Procalcitonin (PCT) is recognized as an inflammatory biomarker, often elevated in COVID-19 pneumonia alongside other biomarkers. Understanding its association with severe outcomes and comparing its predictive ability with other biomarkers is crucial for clinical management.

This retrospective multicenter observational study aimed to investigate the association between PCT levels and adverse outcomes in hospitalized COVID-19 patients. Additionally, it sought to compare the predictive performance of various biomarkers.

The study analyzed data from the Society of Critical Care Medicine (SCCM) Viral Infection and Respiratory Illness Universal Study (VIRUS) registry, comprising COVID-19 patients hospitalized across multiple Mayo Clinic sites between March 2020 and June 2022.

A total of 7851 adult COVID-19 patients were included. Patients were categorized into 6 groups based on the worst WHO ordinal scale. Multivariate models were constructed using peak biomarker levels within 72 hours of admission, adjusted for confounders.

Elevated PCT levels were independently associated with increased odds of adverse outcomes, including ICU admission (adjusted odds ratio [aOR] 1.32, 95%CI 1.27-1.38), IMV requirement (aOR 1.35, 95%CI: 1.28-1.42), and in-hospital mortality (aOR 1.30, 95%CI: 1.22-1.37). A 3.48-fold increase in IMV requirement and 3.55 times increase in in-hospital mortality were noted with peak PCT ⩾ 0.25 ng/ml. Similar associations were observed with other biomarkers like NLR (AUC 0.730), CRP, IL-6, LDH (AUC 0.800), and D-dimer (AUC 0.719). Models incorporating NLR, LDH, D-dimer, and PCT demonstrated the highest predictive accuracy, with a combined model exhibiting an area under the curve (AUC) of 0.826 (95%CI 0.803-0.849).

Higher PCT levels were significantly linked to worse outcomes in COVID-19 patients, emphasizing its potential as a prognostic marker. Biomarker-based predictive models, particularly those including PCT, showed promising utility for risk assessment and clinical decision-making. Further prospective studies are warranted to validate these findings on a larger scale.

Critically ill COVID-19 patients are usually subjected to clinical, laboratory, and radiological diagnostic procedures resulting in numerous findings. Utilizing these findings as indicators for disease progression or outcome prediction is particularly intriguing.

Exploring the significance of dynamic changes in haematological and biochemical parameters in predicting the mortality of critically ill COVID-19 patients.

The present study was a prospective and observational study involving mechanically ventilated 75 critically ill adult COVID-19 patients with hypoxemic respiratory failure. The collected data included baseline patient characteristics, treatment options, outcome, and laboratory findings at admission and 7 days after. The dynamics of the obtained findings were compared between survivors and non-survivors.

The 28-day survival rate was 61.3%. In the group of non-survivors significant dynamic changes were found for C-reactive protein (p= 0.001), interleukin-6 (p< 0.001), lymphocyte (p= 0.003), neutrophil-lymphocyte ratio (p= 0.003), platelets (p< 0.001), haemoglobin (p< 0.001), iron (p= 0.012), and total iron-binding capacity (p< 0.001). Statistically significant changes over time were found for ferritin (p= 0.010), D-dimer (p< 0.001), hs-troponin T (p< 0.002), lactate dehydrogenase (p= 0.001), glucose (p= 0.023), unsaturated iron-binding capacity (p= 0.008), and vitamin D (p< 0.001).

The dynamic changes in inflammatory, haematological and biochemical parameters can predict disease severity, and outcome.

The soluble receptor for advanced glycation end-products (sRAGE) is a marker of alveolar type I cell injury associated with outcomes in COVID-19 pneumonia. How plasma sRAGE changes over time and whether it remains associated with long-term clinical outcomes beyond a single measurement in COVID-19 have not been well studied. We studied two cohorts in randomized clinical trials of monoclonal antibody treatment for COVID-19 (bamlanivimab and tixagevimab/cilgavimab). We first studied the association between baseline plasma sRAGE and 90-day clinical outcomes, which had been previously demonstrated in the bamlanivimab cohort, among hospitalized patients with COVID-19 supported with high-flow nasal oxygen (HFNO) or noninvasive ventilation (NIV) in the tixagevimab/cilgavimab study. Next, we investigated the relationship between day 3 sRAGE and 90-day outcomes and how plasma sRAGE changes over the first 3 days of hospitalization in both clinical trial cohorts. We found that plasma sRAGE in the highest quartile in the HFNO/NIV participants in the tixagevimab/cilgavimab trial was associated with a significantly lower rate of 90-day sustained recovery [recovery rate ratio = 0.31, 95% confidence interval (CI) = 0.14-0.71, P = 0.005] and with a significantly higher rate of 90-day mortality (hazard ratio = 2.49, 95% CI = 1.15-5.43, P = 0.021) compared with the lower three quartiles. Day 3 plasma sRAGE in both clinical trial cohorts remained associated with 90-day clinical outcomes. The trajectory of sRAGE was not influenced by treatment assignment. Our results indicate that plasma sRAGE is a valuable prognostic marker in COVID-19 up to 3 days after initial hospital presentation.NEW & NOTEWORTHY The soluble receptor for advanced glycation end-products (sRAGE) is a marker of alveolar type I epithelial cell injury associated with clinical outcomes in acute respiratory distress syndrome and, more recently, in hospitalized subjects with COVID-19. How plasma sRAGE changes over time and whether plasma sRAGE remains associated with long-term clinical outcomes beyond a single baseline measurement in patients with COVID-19 have not been well studied.

Experimental and observational studies suggest that circulating micronutrients, including vitamin D (VD), may increase COVID-19 risk and its associated outcomes. Mendelian randomization (MR) studies provide valuable insight into the causal relationship between an exposure and disease outcomes.

The aim was to conduct a systematic review and meta-analysis of causal inference studies that apply MR approaches to assess the role of these micronutrients, particularly VD, in COVID-19 risk, infection severity, and related inflammatory markers.

Searches (up to July 2023) were conducted in 4 databases.

The quality of the studies was evaluated based on the MR-STROBE guidelines. Random-effects meta-analyses were conducted where possible.

There were 28 studies (2 overlapped) including 12 on micronutrients (8 on VD) and COVID-19, 4 on micronutrients (all on VD) and inflammation, and 12 on inflammatory markers and COVID-19. Some of these studies reported significant causal associations between VD or other micronutrients (vitamin C, vitamin B6, iron, zinc, copper, selenium, and magnesium) and COVID-19 outcomes. Associations in terms of causality were also nonsignificant with regard to inflammation-related markers, except for VD levels below 25 nmol/L and C-reactive protein (CRP). Some studies reported causal associations between cytokines, angiotensin-converting enzyme 2 (ACE2), and other inflammatory markers and COVID-19. Pooled MR estimates showed that VD was not significantly associated with COVID-19 outcomes, whereas ACE2 increased COVID-19 risk (MR odds ratio = 1.10; 95% CI: 1.01-1.19) but did not affect hospitalization or severity of the disease. The methodological quality of the studies was high in 13 studies, despite the majority (n = 24) utilizing 2-sample MR and evaluated pleiotropy.

MR studies exhibited diversity in their approaches but do not support a causal link between VD/micronutrients and COVID-19 outcomes. Whether inflammation mediates the VD-COVID-19 relationship remains uncertain, and highlights the need to address this aspect in future MR studies exploring micronutrient associations with COVID-19 outcomes.

PROSPERO registration no. CRD42022328224.

The coronavirus disease 2019 (COVID-19) pandemic has presented an unprecedented challenge to the global healthcare system, prompting an urgent need to understand the factors influencing patient outcomes. Critical to improving treatment protocols and reducing mortality rates is an in-depth assessment of the clinical profile, laboratory findings, and management strategies employed in treating COVID-19 patients. This research provides valuable insights that could influence future therapeutic approaches and public health strategies, ultimately aiming to reduce the morbidity and mortality associated with COVID-19. The study aimed to assess mortality predictors in patients admitted to the intensive care unit (ICU) due to COVID-19.

This study employed a retrospective approach, utilizing patient data from medical records. The collected data encompassed demographic and clinical profiles and details regarding the duration of admission and treatment. The evaluation focused on patients admitted to the ICU for COVID-19 between March 2020 and July 2021, with confirmation through real-time reverse transcriptase polymerase chain reaction (RT-PCR). Rigorous statistical analysis was conducted to compare outcomes between discharged and deceased patients.

The study included a total of 202 ICU patients admitted for COVID-19. Among the cases, 147 (72.8%) were males and 55 (27.2%) were females. The mean age was 58.42 years, with a standard deviation of 15.59 years. Fever (92%) emerged as the most frequently encountered symptom, followed by cough (48.5%) and dyspnea (35%). Patients with underlying comorbidities exhibited a higher susceptibility to developing a severe or critical disease. Hypertension (n = 38) was identified as the most prevalent comorbidity, followed by type 2 diabetes mellitus (n = 36). Hypertension has demonstrated a significant association with disease outcomes. Body temperature, respiratory rate, oxygen saturation, and mechanical ventilation played substantial roles in patient outcomes.

The study revealed that underlying comorbidities and complications, such as acute respiratory distress syndrome (ARDS), were linked to significantly higher mortality rates among COVID-19 patients. Abnormal laboratory parameters also exhibited significant differences in the outcomes of ICU patients.

The ability of the SARS-CoV-2 virus to cause DNA damage in infected humans requires its study as a potential indicator of COVID-19 progression. DNA damage was studied in leukocytes of 65 COVID-19 patients stratified by sex, age, and disease severity in relation to demographic, clinical, and laboratory parameters. In a combined group of COVID-19 patients, DNA damage was shown to be elevated compared to controls (12.44% vs. 5.09%, p < 0.05). Severe cases showed higher DNA damage than moderate cases (14.66% vs. 10.65%, p < 0.05), and males displayed more damage than females (13.45% vs. 8.15%, p < 0.05). DNA damage is also correlated with international normalized ratio (INR) (r = 0.471, p < 0.001) and creatinine (r = 0.326, p < 0.05). In addition to DNA damage, severe COVID-19 is associated with age, C-reactive protein (CRP), and creatinine. Receiver operating characteristic analysis identified age, INR, creatinine, DNA damage, and CRP as significant predictors of disease severity, with cut-off values of 72.50 years, 1.46 s, 78.0 µmol/L, 9.72%, and 50.0 mg/L, respectively. The results show that DNA damage correlates with commonly accepted COVID-19 risk factors. These findings underscore the potential of DNA damage as a biomarker for COVID-19 severity, suggesting its inclusion in prognostic assessments to facilitate early intervention and improve patient outcomes.

Since the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic has infected nearly 800 million people and caused almost seven million deaths. Obesity was quickly identified as a risk factor for severe COVID-19, ICU admission, acute respiratory distress syndrome, organ support including mechanical ventilation and prolonged length of stay. The relationship among obesity; COVID-19; and respiratory, thrombotic, and renal complications upon admission to the ICU is unclear.

The predominant effect of a hyperinflammatory status or a cytokine storm has been suggested in patients with obesity, but more recent studies have challenged this hypothesis. Numerous studies have also shown increased mortality among critically ill patients with obesity and COVID-19, casting doubt on the obesity paradox, with survival advantages with overweight and mild obesity being reported in other ICU syndromes. Finally, it is now clear that the increase in the global prevalence of overweight and obesity is a major public health issue that must be accompanied by a transformation of our ICUs, both in terms of equipment and human resources. Research must also focus more on these patients to improve their care. In this review, we focused on the central role of obesity in critically ill patients during this pandemic, highlighting its specificities during their stay in the ICU, identifying the lessons we have learned, and identifying areas for future research as well as the future challenges for ICU activity.

Our study aimed to describe the group of severe COVID-19 patients at an institutional level, and determine factors associated with different outcomes.

A retrospective chart review of patients admitted with severe acute hypoxic respiratory failure due to COVID-19 infection. Based on outcomes, we categorized 3 groups of severe COVID-19: (1) Favorable outcome: progressive care unit admission and discharge (2) Intermediate outcome: ICU care (3) Poor outcome: in-hospital mortality.

Eighty-nine patients met our inclusion criteria; 42.7% were female. The average age was 59.7 (standard deviation (SD):13.7). Most of the population were Caucasian (95.5%) and non-Hispanic (91.0%). Age, sex, race, and ethnicity were similar between outcome groups. Medicare and Medicaid patients accounted for 62.9%. The average BMI was 33.5 (SD:8.2). Moderate comorbidity was observed, with an average Charlson Comorbidity index (CCI) of 3.8 (SD:2.6). There were no differences in the average CCI between groups(p = 0.291). Many patients (67.4%) had hypertension, diabetes (42.7%) and chronic lung disease (32.6%). A statistical difference was found when chronic lung disease was evaluated; p = 0.002. The prevalence of chronic lung disease was 19.6%, 27.8%, and 40% in the favorable, intermediate, and poor outcome groups, respectively. Smoking history was associated with poor outcomes (p = 0.04). Only 7.9% were fully vaccinated. Almost half (46.1%) were intubated and mechanically ventilated. Patients spent an average of 12.1 days ventilated (SD:8.5), with an average of 6.0 days from admission to ventilation (SD:5.1). The intermediate group had a shorter average interval from admission to ventilator (77.2 hours, SD:67.6), than the poor group (212.8 hours, SD:126.8); (p = 0.001). The presence of bacterial pneumonia was greatest in the intermediate group (72.2%), compared to the favorable group (17.4%), and the poor group (56%); this was significant (p<0.0001). In-hospital mortality was seen in 28.1%.

Most patients were male, obese, had moderate-level comorbidity, a history of tobacco abuse, and government-funded insurance. Nearly 50% required mechanical ventilation, and about 28% died during hospitalization. Bacterial pneumonia was most prevalent in intubated groups. Patients who were intubated with a good outcome were intubated earlier during their hospital course, with an average difference of 135.6 hours. A history of cigarette smoking and chronic lung disease were associated with poor outcomes.

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates advanced prognostic tools to anticipate disease progression and optimize patient outcomes. This study evaluates the predictive value and diagnostic potential of interleukins interleukin (IL) IL-10, IL-17A, IL1-β, IL-6, chemokine ligand (CXCL), and Monocyte Chemotactic Protein (MCP) for severe coronavirus disease 2019 (COVID-19) and COVID-19 mortality, aiming to correlate cytokine levels with disease severity. Conducted from January 2023 to January 2024, this prospective cohort study involved patients hospitalized with moderate and severe COVID-19 from Romania. This study analyzed statistically significant predictors of severe COVID-19 outcomes. IL-6 and MCP emerged as significant, with hazard ratios (HRs) of 2.35 (95% confidence interval (CI): 1.54-3.59, p = 0.014) and 2.05 (95% CI: 1.22-3.45, p = 0.007), respectively. Compound scores integrating multiple inflammatory markers also demonstrated predictive value; Compound Score 2 had an HR of 2.23 (95% CI: 1.35-3.68, p = 0.002), surpassing most single markers in association with severe disease. Notably, interleukins IL-10 and IL-1β did not show significant associations with disease severity. This study underscores the importance of IL-6 and MCP as robust predictors of severe COVID-19, substantiating their role in clinical assessments to foresee patient deterioration. The utility of compound scores in enhancing predictive accuracy suggests a composite approach may be more effective in clinical settings.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.

The COVID-19 pandemic related to SARS-CoV-2 virus was responsible for global pandemic. The severe form of the disease was linked to excessive activation of immune pathways together with a systemic cytokine storm response and thrombotic venous or arterial complications. Factors predicting severe outcomes including venous and/or pulmonary thrombosis (VT) and death were identified, but the prognostic role of their combination was not addressed extensively.

We investigated the role of prognostic factors from the coagulation or inflammatory pathways to better understand the outcome of the disease.

For this, we prospectively studied 167 SARS-CoV-2-positive patients from admission in intensive care units (ICU) or emergency departments from four academic hospitals over a 14-month period. Besides standard biology, we assessed serum concentrations of inflammatory markers, coagulation factors and peripheral blood cells immunophenotyping.

Thirty-nine patients (23.3%) developed VT and 30 patients (18%) died. By univariate analysis, C-reactive protein (CRP) level > 150 mg/L, interleukin-6 (IL-6) ≥ 20 pg/mL, D-dimers > 1,500 μg/L, ADAMTS13 activity ≤ 50%, Von.

A combination of coagulation and inflammatory markers can refine the prognostication of severe outcome in COVID-19, and could be useful for the initial evaluation of other types of viral infection.

Cytokine dysregulation in COVID-19 patients aged over 60 has been associated to adverse outcomes. While serum levels have been studied, cellular expression, particularly in Afro-Colombians, remains understudied. This research aims to describe cytokine expression in peripheral blood leukocytes and its association with adverse outcomes in COVID-19 patients aged over 60 at Cartagena's referral hospital.

A cohort study was conducted, encompassing severe and critical cases of COVID-19 between November 2021 and February 2022. At baseline, the cellular expression level of cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ was assessed using flow cytometry. Additionally, various biochemical, hematological, and coagulation markers were evaluated. The main outcome was time to death.

Among the 50 enrolled participants, the median age was 76.5 years, 60% were male, 60% were admitted to the ICU, and 42% died. Lactate dehydrogenase and hemoglobin were the only markers that differed between fatal and surviving cases. Regarding cytokines, the level of IL-6 expression was associated with an increased risk of death. Specifically, a one percent increase in the expression was associated with a 7.3% increase in the risk of death. Stratifying the analysis by death and ICU admission, the median expression level remained high in fatal cases who were admitted to the ICU.

Our findings revealed a significant association between high cellular expression levels of IL-6 and an increased risk of mortality. These results provide valuable scientific insights that could inform the prioritization of case management, providing especially advantageous for the vulnerable Afro-Colombian group.

This study aimed to demonstrate how the prognostic nutritional index (PNI) and systemic immune-inflammatory index (SII) help predict the severity and prognosis of patients with type 2 diabetes (T2DM) and coronavirus disease (COVID-19).

This retrospective cohort study included 501 T2DM patients (male, 42.1%; female, 57.9%) who were hospitalized due to COVID-19 between April 2020 and December 2020. The patients were divided into survivors and non-survivors. After comparing demographic and laboratory data between the groups, the correlation of PNI and SII with clinical and laboratory data was evaluated.

The median (interquartile) ages of the non-survivor and survivor groups were 74 (15) and 69 (14) years, respectively, and the difference was significant (p<0.001). The PNI was significantly lower in the non-survivor group than in the survivor group (p<0.001). The SII was significantly higher in the non-survivor group than in the survivor group (p<0.001). PNI was negatively correlated with glucose levels (r=-0.115, p=0.011). If the cut-off PNI value of 29.1 was used, it had a sensitivity and specificity of 76.2% and 76.3%, respectively, in predicting the severity of the illness and the risk of death in T2DM patients.

Consequently, the PNI and SII levels are effective in predicting survival and disease severity in patients with COVID-19 and T2DM.

Acute kidney injury (AKI) occurs frequently in COVID-19 patients and is associated with greater morbidity and mortality. Knowing the risks of AKI allows for identification, prevention, and timely treatment. This study aimed to identify the risk factors associated with AKI in hospitalized patients.

A descriptive, retrospective, cross-sectional, and analytical component study of adult patients hospitalized with COVID-19 from March 1 to December 31, 2020 was carried out. AKI was defined by the creatinine criteria of the KDIGO-AKI guidelines. Information, regarding risk factors, was obtained from electronic medical records.

Out of the 934 patients, 42.93% developed AKI, 60.59% KDIGO-1, and 9.9% required renal replacement therapy. Patients with AKI had longer hospital stay, higher mortality, and required more intensive care unit (ICU) admission, mechanical ventilation, and vasopressor support. Multivariate analysis showed that age (OR 1.03; 95% CI 1.02-1.04), male sex (OR 2.13; 95% CI 1.49-3.04), diabetes mellitus (DM) (OR 1.55; 95% CI 1.04-2.32), chronic kidney disease (CKD) (OR 2.07; 95% CI 1.06-4.04), C-reactive protein (CRP) (OR 1.02; 95% CI 1.00-1.03), ICU admission (OR 1.81; 95% CI 1.04-3.16), and vasopressor support (OR 7.46; 95% CI 3.34-16.64) were risk factors for AKI, and that bicarbonate (OR 0.89; 95% CI 0.84-0.94) and partial pressure arterial oxygen/inspired oxygen fraction index (OR 0.99; 95% CI 0.98-0.99) could be protective factors.

A high frequency of AKI was documented in COVID-19 patients, with several predictors: age, male sex, DM, CKD, CRP, ICU admission, and vasopressor support. AKI occurred more frequently in patients with higher disease severity and was associated with higher mortality and worse outcomes.

Enzymes of the peptidylarginine deiminase family (PADs) play a relevant role in the pathogenesis of COVID-19. However, the association of single nucleotide polymorphisms (SNPs) in their genes with COVID-19 severity and death is unknown.

We included 1045 patients who were diagnosed with COVID-19 between October 2020 and December 2021. All subjects were genotyped for PADI2 (rs1005753 and rs2235926) and PADI4 (rs11203366, rs11203367, and rs874881) SNPs by TaqMan assays and their associations with disease severity, death, and inflammatory biomarkers were evaluated.

291 patients presented had severe COVID-19 according to PaO2/FiO2, and 393 had a non-survival outcome. Carriers of the rs1005753 G/G genotype in the PADI2 gene presented susceptibility for severe COVID-19, while the heterozygous carriers in rs11203366, rs11203367, and rs874881 of the PADI4 gene showed risk of death. The GTACC haplotype in PADI2-PADI4 was associated with susceptibility to severe COVID-19, while the GCACC haplotype was a protective factor. The GCGTG haplotype was associated with severe COVID-19 but as a protective haplotype for death. Finally, the GTACC haplotype was associated with platelet-to-lymphocyte ratio (PLR), the GCACC haplotype with neutrophil-to-hemoglobin and lymphocyte and the GCGTG haplotype as a protective factor for the elevation of procalcitonin, D-dimer, CRP, LCRP, NHL, SII, NLR, and PLR.

Our results suggest that the haplotypic combination of GTACC and some individual genotypes of PADI2 and PADI4 contribute to the subjects' susceptibility for severity and death by COVID-19.

Patients on hemodialysis are particularly vulnerable to COVID-19 and may have a reduced response to vaccination because of a decreased immune response. The nutritional status before or during the infection could also impact on the clinical effectiveness of vaccination.

We aim to describe the evolution of clinical and nutritional biomarkers of hemodialysis patients infected with SARS-CoV-2 and to assess their association with vaccination status.

An observational, analytic, longitudinal, retrospective multicenter study was carried out in 82 patients on hemodialysis with SARS-CoV-2 infection. Nutritional status was assessed using the Geriatric Nutritional Risk Index (GNRI), anthropometry, and biochemical parameters. The association of the vaccine doses with clinical- and nutritional-related variables was also evaluated.

The percentage of vaccinated patients was similar to that of nonvaccinated patients. Before infection, most of the patients were malnourished. They presented lower albumin, creatinine, and urea levels than the well-nourished patients. Significant deterioration of nutritional status after infection was evidenced considering GNRI score, dry weight, and body mass index. Albumin and creatinine also decreased significantly after infection, whereas C-reactive protein increased in the acute phase. Significant inverse correlation was found between the variation of post-pre GNRI scores and basal albumin and C-reactive protein at 7 days. In addition, we observed the opposite trend between albumin at 30 days and basal cholesterol. A negative value in the GNRI variation was associated with bilateral pneumonia, need for hospitalization, and nutritional support. Vaccinated patients presented substantially less bilateral pneumonia and hospitalization. No significant effects were observed between vaccine doses and the variation in nutritional status, although a positive correlation was detected with the albumin at 7 days and C-reactive protein before infection and the number of vaccine doses received.

COVID-19 is associated with affectations in the nutritional status and biomarkers in hemodialysis patients. In this study, vaccines have shown a protective effect against the clinical consequences of COVID. However, they have shown limitations in preventing the deterioration of nutritional status after infection. The results highlight the importance of promoting the vaccination in these patients as well as incorporating nutritional assessment before, during, and after the infection.

Although a variety of risk factors for pneumonia after spontaneous intracerebral hemorrhage have been established, an objective and easily obtainable predictor is still needed. Lactate dehydrogenase is a nonspecific inflammatory biomarker. In this study, we aimed to assess the association between lactate dehydrogenase and pneumonia in spontaneous intracerebral hemorrhage patients.

Our study was a retrospective, multicenter cohort study, undertaken in 7562 patients diagnosed with spontaneous intracerebral hemorrhage from 3 hospitals. All serum Lactate dehydrogenase was collected within 7 days from admission and divided into four groups as quartile(Q). We conducted a multivariable logistic regression analysis to assess the association of Lactate dehydrogenase with pneumonia.

Among a total of 7562 patients, 2971 (39.3%) patients were diagnosed with pneumonia. All grades of elevated lactate dehydrogenase were associated with increased raw and risk-adjusted risk of pneumonia. Multiple logistic regression analysis showed odds ratios for Q2-Q4 compared with Q1 were 1.21 (95% CI, 1.04-1.42), 1.64(95% CI, 1.41-1.92), and 1.92 (95% CI, 1.63-2.25) respectively. The odds ratio after adjustment was 4.42 (95% CI, 2.94-6.64) when lactate dehydrogenase was a continuous variable after log-transformed.

Elevated lactate dehydrogenase is significantly associated with an increase in the odds of pneumonia and has a predictive value for severe pneumonia in patients with pneumonia. Lactate dehydrogenase may be used to predict pneumonia events in spontaneous intracerebral hemorrhage patients as a laboratory marker.

Pathogenic mechanisms and long-term consequences of COVID-19 require attention in studies on SARS-CoV-2. The association of the severity of COVID-19 with genetic factors, such as human leukocyte antigen (HLA) genes, remains underexplored. Our study assessed the relationships between HLA class II alleles and COVID-19 severity and blood-based indicators of systemic inflammation and organ damage, serum markers of epithelial cell apoptosis such as caspase-cleaved CK18 fragment M30 (CK18-M30) and the extracellular matrix product hyaluronic acid (HA).

The study included 101 hospitalized COVID-19 patients (mean age 60 ±14 years). Clinical tests were performed at admission to the hospital. The levels of CK18-M30 and HA were detected in serum by enzyme-linked immunosorbent assay (ELISA). HLA typing was performed in HLA-DRB1, -DQA1, and -DQB1 loci by the polymerase chain reaction with low-resolution sequence-specific primers.

Sixty-one patients had a non-severe and 40 had a severe or critical disease course (following the WHO definition). The severity was associated with older age, male gender, higher HA, CK18-M30, and some indicators of inflammation. Despite the lack of direct association between HLA alleles and the severity of COVID-19, the presence of HLA-DRB1*04 and 12 alleles in the genotype was associated with lowered or elevated HA, respectively. The HLA-DQB1*03:01 allele was associated with lowered CK18-M30, aspartate aminotransferase, and ferritin. In addition, HLA-DQB1*06:01 was associated with elevated alanine aminotransferase.

Associations of HLA class II alleles with markers of epithelial cell apoptosis and extracellular matrix production indirectly support the influence of HLA genes on acute COVID-19 severity.

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.

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.

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.

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.

We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.

For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.

A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.

COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Different biomarkers have been suggested as novel biomarkers of coronavirus disease 2019 (COVID-19) theragnosis. With the aim of having a better clinical management of COVID-19, we decided to determine the relationship between hematological, inflammatory, and novel biomarkers with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) immunoglobulin (Ig)M and IgG antibodies.

Blood samples from 127 confirmed COVID-19 patients aged 11-84 years old were collected and tested for SARS-CoV-2 IgM and IgG antibodies alongside with hematological, inflammatory, and novel biomarkers. The Spearman correlation test was utilized to analyze the correlation between these biomarkers with SARS-CoV-2 IgM and IgG antibodies.

The SARS-CoV-2 IgM antibody significantly correlated with erythrocyte sedimentation rate (ESR) (r = 0.329, p = 0.000), C-reactive protein (CRP) (r = 0.459, p = 0.000), interleukin (IL)-6 (r = 0.345, p = 0.000), IL-8 (r = 0.263, p = 0.003), neutrophil to lymphocyte ratio (NLR) (r = 0.182, p = 0.040), derived NLR (dNLR) (r = 0.197, p = 0.026), neutrophil to monocyte ratio (NMR) (r = 0.184, p = 0.038), and CRP to lymphocyte ratio (CLR) (r = 0.495, p = 0.000). Also, we find significant correlation between SARS-CoV-2 IgG antibody with hemoglobin (Hb) (r = -0.257, p = 0.004), hematocrit (Hct) (r = -0.227, p = 0.010), mean corpuscular Hb concentration (MCHC) (r = -0.212, p = 0.017), lymphocyte count (r = -0.211, p = 0.017), platelet count (r = 0.179, p = 0.044), ESR (r = 0.461, p = 0.000), CRP (r = 0.344, p = 0.000), IL-6 (r = 0.178, p = 0.046), IL-8 (r = 0.237, p = 0.007), platelet to lymphocyte ratio (PLR) (r = 0.295, p = 0.001), and CLR (r = 0.376, p = 0.000).

Hematological biomarkers (Hb, Hct, MCHC, lymphocyte count, and platelet count), inflammatory biomarkers (ESR, CRP, IL-6, and IL-8), and novel biomarkers (dNLR, NLR, NMR, PLR, and CLR) are valuable indicators for clinical management of COVID-19.

It is well known that laboratory markers could help in identifying risk factors of severe illness and predicting outcomes of diseases. Here, we performed a retrospective modeling study of severity and mortality predictors of hematological and biochemical laboratory parameters in Iranian COVID-19 patients.

Data were obtained retrospectively from medical records of 564 confirmed Iranian COVID-19 cases. According to the disease severity, the patients were categorized into two groups (severe or nonsevere), and based on the outcome of the disease, patients were divided into two groups (recovered or deceased). Demographic and laboratory data were compared between groups, and statistical analyses were performed to define predictors of disease severity and mortality in the patients.

The study identified a panel of hematological and biochemical markers associated with the severe outcome of COVID-19 and constructed different predictive models for severity and mortality. The disease severity and mortality rate were significantly higher in elderly inpatients, whereas gender was not a determining factor of the clinical outcome. Age-adjusted white blood cells (WBC), platelet cells (PLT), neutrophil-to-lymphocyte ratio (NLR), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), erythrocyte sedimentation rate (ESR), mean corpuscular hemoglobin (MCHC), blood urea nitrogen (BUN), and creatinine (Cr) also showed high accuracy in predicting severe cases at the time of hospitalization, and logistic regression analysis suggested grouped hematological parameters (age, WBC, NLR, PLT, HGB, and international normalized ratio (INR)) and biochemical markers (age, BUN, and lactate dehydrogenase (LDH)) as the best models of combined laboratory predictors for severity and mortality.

The findings suggest that a panel of several routine laboratory parameters recorded on admission could be helpful for clinicians to predict and evaluate the risk of disease severity and mortality in COVID-19 patients.

Current evidence suggests that a group of patients who had survived coronavirus disease, 2019 (COVID-19) and developed post-COVID pain can exhibit altered nociceptive processing. The role of serological biomarkers and hospitalization treatments in post-COVID pain is unclear. This study aimed to investigate the association of serological biomarkers and treatments received during hospitalization with sensitization-associated symptoms in COVID-19 survivors with post-COVID pain. One hundred and eighty-three (n = 183) patients who had been hospitalized due to COVID-19 in one urban hospital of Madrid (Spain) during the first wave of the pandemic were assessed in a face-to-face interview 9.4 (SD 3.4) months after hospitalization. Levels of 19 serological biomarkers, hospitalization data, and treatments during hospitalization were obtained from hospital records. Sensitization-associated symptoms (Central Sensitization Inventory, CSI), sleep quality (Pittsburgh Sleep Quality Index, PSQI), pain catastrophism (Pain Catastrophizing Scale), and anxiety/depressive level (Hospital Anxiety and Depression Scale, HADS) were assessed. The prevalence of post-COVID pain was 40.9% (n = 75). Twenty-nine (38.6%) patients had sensitization-associated symptoms. Overall, no differences in hospitalization data and serological biomarkers were identified according to the presence of sensitization-associated symptoms. The analysis revealed that patients with sensitization-associated symptoms exhibited higher lymphocyte count and lower urea levels than those without sensitization-associated symptoms, but differences were small. Pain catastrophism and depressive levels, but not fatigue, dyspnea, brain fog, anxiety levels, or poor sleep, were higher in individuals with sensitization-associated symptoms. In conclusion, this study revealed that sensitization-associated post-COVID pain symptoms are not associated with serological biomarkers at hospital admission and hospitalization treatments received.

Predicting severe disease is important in provocative decision-making for the management of patients with the coronavirus disease 2019 (COVID-19); However, there are still some controversies about the COVID-19's severity predicting factors. This study aimed to investigate the relationships between clinical and laboratory findings regarding COVID-19's severity in patients admitted to a tertiary hospital in Mashhad, Iran.

A cross-sectional study was conducted on patients with documented COVID-19 infection based on the reverse transcription-polymerase chain reaction test. Clinical symptoms, vital signs, and medical history of the patients were recorded from their medical records. Laboratory findings and computed tomography (CT) study findings were documented. Disease severity was defined based on CT scan findings.

A total of 564 patients (58.8 ± 16.8 years old) were evaluated. The frequency of severe disease was 70.4%. There was a significant difference in heart rate (p = 0.0001), fever (p = 0.002), dyspnea (p = 0.0001), chest pain (p = 0.0001), diarrhea (p = 0.021), arthralgia (p = 0.0001), and chills (p = 0.044) as well as lymphopenia (p = 0.014), white blood cell count (p = 0.001), neutrophil count (p < 0.0001), lymphocyte count (p < 0.0001), and prothrombin time (p = 0.001) between disease severity groups. Predictors of severe COVID-19 were pulse rate (crude odds ratio [cOR] = 1.014, 95% confidence interval [CI] for cOR: 1.001, 1.027) and leukopenia (cOR = 3.910, 95% CI for cOR: 1.294, 11.809). Predictors for critical COVID-19 were pulse rate (cOR = 1.075, 95% CI for cOR: 1.046, 1.104), fever (cOR = 2.516, 95%CI for cOR: 1.020, 6.203), dyspnea (cOR = 4.190, 95% CI for cOR: 1.227, 14.306), and leukocytosis (cOR = 3.866, 95% CI for cOR: 1.815, 8.236).

Leukopenia and leukocytosis have the strongest correlation with the COVID-19 severity. These findings could be a valuable guild for clinicians in COVID-19 patient management in the inpatient setting.

Background COVID-19 is a major cause of illness and mortality. The management of COVID-19-related illnesses might change if variables connected to their severity and the requirement for ICU admission could be found. The severity of COVID-19 might be efficiently predicted with several laboratory measures, such as ferritin levels and D-dimer analysis. Objectives This study aimed to evaluate the association between serum D-dimer and ferritin levels and their effects on mortality in patients with COVID-19. Methods This retrospective observational study included all patients with positive real-time polymerase chain reaction (RT-PCR) results for COVID-19 who were hospitalized in the Ministry of Health South Al-Qunfudah General Hospital between March and September 30, 2020. Their laboratory parameters, serum D-dimer, and ferritin levels were evaluated. IBM SPSS Statistics for Windows, Version 26.0 (released 2019; IBM Corp., Armonk, New York, United States) was used to analyze the data. Results A total of 318 COVID-19 patients were analyzed; 56.9% (n=181) were male and 43.1% (n=137) were female. Of these, 78.6% (n=250) survived, including 58% of men and 42% of women. The mean D-dimer was 2.1 mcg/mL (SD=3.16) and the mean ferritin was 698.59 ng/mL (SD=603.11). Non-recovered patients were substantially older (66.16 years old) and had higher D-dimer (5.46) mcg/mL and ferritin levels (992.96) ng/mL. Intubation length and gender did not affect survival. Of the non-survivors, 95.6% (n=239) were admitted to the ICU, and 50% (n=34) required mechanical ventilation. Conclusions COVID-19 infection mortality dramatically increased with older age and increased mean ferritin and plasma D-dimer values, which were significantly higher in COVID-19 non-survivors than in survivors. Therefore, assessing and monitoring these laboratory markers in the early stages of the disease may have a significant impact on preventing disease progression and death.

To analyze variations in blood profile, the extent of hematological disorders, and the impact of comorbidities and COVID-19 vaccination on blood profile parameters and their clinical value for prognostic of SARS-CoV-2 infection.

This cross-sectional study took place in Douala, Cameroon. A complete blood count and molecular detection of SARS-CoV-2 were performed on patients. Clinical value was appraised using area under the curve (AUC) analysis.

In total, 420 participants were included. A significant reduction of hematological parameters such as lymphocytes (p < 0.0001), red blood cells (P = 0.0025), mean corpuscular hemoglobin count (P < 0.0001), and platelets (P = 0.02) was seen in SARS-CoV-2 (+) patients. Anisocytosis was the main hematological disorder (95.5%). Normocytic normochromic anemia was predominant in SARS-CoV-2 (+) (35.3%) while microcytic normochromic anemia was more frequently seen in SARS-CoV-2 (-) (16.5%). Neutrophil-to-lymphocyte (NLR) consistently showed statistically significant AUC ∼0.75 upon stratification for age, gender, and comorbidities, with the exception of COVID-19 vaccination uptake.

In a context where molecular detection methods are difficultly affordable in health facilities in developing countries such as Cameroon, NLR could be clinically interesting for identifying SARS-CoV-2 infected individuals, especially those with comorbidities.

C-reactive protein (CRP) is a cytokine-mediated acute phase reactant with a recognized role in inflammatory conditions and infectious disease. In coronavirus disease 2019 (COVID-19), elevated CRP concentrations in serum were frequently detected and significantly associated with poor outcome in terms of disease severity, need for intensive care, and in-hospital death. For these reasons, the marker was proposed as a powerful test for prognostic classification of COVID-19 patients. In most of available publications, there was however confounding information about how interpretative criteria for CRP in COVID-19 should be derived, including quality of employed assays and optimal cut-off definition. Assuring result harmonization and controlling measurement uncertainty in terms of performance specifications are fundamental to allow worldwide application of clinical information according to specific CRP thresholds and to avoid risk of patient misclassification.

Anti-viral and anti-inflammatory therapies were effective in altering virus repletion and immune dysregulation in Coronavirus Disease 2019 (COVID-19) patients. This study aimed to explore the effect of combination therapy on disease progression in a real-world setting.

A total of 836 patients confirmed with SARS-CoV-2 infection participated in the study from 15 November to 25 December 2022 at Beijing Youan Hospital, Capital Medical University. A prospective cohort study was implemented to investigate the prognostic effect of the combination therapy on virus shedding and clinical recovery.

About 78% of patients used nirmatrelvir/ritonavir (N/R, Paxlovid®, Pfizer) negatively, 16% of patients were prescribed nirmatrelvir/ritonavir beyond five days of symptom onset, 4% of patients received N/R monotherapy within five days of symptom onset and 2% of patients received N/R combined with dexamethasone. Compared with untreated patients, N/R monotherapy reduced the median time to 10.0 days from 12.0 days according to the negative conversion of nucleic acid amplification test (NAAT), and combination therapy reduced the time to 7.0 days, and increased to a 1.99 (95% CI 0.92, 4.32) and 14.23-fold (95% CI 4.50, 44.95) probability of negative NAAT, respectively. N/R monotherapy reduced the clinical recovery time to 10.0 days from 13.0 days. Single-use and combined-use non-significantly increased the recovery probability by 61% and 69%, respectively. In mild and moderate patients, the HRs for clinical recovery increased to 1.69 (95% CI 0.73, 3.94) and 2.18 (95% CI 0.29, 16.62), respectively.

Combination therapy of N/R and dexamethasone increased negative conversion of NAAT and was associated with a non-significant improvement in clinical recovery. Further studies are warranted to confirm this efficacy.

The clinical profile of hospitalized moderate-category COVID-19 patients has been understudied globally and in India.

The present study was conducted to study the clinical profile and assess the proportions of patients who progressed to severe disease and its predictors among moderate COVID-19 patients.

In this single-center observational study, 100 moderate-category COVID-19 patients as per Ministry of Health and Family Welfare (MoHFW) criteria of age ≥18 years of either sex, excluding pregnant females from February to November 2021, were studied by analyzing their clinical profiles and assessing Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score 2 (NEWS-2), and chest computed-tomography severity score (CTSS) to predict progression to severe disease. Severe disease was defined as per MoHFW criteria.

Out of 100 moderate-category COVID-19 patients, progression to severe disease was seen in 11 patients (11%), among which eight patients had expired, three patients were discharged, and the rest of the 89 patients (89%) who did not progress to severe disease were discharged. A higher age (62.2± 19.5 vs 54.8 ± 14.6 years), along with multivariate analysis revealing male sex (1.25 times), chronic kidney disease (2.86 times), leukocytosis (6.10 times), thrombocytopenia (1.04 times), anemia (9.3 times), a higher qSOFA score (3.6 times), and a higher NEWS-2 score on admission (1.56 times) had higher odds of progression to severe disease. A significant correlation (P < .05) of qSOFA score with serum LDH, ferritin, and hs-CRP levels; CT severity score with the serum ferritin, IL-6, and LDH levels; and NEWS-2 with serum LDH, hs-CRP, and ferritin levels were found. Moreover, the NEWS-2 score was found slightly better than qSOFA on receiver operating characteristic (ROC) curve analysis, with an area under the curve of 85.8% and 83.2%, respectively, predicting progression to severe disease.

Our study revealed male gender, chronic kidney disease, leukocytosis, anemia, thrombocytopenia, a higher qSOFA and NEWS-2 score on admission, and further, NEWS-2 score better than qSOFA on ROC curve analysis, with an area under the curve of 85.8% and 83.2%, respectively, in predicting severe disease among hospitalized moderate COVID-19 patients.

Long COVID-19 is a condition characterized by persistent symptoms lasting beyond the acute phase of COVID-19. Long COVID-19 produces diverse symptomatology and can impact organs and systems, including the hematological system. Several studies have reported, in COVID-19 patients, hematological abnormalities. Most of these alterations are associated with a higher risk of severe disease and poor outcomes. This literature review identified studies reporting hematological parameters in individuals with Long COVID-19. Findings suggest that Long COVID-19 is associated with a range of sustained hematological alterations, including alterations in red blood cells, anemia, lymphopenia, and elevated levels of inflammatory markers such as ferritin, D-dimer, and IL-6. These alterations may contribute to a better understanding of the pathophysiology of Long COVID-19 and its associated symptoms. However, further research is needed to elucidate the underlying mechanisms and potential treatments for these hematological changes in individuals with Long COVID-19.

The severity of COVID-19 is associated with an elevated level of a variety of inflammatory mediators. Increasing evidence suggests that the Th17 response contributes to the severity of COVID-19 pneumonia, whereas Th22 response plays a regulatory role in SARS-CoV-2 infection. Two main types of available COVID-19 treatments are antivirals and immunomodulatory drugs; however, their effect on a cytokine profile is yet to be determined.

This study aim to analyse a cytokine profile in peripheral blood from patients with COVID-19 (n=44) undergoing antiviral or/and immunomodulatory treatment and healthy controls (n=20). Circulating CD4+ and CD8+ T cells and their intracellular expression of IL-17A and IL-22 were assessed by flow cytometry.

Initial results showed an overexpression of IL-17F, IL-17A, CCL5/RANTES, GM-CSF, IL-4, IL-10, CXCL-10/IP-10 and IL-6 in COVID-19 patients compared to healthy controls. Treatment with remdesivir resulted in a significant decline in concentrations of IL-6, IL-10, IFN-alpha and CXCL10/IP-10. Immunomodulatory treatment contributed to a significant downregulation of IL-10, IFN-alpha, CXCL10/IP-10 and B7-H3 as well as upregulation of IL-22 and IL-1 beta. A combination of an antiviral and immunomodulatory treatment resulted in a significant decrease in IL-17F, IL-10, IFN-alpha, CXCL10/IP-10 and B7-H3 levels as well as an increase in IL-17A and IL-1 beta. We found significantly higher percentage of both CD4+ and CD8+ T cells producing IL-17A and CD4+ T cells producing IL-22 in patients with COVID-19.

Administration of antiviral or/and immunomodulatory treatment resulted in a significant downregulation of pro-inflammatory cytokine expression and an upregulation of T cell absolute counts in most cases, thus showing effectiveness of treatment in COVID-19. SARS-CoV-2 infection induced cytokine overexpression in hospitalized patients with COVID-19 as well as lymphopenia, particularly a decrease in CD4+ and CD8+ T cell counts. Moreover, despite the reduced counts of CD4+ and CD8+ T cells, both subsets showed overactivation and increased expression of IL-17A and IL-22, thus targeting Th17 response might alleviate inflammatory response in severe disease.

The future waves of COVID 19 infections will continue to raise serious problems in patients with severe forms of the disease. Bacterial infections associated with SARS-CoV-2 disease may complicate the progress of hospitalized patients with COVID-19. The present study aimed to evaluate the etiological spectrum of superinfection in adult patients with COVID-19 and to investigate the correlation between superinfection with multidrug-resistant (MDR) bacteria and serum procalcitonin (PCT). A total of 82 COVID-19 hospitalized patients with COVID-19 and bacterial superinfection were included. The superinfections were classified into early infections (3-7 days from admission) and late infections (>7 days from admission). Bacterial superinfection etiological spectrum, MDR bacteria profile and levels of serum PCT were studied. The most frequently isolated bacteria were Klebsiella pneumoniae, Acinetobacter baumannii and Enterococcus spp. MDR bacteria were involved in 73.17% of COVID-19 patients with bacterial superinfections. Most MDR bacteria superinfections (73.52%) occurred in the late infection period. Klebsiella pneumoniae, Enterococcus spp. and Methicillin-resistant Staphylococcus aureus were the most common MDR bacteria identified in late infections after hospitalization in 20.43, 4.30 and 4.30% of all infections, respectively. Serum PCT values were significantly higher in patients with MDR bacteria superinfection compared with patients with sensitive bacteria superinfection (P=0.009). The principal findings of the present study were the high prevalence of superinfection with MDR bacteria among the COVID-19 patients with bacterial superinfections and the presence of a statistically significant association between serum PCT levels and the presence of superinfection with MDR bacteria. The most effective way to fight against microbial resistance to antibiotics, whether it occurs independently or overlaps with viral infections, is to pursue a national policy for the rational use of antibiotics.

The monocyte distribution width (MDW), a novel inflammatory biomarker reflecting morphological changes in response to inflammation, has been shown to be useful in identifying COVID-19 infection or predicting death. However, data on the association with predicting the need for respiratory support are still limited. The aim of this study was to determine the association of MDW with the need for respiratory support in patients with SARS-CoV-2 infection.

This is a single-center retrospective cohort study. Consecutive hospitalized COVID-19 adult patients who presented at the outpatient department (OPD) or emergency department (ED) between May and August 2021 were enrolled. Respiratory support was defined as any one of the following: conventional oxygen therapy, high-flow oxygen nasal cannula, noninvasive, or invasive mechanical ventilation. The performance of MDW was measured using the area under the receiver operating characteristic (AuROC) curve.

Of the 250 enrolled patients, 122 (48.8%) patients received respiratory support. The mean MDW was significantly higher in the respiratory support group: 27.2 ± 4.6 vs 23.6 ± 4.1 (p < 0.001). The MDW ≥ 25 had the best AuROC characteristics of 0.70 (95% CI: 0.65-0.76).

The MDW is a potential biomarker that may aid in identifying individuals at risk of requiring oxygen support in COVID-19 and can be easily implemented in clinical practice.

Daorattanachai K, Hirunrut C, Pirompanich P, Weschawalit S, Srivilaithon W. Association of Monocyte Distribution Width with the Need for Respiratory Support in Hospitalized COVID-19 Patients. Indian J Crit Care Med 2023;27(5):352-357.

Background: COVID-19 is a respiratory disease that eventually became a pandemic, with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently been reported to help in early prediction and managing severe cases, which might improve outcomes. Our study aimed to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in COVID-19 patients and its effect on the outcome. Methods: We have collected retrospective data on socio-demographics, medical history, biomarkers, and disease outcomes from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID-19 in our cohort. The presence of abnormal inflammatory biomarkers (D-dimer, CRP, troponin, LDH, ferritin, and t white blood cells) was significantly associated with unstable COVID-19 disease. In addition, patients with evidence of severe respiratory disease, particularly those who required mechanical ventilation, had higher biomarkers when compared to those with stable respiratory conditions (p < 0.001). Conclusion: Identifying biomarkers predicts outcomes for COVID-19 patients and may significantly help in their management.