is a serious global health concern. The severity of the disease can be determined by serologic indicators such as C-reactive protein, lactate dehydrogenase, D-dimer, ferritin, and interleukin-6. (IL-6). Patients with preexisting conditions such as respiratory, cardiovascular, and pulmonary disease could be at risk of adverse outcomes. It is crucial to provide adequate medical care to manage these patients and increase their chances of survival.

AIM

The study examined the impact of comorbidity and inflammatory markers on the severity and mortality of hospitalised COVID-19 patients.

MATERIALS AND METHODS

This retrospective study included 101 COVID-19 patients who had comorbidities and were hospitalised from April 2021 to April 2022.

RESULTS

Patients with a severe COVID-19 infection could be anticipated to have higher levels of inflammatory markers in their blood. Patients with chronic kidney and coronary artery disease have a worse prognosis than those with other comorbidities (P value <0.001). However, tuberculosis had no statistically significant effect on mortality and showed a minimal chance of death (P value = 0.303). In addition, tocilizumab performed poorly and was ineffective against the COVID-19 treatment. However, ivermectin exhibited a statistically significant probability of survival in COVID-19 patients.

CONCLUSION

The inflammatory markers D-dimer, ferritin, and IL-6 were identified as valuable indicators of disease severity. Further, chronic kidney disease and coronary artery disease were identified as risk factors for mortality, while tuberculosis showed potential protective effects. The study showed that higher neutrophil levels were linked to mortality in tocilizumab-treated patients, while ivermectin showed promise in increasing survival rates.

Glucose metabolic reprogramming-related parameters for the prediction of 28-day neurological prognosis and all-cause mortality in patients after cardiac arrest: a prospective single-center observational study

BACKGROUND

We aimed to observe the dynamic changes in glucose metabolic reprogramming-related parameters and their ability to predict neurological prognosis and all-cause mortality in cardiac arrest patients after the restoration of spontaneous circulation (ROSC).

METHODS

Adult cardiac arrest patients after ROSC who were admitted to the emergency or cardiac intensive care unit of the First Affiliated Hospital of Dalian Medical University from August 1, 2017, to May 30, 2021, were enrolled. According to 28-day survival, the patients were divided into a non-survival group (n=82) and a survival group (n=38). Healthy adult volunteers (n=40) of similar ages and sexes were selected as controls. The serum levels of glucose metabolic reprogramming-related parameters (lactate dehydrogenase [LDH], lactate and pyruvate), neuron-specific enolase (NSE) and interleukin 6 (IL-6) were measured on days 1, 3, and 7 after ROSC. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score and Sequential Organ Failure Assessment (SOFA) score were calculated. The Cerebral Performance Category (CPC) score was recorded on day 28 after ROSC.

RESULTS

Following ROSC, the serum LDH (607.0 U/L vs. 286.5 U/L), lactate (5.0 mmol/L vs. 2.0 mmol/L), pyruvate (178.0 μmol/L vs. 70.9 μmol/L), and lactate/pyruvate ratio (34.1 vs. 22.1) significantly increased and were higher in the non-survivors than in the survivors on admission (all P<0.05). Moreover, the serum LDH, pyruvate, IL-6, APACHE II score, and SOFA score on days 1, 3 and 7 after ROSC were significantly associated with 28-day poor neurological prognosis and 28-day all-cause mortality (all P<0.05). The serum LDH concentration on day 1 after ROSC had an area under the receiver operating characteristic curve (AUC) of 0.904 [95% confidence interval [95% CI]: 0.851-0.957]) with 96.8% specificity for predicting 28-day neurological prognosis and an AUC of 0.950 (95% CI: 0.911-0.989) with 94.7% specificity for predicting 28-day all-cause mortality, which was the highest among the glucose metabolic reprogramming-related parameters tested.

CONCLUSION

Serum parameters related to glucose metabolic reprogramming were significantly increased after ROSC. Increased serum LDH and pyruvate levels, and lactate/pyruvate ratio may be associated with 28-day poor neurological prognosis and all-cause mortality after ROSC, and the predictive efficacy of LDH during the first week was superior to others.

Relationship between serum lactate dehydrogenase levels and prognosis in patients infected with omicron and delta variants of COVID-19: A cross-sectional study

Determining the prognosis of COVID-19 is crucial for understanding disease trends and developing effective treatment strategies, particularly for severe cases. However, there is currently insufficient evidence regarding the role of lactate dehydrogenase (LDH) in COVID-19 and its prognostic implications. This retrospective cross-sectional study analyzed data from all patients hospitalized at Urmia Imam Khomeini Hospital between September 2021 and March 2023 with a diagnosis of COVID-19 involving the Delta and Omicron variants. Patient information, including age, sex, duration of hospitalization, admission to the intensive care unit (ICU), strain type, and outcomes, was extracted. Exclusion criteria encompassed myocardial infarction, lung, liver, blood, skeletal muscle diseases, injury, neoplasm, pancreatitis, pregnancy, poisoning, unwillingness to participate in the study, and the use of ascorbic acid. A total of 609 patients with an average age of 54.80 years were included in this study. Among these patients, 56.3% were female, and the mortality rate was 20.7%. The serum LDH levels were significantly higher in patients who succumbed to the disease (P < 0.001), those requiring ICU admission (P < 0.001), and female patients (P = 0.02) compared to other groups. Furthermore, the LDH serum levels exhibited a strong significant correlation with the duration of hospitalization (r = -0.11, P = 0.007). The results revealed that there is a difference in the mean serum LDH levels between deceased patients and discharged patients (P < 0.001). The findings indicate that elevated serum LDH levels are associated with increased mortality, prolonged hospitalization, ICU admission, and infection with the Delta variant of COVID-19.

'Immune-inflammatory markers & clinical characteristics for outcomes in hospitalized SARS-CoV-2 infected patients of Pakistan: a retrospective analysis'

OBJECTIVE

Accumulating evidence suggests the role of immune-inflammatory markers in early risk stratification and prognostication of COVID-19 patients. We aimed to evaluate their association with severity and the development of diagnostic scores with optimal thresholds in critical patients.

SETTING AND PARTICIPANTS

This retrospective case study includes hospitalized COVID-19 patients from March 2019 to March, 2022, in the developing area teaching hospital in Pakistan. Polymerase chain reaction (PCR) positive patients, n = 467 were investigated for clinical outcomes, comorbidities and disease prognosis. The plasma levels of Interleukin-6 (IL-6), Lactate dehydrogenase (LDH), C-reactive protein (CRP), Procalcitonin (PCT), ferritin and Complete blood count markers were measured.

RESULTS

Majority were males (58.8%) and patients with comorbidities had more severe disease. Hypertension and diabetes mellitus were the commonest comorbidities. Shortness of breath, myalgia and cough were the main symptoms. The hematological markers NLR, as well as the plasma levels of immune-inflammatory variables, IL-6, LDH, Procalcitonin, Erythrocyte sedimentation rate, Ferritin were markedly raised in severe and critical patients (p < 0.0001 for these markers). ROC analysis supports IL-6 as the most accurate marker with high prognostic relevance with proposed cut-off threshold (43 pg/ml), determining >90% of patients in terms of COVID-19 severity (AUC = 0.93, 91.7%, se; 90.3%sp). Furthermore, positive correlation with all other markers including NLR with cut-off = 2.99 (AUC = 0.87, se = 89.8%, sp = 88.4%), CRP with cut-offs at 42.9 mg/l, (AUC = 0.883, se = 89.3% and sp = 78.6%), LDH cut-off at 267μg/L, evidenced in >80% patients (AUC = 0.834 se = 84% and sp = 80%). Additionally, ESR and ferritin have the corresponding AUC 0.81 and 0.813 with cut-off at 55 mm/hr and 370, respectively.

CONCLUSION

Investigating the immune-inflammatory markers can assist physicians in providing prompt treatment and ICU admission in terms of COVID-19 severity. As a result, which may reduce the overall mortality of COVID-19 patients.

Machine Learning Predictive Modeling for the Identification of Moderate Coronavirus Disease 2019 During the Pandemic: A Retrospective Study

BACKGROUND

Timely differentiation of moderate COVID-19 cases from mild cases is beneficial for early treatment and saves medical resources during the pandemic. We attempted to construct a model to predict the occurrence of moderate COVID-19 through a retrospective study.

METHODS

In this retrospective study, clinical data from patients with COVID-19 admitted to Hainan Western Central Hospital in Danzhou, China, between August 1, 2022, and August 31, 2022, was collected, including sex, age, signs on admission, comorbidities, imaging data, post-admission treatment, length of stay, and the results of laboratory tests on admission. The patients were classified into a mild-to-moderate-type group according to WHO guidance. Factors that differed between groups were included in machine learning models such as Bernoulli Naïve Bayes (BNB), linear discriminant analysis, support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR) models. These models were compared to select the optimal model with the best predictive efficacy for moderate COVID-19. The predictive performance of the models was assessed using the area under the curve (AUC), sensitivity, specificity, and calibration plot.

RESULTS

A total of 231 patients with COVID-19 were included in this retrospective analysis. Among them, 152 (68.83%) were mild types, 72 (31.17%) were moderate types, and there were no patients with severe or critical types. A logistic regression model combined with age, respiratory rate (RR), lactate dehydrogenase (LDH), D-dimer, and albumin was selected to predict the occurrence of moderate COVID-19. The receiver operating characteristic curve (ROC) showed that AUC, sensitivity, and specificity in the model were 0.719, 0.681, and 0.635, respectively, in predicting moderate COVID-19. Calibration curve analysis revealed that the predicted probability of the model was in good agreement with the true probability. Stratified analysis showed better predictive efficacy after modeling for people aged ≤66 years (AUC = 0.7656) and a better calibration curve.

CONCLUSION

The LR model, combined with age, RR, D-dimer, LDH, and albumin, can predict the occurrence of moderate COVID-19 well, especially for patients aged ≤66 years.

Role of biomarkers in prognostication of moderate and severe COVID-19 cases

Background

COVID-19 pandemic demanded upgrading of laboratory medicine to limit morbidity, disability and mortality from moderate and severe SARS-COV-2 infections.

Objective

To assess among moderate and severe COVID-19 patients, C-reactive protein (CRP), procalcitonin (PCT), ferritin, D-dimer, interleukin 6 (IL-6), lactate dehydrogenase (LDH), total and differential leucocyte count (TLC and DLC), neutrophil-to-lymphocyte ratio (NLR), absolute platelet count (APC), prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalized ratio (INR) to find their interdependence and role in prognosis.

Methods

This open label analytical cross-sectional noninterventional study evaluated array of independent biochemical, haematological and coagulopathy markers, viz. CRP, PCT, ferritin, D-dimer, IL-6, LDH, TLC, DLC, NLR, absolute platelet count, PT, APTT and INR on consecutive 100 patients with diagnosis of moderate and severe COVID-19 from July to August 2021.

Results

In our study, on consecutive designated 100 cases (55 cases moderate and 45 cases severe), more severity were reported as the age progressed; gender difference was not noted. Among independent markers, CRP, PCT, ferritin, D-dimer, IL-6 and LDH had statistically significant relation in comparison with severity of the disease as Chi-square calculated value (P < 0.05). TLC, DLC and APC showed no significant relation in comparison with severity of the disease; NLR had highly significant relation. PT showed significant relation in comparison with severity, though APTT and INR did not show significant relation.

Conclusion

Our research group felt that CRP, PCT, ferritin, D-dimer, IL-6, LDH and NLR should be in included in clinical practice guidelines to prognosticate COVID-19 cases. Furthermore, translational researches are needed at all levels of healthcare to improve validity for practices of primary care physicians.

The effect of quercetin supplementation on clinical outcomes in COVID-19 patients: A systematic review and meta-analysis

Coronavirus disease (COVID-19) affects both the respiratory system and the body as a whole. Natural molecules, such as flavonoid quercetin, as potential treatment methods to help patients combat COVID-19. The aim of this systematic review and meta-analysis is to give a comprehensive overview of the impact of quercetin supplementation on inflammatory factors, hospital admission, and mortality of patients with COVID-19. The search has been conducted on PubMed, Scopus, Web of Science, EMBASE, and the Cochrane Library using relevant keywords until August 25, 2023. We included randomized controlled trials (RCTs) comparing COVID-19 patients who received quercetin supplementation versus controls. We included five studies summarizing the evidence in 544 patients. Meta-analysis showed that quercetin administration significantly reduced LDH activity (standard mean difference (SMD): -0.42, 95% CI: -0.82, -0.02, I 2 = 48.86%), decreased the risk of hospital admission by 70% (RR: 0.30, 95% CI: 0.14, 0.62, I 2 = 00.00%), ICU admission by 73% (RR: 0.27, 95% CI: 0.09, 0.78, I 2 = 20.66%), and mortality by 82% (RR: 0.18, 95% CI: 0.03, 0.98, I 2 = 00.00%). No significant changes in CRP, D-dimmer, and ferritin were found between groups. Quercetin was found to significantly reduce LDH levels and decrease the risk of hospital and ICU admission and mortality in patients with COVID-19 infection.

Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection

AIM

To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance.

RESULTS

Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9-37), while the low-risk group did not reach the median survival of 50% (p < 0.001).

CONCLUSIONS

A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.

Eosinophil recovery in hospitalized COVID-19 patients is associated with lower rates of ICU admission and in-hospital mortality: An observational cohort analysis

BACKGROUND

Admission eosinopenia (<100 cells/μL) is associated with poor clinical outcomes in hospitalized COVID-19 patients. However, the effects of eosinophil recovery (defined as reaching ≥50 eosinophils/μL) during hospitalization on COVID-19 outcomes have been inconsistent.

METHODS

The study included 1,831 patients admitted to UCLA hospitals between February 2020 and February 2021 with PCR-confirmed COVID-19. Using competing risk regression and modeling eosinophil recovery as a time-dependent covariate, we evaluated the longitudinal relationship between eosinophil recovery and in-hospital outcomes including ICU admission, need for mechanical ventilation, and in-hospital mortality. All analyses were adjusted for covariates including age, BMI, tobacco smoke exposure, comorbidities known to be risk factors for COVID-19 mortality, and treatments including dexamethasone and remdesivir.

RESULTS

Eosinophil recovery was evaluated in patients with <50 eosinophils/μL on admission (n = 1282). These patients cumulatively amassed 11,633 hospital patient-days; 3,985 of those days qualified as eosinophil recovery events, which were represented by 781 patients achieving at least one instance of eosinophil recovery during hospitalization. Despite no significant difference in the rate of mechanical ventilation, eosinophil recoverers had significantly lower rates of in-hospital mortality (aHR: 0.44 [0.29, 0.65], P = 0.001) and ICU admission (aHR: 0.25 [0.11, 0.61], P = 0.002).

CONCLUSION

Trending eosinophil counts during hospitalization is simple and can be performed in resource-limited healthcare settings to track the inflammatory status of a patient. Lack of eosinophil recovery events can identify those at risk for future progression to severe COVID.

Lactate dehydrogenase contribution to symptom persistence in long COVID: A pooled analysis

There's critical need for risk predictors in long COVID. This meta-analysis evaluates the evidence for an association between plasma lactate dehydrogenase (LDH) and long COVID and explores the contribution of LDH to symptoms persistent across the distinct post-acute sequelae of COVID-19 (PASC) domains. PubMed, EMBASE, Web of Science, and Google Scholar were searched for articles published up to 20 March 2023 for studies that reported data on LDH levels in COVID-19 survivors with and without PASC. Random-effect meta-analysis was employed to estimate the standardized mean difference (SMD) with corresponding 95% confidence interval of each outcome. There were a total of 8289 study participants (3338 PASC vs. 4951 controls) from 46 studies. Our meta-analysis compared to the controls showed a significant association between LDH elevation and Resp-PASC [SMD = 1.07, 95%CI = 0.72, 1.41, p = 0.01] but not Cardio-PASC [SMD = 1.79, 95%CI = -0.02, 3.61, p = 0.05], Neuro-PASC [SMD = 0.19, 95%CI = -0.24, 0.61, p = 0.40], and Gastrointestinal-PASC [SMD = 0.45, 95%CI = -1.08, 1.98, p = 0.56]. This meta-analysis suggests elevated LDH can be used for predicting Resp-PASC, but not Cardio-PASC, Neuro-PASC or gastrointestinal-PASC. Thus, elevated plasma LDH following COVID infection may be considered as a disease biomarker.

Serum KL-6 as a Candidate Predictor of Outcome in Patients with SARS-CoV-2 Pneumonia

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-infection is associated with an extremely variable disease course. When interstitial pneumonia (IP) occurs, it can lead to acute respiratory distress syndrome and death. Serum Krebs von den Lungen-6 (KL-6) is an established marker of IP, but its role as a marker of SARS-CoV-2 pneumonia is debated. This bicentric study included 157 patients with SARS-CoV-2 pneumonia. The WHO Ordinal Scale for Clinical Improvement (0-10 points) was used to classify the clinical course. Serum samples were collected at admission, and on days 3 and 7 of hospitalization. KL-6 was measured by using automated chemiluminescence immunoassay. A total of 68 patients developed a severe SARS-CoV-2 pneumonia, 135 of them required oxygen, and 15 died during hospitalization. The patients requiring non-invasive ventilation, invasive ventilation, or extracorporeal membrane oxygenation had significantly higher serum KL-6 levels at admission. The serum KL-6 levels were tendentially higher in patients who died than in those who survived. Logistic regression identified serum KL-6 at a cut-off of 335 U/mL at admission as a significant predictor of severe SARS-CoV-2 pneumonia outcome. Serum KL-6 seems to be a candidate biomarker for the clinical routine to stratify patients with SARS-CoV-2 pneumonia for the risk of a severe disease outcome or death.

IL-6 and Neutrophil/Lymphocyte Ratio as Markers of ICU Admittance in SARS-CoV-2 Patients with Diabetes

Inflammation along with coagulation disturbances has an essential role in the evolution towards a severe disease in patients with the coronavirus disease 2019 (COVID-19). This study aimed to evaluate inflammatory and coagulation biomarkers when predicting the need to visit an intensive care unit (ICU) in diabetes mellitus (DM) patients. In a retrospective study, laboratory parameters were examined for 366 participants: ICU = 90, of which 44 patients had DM and no ICU admittance = 276. The ability of inflammatory and coagulation markers to distinguish the severity of COVID-19 was determined using univariate and multivariate regression analysis. In all patients, lactate dehydrogenase was the only predictor for ICU admittance in the multivariate analysis. In the DM group, the results showed that the interleukin (IL)-6 and neutrophil/lymphocyte ratio (NLR) values at admission could predict the need for ICU admittance. Even though there were significant differences between the ICU and no ICU admittance groups regarding the coagulation markers, they could not predict the severity of the disease in DM patients. The present study showed for the first time that the IL-6 and NLR admission values could predict ICU admittance in DM patients. This finding could help clinicians manage the infection more easily if the COVID-19 pandemic strikes again.

Characteristics and outcomes of COVID-19 in patients with plasma cell dyscrasias during the first Omicron wave in Beijing since December 2022: a retrospective study at a National Clinical Research Center for Hematologic Disease

The wave of coronavirus disease 2019 (COVID-19) with Omicron variant reached its first peak in Beijing, China in December 2022. We delineated characteristics and factors associated with adverse outcome of patients with plasma cell dyscrasias (PCDs) and COVID-19 during the first month of the wave. A total of 104 patients with a median age of 65 years were included in the study, with multiple myeloma (74%, n=77) and primary Immunoglobulin light chain amyloidosis (16.3%, n=17) being the two most common disease. Overall, severe or critical COVID-19 was developed in 18 (17.3%) patients, with a total all-cause mortality rate of 4.8% (n=5). The vaccination coverage was 41% and 48.1%, before and during the upsurge of Omicron, respectively, calling for the improvement of vaccination in PCD patients. Multivariable analysis revealed that age was the only independent risk factors (OR=1.14, 95% CI: 1.06-1.26, p = 0.002) associated with the development of severe or critical disease. Among patients with severe or critical group, low levels of albumin (HR=18.29; 95% CI: 1.82-183.44, p = 0.013) and high levels of lactic dehydrogenase (LDH) (HR=0.08; 95% CI: 0.01-0.65, p = 0.018) were associated with longer time to negative conversion of COVID-19.

[LDH-neutrophil-lymphocyte index as a predictor of 28-day mortality in patients with COVID-19]

Background

Coronavirus disease 2019 (COVID-19) represents the greatest health crisis of our times; it was declared by WHO a pandemic in March 2020. The risk of presenting a severe disease is inter-individual, since it varies according to age, comorbidities, and immunological status, in addition to the type of SARS-CoV-2 variant. The neutrophil/lymphocyte ratio (NLR) and lactic dehydrogenase (LDH) are widely used markers to assess the severity and predict the course of the disease in patients with COVID-19, with a direct relationship of higher value-worse prognosis.

Objective

To verify if the LDH-neutrophil-lymphocyte index calculated from laboratory tests taken within the first 24 hours of admission is useful as a predictor of 28-day mortality in adult patients diagnosed with COVID-19.

Material and methods

Retrospective and analytical cohort study. All consecutive patients over 16 years of any gender, admitted to a tertiary care center from March 2020 to March 2021, who had a diagnosis of COVID-19 with a positive PCR for SARS-CoV-2, were included.

Results

Higher levels of the LDHNL index were associated with higher mortality in patients hospitalized for COVID-19 (Q2 vs. Q1: RR 1.52 [1.24-1.87], p < 0.05; Q3 vs. Q1: RR 1.87 [1.55-2.25], p < 0.05; and Q4 vs. Q1: RR 2.74 [2.22-3-39], p < 0.05).

Conclusions

The serum LDHNL index taken in the first 24 hours of admission can help to predict early the risk of mortality in hospitalized patients with COVID-19.

Prognostic value of R wave peak time in COVID-19 pneumonia

BACKGROUND

R wave peak time (RWPT) is also known as intrinsicoid deflection time or ventricular activation time. It shows the conduction time from the endocardium in the ventricle to the epicardium. It provides diagnostic and prognostic information for many cardiovascular diseases, such as RWPT prolongation, left ventricular hypertrophy, volume overload, conduction system abnormalities, and myocardial ischemia. Objectives The aim of this study is to investigate the relationship between COVID-19 mortality and RWPT in superficial ECG.

METHODS

This study retrospectively examined 640 patients diagnosed with COVID-19 and treated in an intensive care unit at a single center between January 2021 and June 2022. All patients included in the study had clinical and radiological characteristics and signs of COVID-19 pneumonia.

RESULTS

640 patients included in the study were divided into 2 groups: surviving and deceased. There were 510 patients in the surviving group and 130 patients in the deceased group. The deceased group was found to be significantly older. The number of patients with COPD was higher in the deceased group. Troponin, lactate dehydrogenase (LDH), C-reactive protein (CRP), D-dimer and T-peak to T-end interval(Tpe) and RWPT were found to be significantly increased in the deceased group. In binary logistic regression analysis; age, COPD, LDH, CRP, troponin, D-dimer, Tpe interval, RWPT were determined as independent risk factors for mortality.

CONCLUSIONS

Prolonged RWPT is useful in risk stratification for COVID-19 pneumonia mortality.

A Predictive Rule for COVID-19 Pneumonia Among COVID-19 Patients: A Classification and Regression Tree (CART) Analysis Model

BACKGROUND

In this study, we aimed to identify predictive factors for coronavirus disease 2019 (COVID-19) patients with complicated pneumonia and determine which COVID-19 patients should undergo computed tomography (CT) using classification and regression tree (CART) analysis.

METHODS

This retrospective cross-sectional survey was conducted at a university hospital. We recruited patients diagnosed with COVID-19 between January 1 and December 31, 2020. We extracted clinical information (e.g., vital signs, symptoms, laboratory results, and CT findings) from patient records. Factors potentially predicting COVID-19 pneumonia were analyzed using Student's t-test, the chi-square test, and a CART analysis model.

RESULTS

Among 221 patients (119 men (53.8%); mean age, 54.59±18.61 years), 160 (72.4%) had pneumonia. The CART analysis revealed that patients were at high risk of pneumonia if they had C-reactive protein (CRP) levels of >1.60 mg/dL (incidence of pneumonia: 95.7%); CRP levels of ≤1.60 mg/dL + age >35.5 years + lactate dehydrogenase (LDH)>225.5 IU/L (incidence of pneumonia: 95.5%); and CRP levels of ≤1.60 mg/dL + age >35.5 years + LDH≤225.5 IU/L + hemoglobin ≤14.65 g/dL (incidence of pneumonia: 69.6%). The area of the curve of the receiver operating characteristic of the model was 0.860 (95% CI: 0.804-0.915), indicating sufficient explanatory power.

CONCLUSIONS

The present results are useful for deciding whether to perform CT in COVID-19 patients. High-risk patients such as those mentioned above should undergo CT.

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems

OBJECTIVE

We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems.

METHODS

A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.

RESULTS

The final population comprised 743 patients (mean age 65  ±  17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores.

CONCLUSION

Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time.

ADVANCES IN KNOWLEDGE

Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

Increased serum interleukin-6 and lactate dehydrogenase levels among nonsurvival severe COVID-19 patients when compared to survival ones

BACKGROUND

Accurate and rapid laboratory diagnosis of COVID-19 infection and its deterioration is one of the milestones of pandemic control. Therefore, this study aimed to compare the diagnostic and prognostic accuracy of the mainly used laboratory biomarkers (WBCS, neutrophil and lymphocyte percentages, CRP, ferritin, IL-6, D-dimer, procalcitonin, and LDH) in the sera of severe COVID-19 Egyptian patients to assess the most appropriate biomarker used in severe COVID-19 patients.

METHODS

A total of 180 unvaccinated severe COVID-19 patients were enrolled in our study. Demographic data, hospitalization time, medical history, oxygen saturation, respiratory rate, oxygen supply, laboratory findings, and thorax tomography of the patients were obtained retrospectively from the hospital's electronic information system.

RESULTS

Our results revealed that the levels of neutrophil percentage, CRP, IL-6, PCT, and LDH were significantly increased while lymphocyte percentage was significantly decreased among nonsurvival severe COVID-19 patients when compared with survival ones. By using ROC curve analysis, IL-6, and LDH are the most sensitive and specific markers for the prediction of bad prognosis and mortality among severe COVID-19 patients with 100% and 93% sensitivity and 93.7% specificity; respectively. IL-6 and LDH showed significant correlations with the other parameters, which suggested their association with the severity of COVID-19.

CONCLUSION

By using survival severe COVID-19 patients as a control group, our results showed that blood neutrophil percentage, serum CRP, IL-6, PCT, and LDH were significantly increased in non-survivors as compared to survivors. As biomarkers, our results revealed that IL-6 and LDH are good predictors of mortality among severe COVID-19 patients.

Unmasking the enigma: An in-depth analysis of COVID-19 impact on the pediatric population

OBJECTIVES

COVID-19, caused by the novel coronavirus, has had a profound and wide-reaching impact on individuals of all age groups across the globe, including children. This review article aims to provide a comprehensive analysis of COVID-19 in children, covering essential topics such as epidemiology, transmission, pathogenesis, clinical features, risk factors, diagnosis, treatment, vaccination, and others. By delving into the current understanding of the disease and addressing the challenges that lie ahead, this article seeks to shed light on the unique considerations surrounding COVID-19 in children and contribute to a deeper comprehension of this global health crisis affecting our youngest population.

METHODS

A comprehensive literature search was conducted to gather the most recent and relevant information regarding COVID-19 in children. Multiple renowned databases, including MEDLINE, PubMed, Scopus, as well as authoritative sources such as the World Health Organization (WHO), the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the National Institutes of Health (NIH) websites and others were thoroughly searched. The search included articles, guidelines, reports, clinical trials results and expert opinions published within the past three years, ensuring the inclusion of the latest research findings on COVID-19 in children. Several relevant keywords, including "COVID-19," "SARS-CoV-2," "children," "pediatrics," and related terms were used to maximize the scope of the search and retrieve a comprehensive set of articles.

RESULTS AND CONCLUSION

Three years since the onset of the COVID-19 pandemic, our understanding of its impact on children has evolved, but many questions remain unanswered. While SAR-CoV-2 generally leads to mild illness in children, the occurrence of severe cases and the potential for long-term effects cannot be overlooked. Efforts to comprehensively study COVID-19 in children must continue to improve preventive strategies, identify high-risk populations, and ensure optimal management. By unraveling the enigma surrounding COVID-19 in children, we can strive towards safeguarding their health and well-being in the face of future global health challenges.

Inflammatory markers and COVID-19 disease progression

BACKGROUND

The COVID-19 pandemic has resulted in a global humanitarian crisis. Despite ongoing research, transmission risks and many disease characteristics remained unclear. Most patients have displayed elevated levels of certain inflammatory markers, which we sought to investigate further in relation to disease severity. The aim of this study was to examine the correlation between inflammatory markers and the severity of COVID-19 among patients.

METHODS

We conducted a cross-sectional study from April to September 2020, involving 143 COVID-19 PCR-positive patients from Ziauddin Hospital. Electronic patient records provided data on demographics, clinical status, and laboratory results.

RESULTS

The majority of PCR-positive patients were elderly males with comorbidities such as diabetes and hypertension. Almost all patients exhibited increased levels of various inflammatory markers, with procalcitonin (97.2%) being the most common. Statistically significant differences were observed in the levels of TLC (p = 0.005), CRP (p = 0.001), LDH (p = 0.001), Ferritin (p = 0.001), D-dimer (p = 0.001), and procalcitonin (p = 0.028), in relation to COVID-19 severity.

CONCLUSIONS

The data suggest a significant association between levels of inflammatory markers and COVID-19 severity. All markers, except procalcitonin, demonstrated a significant correlation with disease severity. These results could enhance our understanding of COVID-19 pathogenesis and help predict and manage severe cases.

Correlations between serum leptin levels and classical biomarkers in SARS-CoV-2 infection, in critically ill patients

BACKGROUND

Altered levels of some blood markers might be linked with the degree of severity and mortality of patients with SARS-CoV-2 infection. This study aimed to find out if there are correlations between serum leptin levels and classical biomarkers.

MATERIALS AND METHODS

We present a single-center observational cohort study on SARS-CoV-2 infected patients. The study was conducted at Infectious Diseases Clinic of Academic Emergency Hospital Sibiu, from May through November 2020. In this study, we retrospectively analyzed 54 patients, all with confirmed SARS-CoV-2 infection.

RESULTS

Our results revealed that there is a negative correlation between serum leptin and Interleukin-6 levels and a positive correlation between serum leptin and blood glucose levels. A positive correlation between ferritin and lactate dehydrogenase levels was also observed. No correlation was found between leptin and other biomarkers such as ferritin, neutrophil/lymphocyte ratio, lactate dehydrogenase, C-reactive protein, fibrinogen, erythrocyte sedimentation rate, or D-dimer.

CONCLUSIONS

Further studies need to be conducted to investigate the role of leptin in SARS-CoV-2 infection. The results of this research could contribute to the introduction of the determination of serum leptin levels in the routine evaluation of patients with critical illness.

Clinical characteristics and predictors for in-hospital mortality in adult COVID-19 patients: A retrospective single center cohort study in Vilnius, Lithuania

BACKGROUND

The COVID-19 infection had spread worldwide causing many deaths. Mortality rates and patients' characteristics varied within and between countries, making it important to understand the peculiarities of different populations. The aim of this study was to identify the main predictors associated with in-hospital mortality due to COVID-19 in Vilnius, Lithuania.

MATERIALS AND METHODS

This was a retrospective observational cohort study conducted at Vilnius University Hospital Santaros Clinics, Lithuania. The study included SARS-CoV-2 positive patients aged over 18 years and hospitalized between March 2020 and May 2021. Depersonalized data were retrieved from electronic medical records. The predictive values of laboratory parameters were evaluated using ROC analysis. Multivariable binary logistic regression was performed to reveal predictors of in-hospital mortality due to COVID-19.

RESULTS

Among 2794 patients, 54.4% were male, the age median was 59 years (IQR 48-70), 47.4% had at least one comorbidity. The most common comorbidities were arterial hypertension (36.9%) and diabetes mellitus (13.7%). Overall, 12.7% of patients died. Multivariable regression revealed that age (OR 1.04, 95%CI 1.02-1.06), congestive heart failure (OR 3.06, 95%CI 1.96-4.77), obesity (OR 3.90, 95%CI 2.12-7.16), COPD (OR 2.92, 95%CI 1.12-7.60), previous stroke (OR 5.80, 95%CI 2.07-16.21), urea >7.01 mmol/l (OR 2.32, 95%CI 1.47-3.67), AST/ALT >1.49 (OR 1.54, 95%CI 1.08-2.21), LDH >452.5 U/l (OR 2.60, 95%CI 1.74-3.88), CRP >92.68 mg/l (OR 1.58, 95%CI 1.06-2.35), IL-6 >69.55 ng/l (OR 1.62, 95%CI 1.10-2.40), and troponin I >18.95 ng/l (OR 2.04, 95%CI 1.38-3.02), were associated with increased risk for in-hospital mortality in COVID-19 patients.

CONCLUSIONS

Age, congestive heart failure, obesity, COPD, prior stroke, and increased concentration of urea, LDH, CRP, IL-6, troponin I, ALT to AST ratio were identified to be the predictors for in-hospital mortality of COVID-19 patients.

Reliability of the respiratory rate and oxygenation index for successful high-flow nasal cannula support in coronavirus disease pneumonia: a retrospective cohort study

BACKGROUND

High-flow nasal cannula (HFNC) therapy is an important non-invasive respiratory support in acute respiratory failure, including coronavirus disease (COVID-19) pneumonia. Although the respiratory rate and oxygenation (ROX) index is a simple and useful predictor for HFNC failure and mortality, there is limited evidence for its use in patients with COVID-19 pneumonia. We aimed to evaluate the ROX index as a predictor for HFNC failure in patients with COVID-19 pneumonia. We also evaluated the ROX index as a predictor for 28-day mortality.

METHODS

In this single-center, retrospective, cohort study, 248 patients older than 18 years of age with COVID-19 pneumonia received HFNC therapy for acute respiratory failure. The ROX index was evaluated within 4 h from the start of HFNC therapy. Past medical history, laboratory data, and the ROX index were evaluated as predictors for HFNC failure and 28-day mortality.

RESULTS

The ROX index < 4.88 showed a significantly high risk ratio for HFNC failure (2.13 [95% confidence interval [CI]: 1.47 - 3.08], p < 0.001). The ROX index < 4.88 was significantly associated with 28-day mortality (p = 0.049) in patients with COVID-19 pneumonia receiving HFNC therapy. Age, chronic hypertension, high lactate dehydrogenase level, and low ROX index showed significantly high risk ratio for HFNC failure. C-reactive protein level and low ROX index were predictors of 28-day morality.

CONCLUSION

The ROX index is a useful predictor for HFNC success and 28-day mortality in patients with COVID-19 pneumonia receiving HFNC therapy.

TRIAL REGISTRATION

An independent ethics committee approved the study (Research Ethics Review Committee of Kobe City Medical Center General Hospital [number: zn220303; date: February 21, 2022]), which was performed in accordance with the Declaration of Helsinki, Guidelines for Good Clinical Practice.

Analysis of altered level of blood-based biomarkers in prognosis of COVID-19 patients

INTRODUCTION

Immune and inflammatory responses developed by the patients with Coronavirus Disease 2019 (COVID-19) during rapid disease progression result in an altered level of biomarkers. Therefore, this study aimed to analyze levels of blood-based biomarkers that are significantly altered in patients with COVID-19.

METHODS

A cross-sectional study was conducted among COVID-19 diagnosed patients admitted to the tertiary care hospital. Several biomarkers-biochemical, hematological, inflammatory, cardiac, and coagulatory-were analyzed and subsequently tested for statistical significance at P<0.01 by using SPSS version 17.0.

RESULTS

A total of 1,780 samples were analyzed from 1,232 COVID-19 patients (median age 45 years [IQR 33-57]; 788 [63.96%] male). The COVID-19 patients had significantly (99% Confidence Interval, P<0.01) elevated levels of glucose, urea, alanine transaminase (ALT), aspartate aminotransaminase (AST), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), white blood cell (WBC), C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), ferritin, D-Dimer, and creatinine phosphokinase-MB (CPK-MB) compared to the control group. However, the levels of total protein, albumin, and platelets were significantly (P<0.01) lowered in COVID-19 patients compared to the control group. The elevated levels of glucose, urea, WBC, CRP, D-Dimer, and LDH were significantly (P<0.01) associated with in-hospital mortality in COVID-19 patients.

CONCLUSIONS

Assessing and monitoring the elevated levels of glucose, urea, ALT, AST, ALP, WBC, CRP, PCT, IL-6, ferritin, LDH, D-Dimer, and CPK-MB and the lowered levels of total protein, albumin, and platelet could provide a basis for evaluation of improved prognosis and effective treatment in patients with COVID-19.