The value of clinical parameters in predicting the severity of COVID-19

Evaluation of Inflammatory Markers in Patients with COVID-19 Combined with Type 2 Diabetes Mellitus

Purpose

To explore the value of different inflammatory markers in predicting the severity of coronavirus disease 2019 (COVID-19) in patients with type 2 diabetes mellitus (T2DM).

Patients and Methods

A total of 116 patients with COVID-19 in patients with T2DM were collected from December 2022 to March 2023 and were divided into a mild case group (77 cases) and a severe case group (39 cases). The ratio of neutrophil to lymphocyte (NLR), platelet-to-lymphocyte ratio (PLR), neutrophil to lymphocyte × platelet ratio (NLPR), lymphocyte ratio to monocyte (LMR), systemic inflammatory response index (SIRI), systemic inflammatory index (SII), systemic inflammatory composite index (AISI), procalcitonin (PCT), C-reactive protein (CRP) and lactate dehydrogenase (LDH) were compared between the two groups. The screening effect of each variable on the progression of the disease was analyzed using receiver operating characteristic (ROC) curves.

Results

NLR, PLR, NLPR, MLR, SIRI, SII, AISI, LDH, CRP and PCT in severe case group were higher than those in mild case group (P<0.05), and LMR was lower than those in mild case group (P<0.05). ROC curve analysis further demonstrated the diagnostic performance of these biomarkers, with PCT having the largest area under the ROC curve (AUCROC) of 0.83.

Conclusion

NLR, PLR, NLPR, SIRI, SII, LDH, CRP and PCT demonstrate greater reliability in diagnostic value and clinical utility for predicting the severity of COVID-19 in patients with T2DM.

The Prognosis of Neutrophil-to-Lymphocyte Ratio and Lymphocyte-to-Monocyte Ratio in Elderly with Acute Ischemic Stroke

Background

Neutrophil-to-lymphocyte ratio (NLR) and Lymphocyte-to-monocyte ratio (LMR) have been reported to be associated with outcomes in acute ischemic stroke. However, research on elderly populations remains relatively scarce. We investigated the prognosis of NLR and LMR in elderly with acute ischemic stroke(AIS).

Methods

Based on the modified Rankin Score (mRS) on the 90th day after stroke, patients were divided into group and bad prognosis groups. Multivariate logistic regression analysis and receiver operating curves were used to identify prognostic factors and their predictive powers.

Results

In total, 824 elderly patients with AIS were enrolled between November 2021 and December 2023. Significant differences emerged in the NLR, LMR, and lymphocyte count between the two groups (P<0.05). Binary logistic regression identified NLR, LMR and neutrophil count as independent risk factors for an unfavorable prognosis in elderly patients with AIS. The areas under the curve (AUCs) of NLR, LMR, and the combination of NLR and LMR to discriminate poor function prognosis were 0.703, 0.672, and 0.706, respectively. ROC analysis also showed that combination of NLR and LMR was superior to NLR and LMR alone for predicting AIS.

Conclusion

NLR and LMR independently contribute to an unfavorable prognosis in elderly patients with AIS. The area under the ROC curve (AUC) for the combined NLR and LMR was higher than that for NLR and LMR individually, suggesting that combining these two indicators can improve the predictive ability for clinical outcomes in elderly patients with AIS.

Associations between epidemiological and laboratory parameters and disease severity in hospitalized patients with COVID-19 during first and second epidemic waves in middle south Mato Grosso

BACKGROUND

COVID-19 is a multisystemic disease characterized by respiratory distress. Disease severity is associated with several factors. Here we characterize virological findings and evaluate the association of laboratorial, epidemiological, virological findings and clinical outcomes of 251 patients during the first and second epidemic waves of COVID-19.

METHODS

This transversal study used biological samples and data from patients hospitalized with COVID-19 between May 2020 and August 2021 in the metropolitan region of Cuiabá, Mato Grosso Brazil. Biological samples were subjected to RT-qPCR and MinION sequencing. Univariate and multivariate logistic regression and Odds ratio were used to correlate clinical, laboratorial, epidemiological data.

FINDINGS

Patients were represented by males (61.7%) with mean age of 52.4 years, mild to moderate disease (49,0%), overweight/obese (69.3%), with comorbidities (66.1%) and evolving to death (55.38%). Severe cases showing symptoms for prolonged time, ≥ 25% of ground-glass opacities in the lungs and fatality rate increased significantly in second wave. Fatality was statistically associated to > 61 years of age,>25% ground-glass opacities in the lungs, immune, cardiac, or metabolic comorbidities. Higher viral load (p < 0.01/p = 0.02 in each wave), decreased erythrocyte (p < 0.01), hemoglobin (p < 0.05/p < 0.01), hematocrit (p < 0.01), RDW (p < 0.01), lymphocyte (p < 0.01), increased leucocyte (p < 0.01), neutrophil (p < 0.01) and CRP levels (p < 0.01) showed significant association with fatality in both waves, as did Neutrophil/Platelet (NPR; p < 0.01), Neutrophil/Lymphocyte (NLR; p < 0.01) and Monocyte/Lymphocyte ratio (MLR; p < 0.01). SARS-CoV-2 genomes from lineage B.1.1.33(n = 8) and Gamma/P.1(n = 15) shared 6/7 and 20/23 lineage-defining mutations, respectively.

MAIN CONCLUSIONS

Severity and mortality of COVID-19 associated with a panel of epidemiological and laboratorial findings, being second wave, caused by Gamma variant, more severe in this in-hospital population.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm

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.

Vital D: A modifiable occupational risk factor of UK healthcare workers

BACKGROUND

The role of Vitamin D in immune function is well reported with a growing evidence base linking low levels to poorer outcomes from infectious disease. Vitamin D deficiency and insufficiency are prevalent worldwide with healthcare workers identified as a known at-risk group. Here we aim to investigate serum Vitamin D levels in a UK population of front line healthcare workers and to promote the occupational risk.

METHODS

A cross-sectional study of 639 volunteers was conducted to identify the prevalence of Vitamin D deficiency and insufficiency amongst a population of front-line health care workers in the UK. Participant demographics and co-morbid factors were collected at the time of serum sampling for multivariate analysis.

RESULTS

Only 18.8% of the population had a normal vitamin D level greater than or equal to 75nmol/L. This is compared to Public Health England's (PHE) stipulated normal levels of 60% during winter. 81.2% had a level less than 75nmol/L, with 51.2% less than 50nmol/L and 6.6% less than 25nmol/L. For serum levels less than 25nmol/L, Asian ethnicity was more likely to have a vitamin D deficiency than non-asian (OR (95%CI): 3.81 (1.73-8.39), p = 0.001), whereas white ethnicity was less likely to have a vitamin D deficiency compared to non-white (OR (95%CI: 0.43 (0.20-0.83), p = 0.03). Other factors that contributed to a higher likelihood of lower-than-normal levels within this population included male sex, decreased age and not taking supplementation.

CONCLUSION

It is concluded that our population of healthcare workers have higher rates of abnormal vitamin D levels in comparison with the general UK population reported prevalence. Furthermore, Asian ethnicity and age 30 years and below are more at risk of vitamin D insufficiency and deficiency. This highlights an occupational risk factor for the healthcare community to consider.

Impact of vaccination status on clinical outcomes of hospitalized COVID-19 patients

INTRODUCTION

It is important to identify the relationship between the COVID-19 vaccination status and the prognosis of this disease in hospitalized patients to gain a more accurate picture of their status and the effect of vaccination, as well as take necessary measures to improve their medical care. Thus, the present study was conducted to investigate the relationship between the vaccination status of hospitalized COVID-19 patients and the disease severity index in terms of clinical, imaging, and laboratory criteria.

METHODS

This research is a descriptive-analytical cross-sectional study. the study population consisted of patients with a positive RT-PCR test for coronavirus, admitted to COVID-19 departments of teaching hospitals in Yazd, Iran, during two months in the sixth peak of COVID-19. The patients' data comprised demographic information (age, sex, and underlying disease), clinical information (length of hospital stay, length of ICU stay, and vaccination status), disease outcome (mortality and intubation), laboratory information (ESR, CRP, and NLR), and imaging information (lung involvement percentage), and finally, the relationship between patients' vaccination status and disease severity indices were analyzed with the chi-square test, independent t-test, and logistic regression analysis at a 95% confidence interval (CI).

FINDINGS

According to research findings, the duration of hospitalization was 5.25 ± 2.34 and 6.11 ± 3.88 days in groups of patients with complete and incomplete vaccination, respectively (P = 0.003). The lengths of ICU stay were 6 ± 4.63 and 5.23 ± 3.73 days in both groups of patients admitted to the ICU (P = 0.395). Furthermore, there were significant relationships between the ICU admission rates, endotracheal intubation, mortality rate, the lung involvement score in the chest CT scan, and the NLR with the vaccination status.Multivariate regression analysis indicated that DM, IHD, NLR, CT scan score and vaccination status were related to patients' in-hospital mortality.

CONCLUSION

Complete vaccination of COVID-19 led to a milder disease in terms of clinical, imaging, and laboratory criteria of patients and decreased the possibility of hospitalization in ICUs, intubation, and mortality in patients.

Association of Inflammatory Markers With Disease Progression and the Severity of COVID-19

Introduction In December 2019, there was a massive outbreak of viral pneumonia, which had a high case fatality rate. Genetic sequencing of the virus showed similarity with severe acute respiratory syndrome coronavirus (SARS-CoV). It was later named novel coronavirus 2019 while the disease it caused was given the nomenclature of COVID-19. This deadly pneumonia outbreak was declared a pandemic by the World Health Organization (WHO). Aim To derive the strength of the correlation between blood levels of various inflammatory markers with the severity of COVID-19 pneumonia in patients affected with novel coronavirus 2019. Materials and methodology A prospective study was conducted on 300 confirmed cases of COVID-19 infection from August 2020 to July 2021 in SSG Hospital, Vadodara. Diagnosis of patients as confirmed cases of COVID-19 infection was done according to the WHO interim guidance for COVID-19. Their inflammatory markers were done for this study. All COVID-19-positive patients who had given negative consent for enrollment were excluded from the study. Patients were classified based on the severity of acute respiratory distress syndrome (ARDS). Comprehensive medical record information, encompassing biodata, clinical symptoms, comorbidities, and laboratory investigations, was systematically collected. Patients were given the standard treatment protocol as per guidelines. Patients were subjected to detailed investigations comprising complete blood counts and inflammatory markers like C-reactive protein (CRP), lactate dehydrogenase (LDH), serum ferritin, and D-dimer. Patients were further investigated by chest X-ray (posteroanterior view) or high-resolution computed tomography of the thorax. Results A total of 300 confirmed cases of COVID-19 infection were included in this study. Most of them were males (52%) with a mean age of 51 years and 48% were females with a mean age of 55 years. The majority of patients (40%) did not have ARDS, 23.3% of patients had mild, 16.7% of patients had moderate, and 20% of patients had severe ARDS. Higher CRP levels, serum ferritin, and serum D-dimer were significantly associated with the severity of COVID-19 infection as compared to those having no symptoms (p < 0.05). Increased levels were associated with severe clinical manifestations of COVID-19. The sensitivity of CRP is 69% and specificity is 100% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. The sensitivity of ferritin is 88% and specificity is 81% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. The sensitivity of D-dimer is 94% and specificity is 89% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. The sensitivity of LDH is 93% and specificity is 84% as a diagnostic marker for COVID-19 pneumonia in terms of ARDS. Conclusions Current evidence from our study showed that higher levels of inflammatory markers such as CRP, LDH, D-dimer, and ferritin are associated with the severity of COVID-19 in terms of ARDS and thus could be used as significant prognostic factors of the disease. These indicators might support clinical decisions to identify high fatality cases and poor diagnosis in the initial admission phase.

Evaluation of oxidative stress level: reactive oxygen species, reduced glutathione, and D-dimer in patients hospitalized due to COVID-19

Elevated D-dimer levels at hospital admission may also indicate a higher likelihood of progressing to a severe or critical state. This study aimed to assess reactive oxygen species (ROS), non-enzymatic antioxidant reduced glutathione (GSH), and D-dimer levels in COVID-19 patients upon admission, examining their association with mortality outcomes. Data was collected from the medical records of 170 patients hospitalized in a referral hospital unit between March 2020 and December 2021. Patients were divided into two groups: the ward bed group (n = 87), comprising 51% with moderate clinical conditions, and the intensive care unit (ICU) group (n = 83), comprising 49% with severe conditions. The mean age was 59.4 years, with a male predominance of 52.4%. The overall death rate was 43%, with 30.6% in the moderate group and 69.4% in the severe group. The average time from symptom onset to hospitalization was 6.42 days. Results showed that non-survivors had high D-dimer and ROS counts, longer ICU stays, and worse saturation levels at admission. In conclusion, elevated ROS and D-dimer levels may contribute to worse outcomes in critically ill patients, potentially serving as specific and sensitive predictors of poor outcomes upon admission.

Analysis of clinical characteristics and risk factors between elderly patients with severe and nonsevere Omicron variant infection

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to millions of confirmed cases and deaths worldwide. Elderly patients are at high risk of developing and dying from COVID-19 due to advanced age, decreased immune function, intense inflammatory response, and comorbidities. Shanghai has experienced a wave of infection with Omicron, a new variant of SARS-CoV-2, since March 2022. There is a pressing need to identify clinical features and risk factors for disease progression among elderly patients with Omicron infection to provide solid evidence for clinical policy-makers, public health officials, researchers, and the general public.

AIM

To investigate clinical characteristic differences and risk factors between elderly patients with severe and nonsevere Omicron SARS-CoV-2 variant infection.

METHODS

A total of 328 elderly patients with COVID-19 admitted to the Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from April 2022 to June 2022 were enrolled and divided into a severe group (82 patients) and a nonsevere group (246 patients) according to the diagnosis and treatment protocol of COVID-19 (version 7). The clinical data and laboratory results of both groups were collected and compared. A chi-square test, t test, Mann-Whitney U test, hierarchical log-rank test, univariate and multivariate logistic regression, and hierarchical analyses were used to determine significant differences.

RESULTS

The severe group was older (84 vs 74 years, P < 0.001), included more males (57.3% vs 43.9%, P = 0.037), had a lower vaccination rate (P < 0.001), and had a higher proportion of comorbidities, including chronic respiratory disease (P = 0.001), cerebral infarction (P < 0.001), chronic kidney disease (P = 0.002), and neurodegenerative disease (P < 0.001), than the nonsevere group. In addition, severe disease patients had a higher inflammatory index (P < 0.001), greater need for symptomatic treatment (P < 0.001), longer hospital stay (P = 0.011), extended viral shedding time (P = 0.014), and higher mortality than nonsevere disease patients (P < 0.001). No difference was observed in the application of Paxlovid in the severe and nonsevere groups (P = 0.817). Oxygen saturation, cerebral infarction, and D-dimer were predictive factors for developing severe disease in patients with COVID-19, with D-dimer having an excellent role (area under the curve: 90.1%, 95%CI: 86.1-94.0%). In addition, D-dimer was a risk factor for developing severe COVID-19 according to multivariate stratified analysis.

CONCLUSION

The clinical course of severe COVID-19 is complex, with a higher need for symptomatic treatment. D-dimer is a suitable biomarker for identifying patients at risk for developing severe COVID-19.

Hematological and Biochemical Laboratory Parameters in COVID-19 Patients: A Retrospective Modeling Study of Severity and Mortality Predictors

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Cell free ACE2 RNA: A potential biomarker of COVID-19 severity

Despite the downward trend of COVID-19 pandemic and increased immunity of the general population, COVID-19 is still an elusive disease with risks due to emerging variants. Fast and reliable diagnosis of COVID-19 disease would allow better therapeutic interventions for patients at risk to develop more severe outcomes. Cell-free RNAs (cfRNAs) have been proven to be an effective biomarker in cancer and infectious diseases. It has been reported that cfRNAs are amplified in the bloodstream of these patients and at earlier stages of the disease, reflecting tissue damage. Hence, we hypothesize that cfRNAs may serve as a potential indicator of COVID-19 disease severity. To our knowledge, this is the first report to display a significant link between COVID-19 severity and cfRNA of angiotensin converting enzyme-2 (ACE2), the receptor for SARS-CoV-2 virus. qRT-PCR analysis of liquid biopsies from COVID-19 patients (n = 82) displayed a significant increase in ACE2-cfRNA levels in patients with severe manifestations. This finding correlated with blood biomarkers (ANC, WBC, and Creatinine) that were also significantly increased in these patients. We previously showed that bronchial cells from obese subjects express higher ACE2 levels, hence, we further analysed the involvement of obesity as a main contributor to severe outcomes. We confirm a significant increase of ACE2-cfRNA in the plasma of obese/overweight (Ob/Ov) COVID-19 patients compared to lean subjects, with no observed significant change in blood biomarkers. These findings suggest that monitoring ACE2-cfRNAs, as a biomarker, during COVID-19 infection may allow for better disease management, specifically for severe-COVID-19 patients.

Can mean platelet volume be a prognosis predictor in viral infections: An example of Covid-19

Purpose

This study revealed the utility of mean platelet volume (MPV) as a mortality marker in SARS-CoV-2 infection, as well as its connection with other inflammatory indicators such as procalcitonin (PCT) and neutrophil-lymphocyte ratio (NLR).

Methods

A total of 1528 patients (853 males and 653 females; mean age: 64.33 ± 16.36 years; range, 18-100 years) were hospitalized with COVID-19 between March 2020 and December 2022. The patients' demographic and clinical information, including ward and critical care data, were gathered from their medical records. On the first and last days, the PCT, NLR, and MPV values of the patients, who were divided into groups based on their hospitalization and outcomes, were analyzed.

Results

When the relevant laboratory data from the first and last days were compared, each group was statistically significant (p < 0.05). There was a moderate association between the final MPV values and the PCT and NLR values of the patients admitted to the ward (r = 0.448 and r = 0.397, respectively, where p < 0.01). There was also a substantial and moderate correlation between the final MPV levels and the PCT and NLR values of patients admitted to the intensive care unit (r = 0.613 and r = 0.361, respectively, p < 0.01). When compared to the patients' outcomes, the MPV had greater specificity and AUC values than the PCT and NLR (94.4 %, 0.968, 80.6 %, 0.923, 81 %, 0.845, respectively).

Conclusion

In patients hospitalized with COVID-19, the specificity of MPV values at the point of sickness severity and outcome was shown to be greater than PCT and NLR values, and MPV values may be a more accurate predictor of mortality than PCR and NLR.

Plasma SARS-CoV-2 N antigen is a powerful molecular marker for early detection of severe COVID-19 in patients and monitoring disease progression

BACKGROUND AND AIMS

Clear and effective indicators for early detection of severe coronavirus disease 2019 (COVID-19) are insufficient. We investigated the clinical value of the plasma SARS-CoV-2 N antigen (plasma N antigen) for severe COVID-19 early identification and disease progression monitoring.

MATERIALS AND METHODS

A cross-sectional study compared the diagnostic value of plasma N antigen levels detected within two days after hospital admission in 957 patients with COVID-19 during the BA2.2 outbreak in Shanghai (April 6-June 15, 2022). A follow-up study analyzed the plasma N antigen prognostic value in 274 non-severe patients, and a longitudinal study evaluated its continuous monitoring value in 16 patients with COVID-19 grade changes.

RESULTS

Plasma N antigen concentrations were significantly higher in severely ill than in non-severely ill patients. The plasma N antigen was superior to nasopharyngeal nucleic acid CT values and established COVID-19 blood biomarkers in identifying severe COVID-19. Patients with high plasma N-antigen concentrations at initial admission were more prone to developing severe COVID-19. The changes in plasma N antigen concentrations were consistent with disease progression. Two logistic regression models, including and excluding plasma N antigen, were established, with model 1 (including plasma N antigen) (AUC = 0.971, 0.958-0.980) yielding a better diagnostic value for severe COVID-19 than Model 2 (plasma N antigen excluded).

CONCLUSION

The plasma N antigen is superior to nasopharyngeal nucleic acids and established COVID-19 blood biomarkers for severe COVID-19 early recognition and progression monitoring, enabling the most accurate patient triaging and efficient utilization of medical resources.

Aftereffects in Epigenetic Age Related to Cognitive Decline and Inflammatory Markers in Healthcare Personnel with Post-COVID-19: A Cross-Sectional Study

Purpose

Epigenetic age and inflammatory markers have been proposed as indicators of severity and mortality in patients with COVID-19. Furthermore, they have been associated with the occurrence of neurological symptoms, psychiatric manifestations, and cognitive impairment. Therefore, we aimed to explore the possible associations between epigenetic age, neuropsychiatric manifestations and inflammatory markers (neutrophil-lymphocyte ratio [NLR], platelet-lymphocyte ratio [PLR], monocyte-lymphocyte ratio [MLR], and systemic immune-inflammation index [SII]) in healthcare personnel with post-COVID condition.

Patients and Methods

We applied the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) tests to 51 Mexican healthcare workers with post-COVID-19 condition; we also estimated their epigenetic age using the PhenoAge calculator.

Results

The participants had a post-COVID condition that lasted a median of 14 months (range: 1-20). High NLR (>1.73) had association with mild cognitive impairment by MMSE (p=0.013). Likewise, high MLR (>0.24) were associated with language domain in MOCA (p=0.046). Low PLR (<103.9) was also related to delayed recall in MOCA (p=0.040). Regarding comorbidities, hypertension was associated with SII (p=0.007), overweight with PLR (p=0.047) and alcoholism was associated with MLR (p=0.043). Interestingly, we observed associations of low PLR (<103.9) and low SII (<1.35) levels with increased duration of post-COVID condition (p=0.027, p=0.031). Likewise, increases in PhenoAge were associated with high levels of SII (OR=1.11, p=0.049), PLR (OR=1.12, p=0.035) and MLR (OR=1.12, p=0.030).

Conclusion

We observed neurocognitive changes related to inflammatory markers and increases in epigenetic age in healthcare personnel with post-COVID-19 condition. Future research is required to assess mental and physical health in individuals with post-COVID-19 symptoms.

Zinc and selenium status in coronavirus disease 2019

We systematically analyzed and attempted to discuss the possibility that deficiencies of zinc or selenium were associated with the incidence and severity of COVID-19. We searched for published and unpublished articles in PubMed, Embase, Web of Science and Cochrane up to 9 February 2023. And we selected healthy individuals, mild/severe, and even deceased COVID-19 patients to analyze their serum data. Data related to 2319 patients from 20 studies were analyzed. In the mild/severe group, zinc deficiency was associated with the degree of severe disease (SMD = 0.50, 95% CI 0.32-0.68, I2 = 50.5%) and we got an Egger's test of p = 0.784; but selenium deficiency was not associated with the degree of severe disease (SMD = - 0.03, 95% CI - 0.98-0.93, I2 = 96.7%). In the surviving/death group, zinc deficiency was not associated with mortality of COVID-19 (SMD = 1.66, 95%CI - 1.42-4.47), nor was selenium (SMD = - 0.16, 95%CI - 1.33-1.01). In the risk group, zinc deficiency was positively associated with the prevalence of COVID-19 (SMD = 1.21, 95% CI 0.96-1.46, I2 = 54.3%) and selenium deficiency was also positively associated with the prevalence of it (SMD = 1.16, 95% CI 0.71-1.61, I2 = 58.3%). Currently, serum zinc and selenium deficiencies increase the incidence of COVID-19 and zinc deficiency exacerbates the disease; however, neither zinc nor selenium was associated with mortality in patients with COVID-19. Nevertheless, our conclusions may change when new clinical studies are published.

Being Eaten Alive: How Energy-Deprived Cells Are Disposed of, Mediated by C-Reactive Protein-Including a Treatment Option

In medicine, C-reactive protein (CRP) has become established primarily as a biomarker, predicting patient prognosis in many indications. Recently, however, there has been mounting evidence that it causes inflammatory injury. As early as 1999, CRP was shown to induce cell death after acute myocardial infarction (AMI) in rats and this was found to be dependent on complement. The pathological effect of CRP was subsequently confirmed in further animal species such as rabbit, mouse and pig. A conceptual gap was recently closed when it was demonstrated that ischemia in AMI or ischemia/hypoxia in the severe course of COVID-19 causes a drastic lack of energy in involved cells, resulting in an apoptotic presentation because these cells cannot repair/flip-flop altered lipids. The deprivation of energy leads to extensive expression on the cell membranes of the CRP ligand lysophosphatidylcholine. Upon attachment of CRP to this ligand, the classical complement pathway is triggered leading to the swift elimination of viable cells with the appearance of an apoptotic cell by phagocytes. They are being eaten alive. This, consequently, results in substantial fibrotic remodeling within the involved tissue. Inhibiting this pathomechanism via CRP-targeting therapy has been shown to be beneficial in different indications.

Associations between laboratory variables and clinical features in patients hospitalized with COVID-19 after non-mRNA vaccination in China: A cross-sectional study

Objectives

Based on the data during the outbreak of COVID-19 in Wuxi city in China, we explored the relationship between laboratory variables and clinical features in patients hospitalized with COVID-19 after non-mRNA vaccination, and attempted to identify the significant impact of vaccination and COVID-19 infection on humans.

Methods

A retrospective observational cohort study was carried out. Patients who received non-mRNA COVID-19 vaccines and were hospitalized with COVID-19 between June 28, 2022, and July 24, 2022 were included. The correlation between different vaccine statuses, the time to negative PCR test, and biochemical parameters were investigated.

Results

All patients had a mild COVID-19 disease. The number of vaccine doses exerted no effects on the time to negative PCR test (P = 0.559). No differences were evident among inactivated, adenoviral-vectored, and recombinant subunit vaccines in the time to negative PCR test.Patients who just received one dose had significantly lower blood glucose levels than those who received three doses (P = 0.024), whereas two doses had no effect on blood glucose levels (one dose vs. two doses, P = 0.223; two doses vs. three doses, P = 0.457).Body temperature (β = 0.168, P = 0.011) and the percentage of lymphocytes (β = -0.219, P = 0.001) were substantially correlated with the time to COVID-19 negative PCR test. The prolonged stay was linked to a rise in GOT that fell within the usual range (P = 0.025).The percentage of lymphocytes (P = 0.007) and serum potassium (P = 0.004) were concordant with the marked change in body temperature.

Conclusions

The dose and type of vaccination had no effect on the time to COVID-19 negative PCR test in patients with mild COVID-19. Comparing the first dose with the booster dose, the blood glucose levels increased within the normal range. The period at which the COVID-19 nucleic acid turned negative correlated with body temperature, the proportion of lymphocytes, GOT, and serum potassium.

Clinical features of COVID-19 among patients with end-stage renal disease on hemodialysis in the context of high vaccination coverage during the omicron surge period: a retrospective cohort study

BACKGROUND

We determined the clinical presentation and outcomes of the Omicron variant of severe acute respiratory syndrome coronavirus 2 infection in hemodialysis patients and identified the risk factors for severe coronavirus disease (COVID-19) and mortality in the context of high vaccination coverage.

METHODS

This was a retrospective cohort study involving hemodialysis patients who were vaccinated against COVID-19 during March-September 2022, when the Omicron variant was predominant, and the COVID-19 vaccination rate was high. The proportion of people with severe COVID-19 or mortality was evaluated using univariate logistic regression.

RESULTS

Eighty-three (78.3%) patients had asymptomatic/mild symptoms, 10 (9.4%) had moderate symptoms, and 13 (12.3%) had severe symptoms. Six (5.7%) patients required intensive care admission, two (1.9%) required mechanical ventilation, and one (0.9%) was kept on high-flow nasal cannula. Of the five (4.7%) mortality cases, one was directly attributed to COVID-19 and four to pre-existing comorbidities. Risk factors for both severe COVID-19 and mortality were advanced age; number of comorbidities; cardiovascular diseases; increased levels of aspartate transaminase, lactate dehydrogenase, blood urea nitrogen/creatinine ratio, brain natriuretic peptide, and red cell distribution; and decreased levels of hematocrit and albumin. Moreover, the number of COVID-19 vaccinations wasa protective factor against both severe disease and mortality.

CONCLUSIONS

Clinical features of hemodialysis patients during the Omicron surge with high COVID-19 vaccination coverage were significant for low mortality. The risk features for severe COVID-19 or mortality were similar to those in the pre-Omicron period in the context of low vaccination coverage.

Hematological Parameters for Predicting Mortality in Acute Exacerbation of Chronic Obstructive Pulmonary Disease

(1) Introduction: COPD is a common and serious condition affecting a significant proportion of the population globally. Patients often suffer from exacerbations which lead to the worsening of their health status and respiratory function, and can often lead to death. Quick and cheap investigations are required that are capable of predicting mortality in patients with acute exacerbations that can be applied in low resource settings. (2) Materials and methods: This was a retrospective study carried out using hospital records of patients admitted for AECOPD from 1 January 2017 to 30 November 2022. Chi-square test (for sex) and Student's t-test were used to look for significant associations. Receiver Operating Characteristics (ROC) curves were plotted and Area Under Curve (AUC) values were calculated for various hematological parameters. Youden's J was used to identify the ideal cut-off with optimal sensitivity and specificity. Multivariate Cox regression was used to identify independent hematological predictors of mortality. Kaplan-Meir survival plots for neutrophil lymphocyte ratio (NLR) with the optimal cut-off were plotted. (3) Results: Amongst the 500 patients, 42 died while 458 survived, giving a mortality rate of 8.4%. NLR had the strongest association with mortality. The cut-off for various parameters were: NLR 14.83 (AUC 0.73), total leukocyte count (TLC) 13,640 cells/mm³ (AUC 0.60), absolute neutrophil count (ANC) 12,556 cells/mm³ (AUC 0.62), derived NLR (dNLR) 9.989 (AUC 0.73), hemoglobin 11.8 mg/dL (AUC 0.59), packed cell volume (PCV) 36.6% (AUC 0.60), and platelet lymphocyte ratio (PLR) 451.32 (AUC 0.55). (4) Conclusions: In patients with acute exacerbation of COPD, NLR was strongly associated with mortality, followed by dNLR. Cox regression identified NLR as an independent predictor of mortality.

Systems genetics identifies miRNA-mediated regulation of host response in COVID-19

BACKGROUND

Individuals infected with SARS-CoV-2 vary greatly in their disease severity, ranging from asymptomatic infection to severe disease. The regulation of gene expression is an important mechanism in the host immune response and can modulate the outcome of the disease. miRNAs play important roles in post-transcriptional regulation with consequences on downstream molecular and cellular host immune response processes. The nature and magnitude of miRNA perturbations associated with blood phenotypes and intensive care unit (ICU) admission in COVID-19 are poorly understood.

RESULTS

We combined multi-omics profiling-genotyping, miRNA and RNA expression, measured at the time of hospital admission soon after the onset of COVID-19 symptoms-with phenotypes from electronic health records to understand how miRNA expression contributes to variation in disease severity in a diverse cohort of 259 unvaccinated patients in Abu Dhabi, United Arab Emirates. We analyzed 62 clinical variables and expression levels of 632 miRNAs measured at admission and identified 97 miRNAs associated with 8 blood phenotypes significantly associated with later ICU admission. Integrative miRNA-mRNA cross-correlation analysis identified multiple miRNA-mRNA-blood endophenotype associations and revealed the effect of miR-143-3p on neutrophil count mediated by the expression of its target gene BCL2. We report 168 significant cis-miRNA expression quantitative trait loci, 57 of which implicate miRNAs associated with either ICU admission or a blood endophenotype.

CONCLUSIONS

This systems genetics study has given rise to a genomic picture of the architecture of whole blood miRNAs in unvaccinated COVID-19 patients and pinpoints post-transcriptional regulation as a potential mechanism that impacts blood traits underlying COVID-19 severity. The results also highlight the impact of host genetic regulatory control of miRNA expression in early stages of COVID-19 disease.

Assessing the Effect of the U.S. Vaccination Program on the Coronavirus Positivity Rate With a Multivariate Framework

The factors influencing the incidence of COVID-19, including the impact of the vaccination programs, have been studied in the literature. Most studies focus on one or two factors, without considering their interactions, which is not enough to assess a vaccination program in a statistically robust manner. We examine the impact of the U.S. vaccination program on the SARS-CoV-2 positivity rate while simultaneously considering a large number of factors involved in the spread of the virus and the feedbacks among them. We consider the effects of the following sets of factors: socioeconomic factors, public policy factors, environmental factors, and non-observable factors. A time series Error Correction Model (ECM) was used to estimate the impact of the vaccination program at the national level on the positivity rate. Additionally, state-level ECMs with panel data were combined with machine learning techniques to assess the impact of the program and identify relevant factors to build the best-fitting models. We find that the vaccination program reduced the virus positivity rate. However, the program was partially undermined by a feedback loop in which increased vaccination led to increased mobility. Although some external factors reduced the positivity rate, the emergence of new variants increased the positivity rate. The positivity rate was associated with several forces acting simultaneously in opposite directions such as the number of vaccine doses administered and mobility. The existence of complex interactions, between the factors studied, implies that there is a need to combine different public policies to strengthen the impact of the vaccination program.

Inflammatory Response in COVID-19 Depending on the Severity of the Disease and the Vaccination Status

The aim of this study was to analyze the serum concentration of interleukin-6 (IL-6), C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), ferritin, and procalcitonin in COVID-19 patients with different forms of the disease. We performed a prospective cohort study on 137 COVID-19 consecutive patients, divided into four groups according to the severity of the disease as follows: 30 patients in the mild form group, 49 in the moderate form group, 28 in the severe form group, and 30 in the critical form group. The tested parameters were correlated with COVID-19 severity. Significant differences were registered between the form of COVID-19 depending on the vaccination status, between LDH concentrations depending on the virus variant, and in IL-6, CRP, and ferritin concentrations and vaccination status depending on the gender. ROC analysis revealed that D-dimer best predicted COVID-19 severe forms and LDH predicted the virus variant. Our findings confirmed the interdependence relationships observed between inflammation markers in relation to the clinical severity of COVID-19, with all the tested biomarkers increasing in severe and critical COVID-19. IL-6, CRP, ferritin, LDH, and D-dimer were increased in all COVID-19 forms. These inflammatory markers were lower in Omicron-infected patients. The unvaccinated patients developed more severe forms compared to the vaccinated ones, and a higher proportion of them needed hospitalization. D-dimer could predict a severe form of COVID-19, while LDH could predict the virus variant.

COVID-19 Biomarkers at the Crossroad between Patient Stratification and Targeted Therapy: The Role of Validated and Proposed Parameters

Clinical knowledge about SARS-CoV-2 infection mechanisms and COVID-19 pathophysiology have enormously increased during the pandemic. Nevertheless, because of the great heterogeneity of disease manifestations, a precise patient stratification at admission is still difficult, thus rendering a rational allocation of limited medical resources as well as a tailored therapeutic approach challenging. To date, many hematologic biomarkers have been validated to support the early triage of SARS-CoV-2-positive patients and to monitor their disease progression. Among them, some indices have proven to be not only predictive parameters, but also direct or indirect pharmacological targets, thus allowing for a more tailored approach to single-patient symptoms, especially in those with severe progressive disease. While many blood test-derived parameters quickly entered routine clinical practice, other circulating biomarkers have been proposed by several researchers who have investigated their reliability in specific patient cohorts. Despite their usefulness in specific contexts as well as their potential interest as therapeutic targets, such experimental markers have not been implemented in routine clinical practice, mainly due to their higher costs and low availability in general hospital settings. This narrative review will present an overview of the most commonly adopted biomarkers in clinical practice and of the most promising ones emerging from specific population studies. Considering that each of the validated markers reflects a specific aspect of COVID-19 evolution, embedding new highly informative markers into routine clinical testing could help not only in early patient stratification, but also in guiding a timely and tailored method of therapeutic intervention.

Age, comorbidities, c-reactive protein and procalcitonin as predictors of severity in confirmed COVID-19 patients in the Philippines

Background

The Coronavirus Disease 2019 (COVID-19) pandemic has been affecting people globally, and the Philippines is one of the countries greatly struck by the virus. The continued rise of new positive cases has drawn attention to the urgent need for healthcare management to cope with this challenge. Severity prediction could help improve medical decision-making and optimise the patient's treatment plan with a good clinical outcome. This study aimed to identify the determinants of COVID-19 disease severity.

Methods

Demographic characteristics and laboratory findings were collected from electronic medical records and paper forms of all confirmed COVID-19 cases reported by the University of Perpetual Help DALTA Medical Center between the September 1, 2020 and the October 31, 2021. We performed statistical analyses and interpretation of data to compare severe and non-severe groups.

Results

5,396 confirmed cases were examined. Most of the severe cases were elderly, male, had blood type A, and with comorbidities. Cycle threshold (Ct) values were lower in the severe group. Most patients had higher-than-normal levels of all blood parameters except platelet, white blood cell (WBC), neutrophil, and lymphocyte counts. Age, sex, ABO blood groups, comorbidities, open reading frame 1 ab (ORF1ab) and nucleocapsid (N) gene Ct values, ferritin, C-reactive protein (CRP), procalcitonin (PCT), D-dimer, white blood cell (WBC) count, neutrophil count, and lymphocyte count were significantly associated with disease severity. In multivariate analysis, age groups >60 and 30-59 years, presence of comorbidities, CRP level >5 ng/mL, and PCT >0.05 ng/mL were identified as disease severity predictors.

Conclusions

Based on our results, age, comorbidities, CRP, and PCT level may be utilised as primary assessment factors for possible hospital admission and close monitoring upon testing. Early detection of these risk factors may provide strategic interventions that help reduce mortality, hospital admissions, and more expensive and extensive treatments.

C-Reactive Protein as The Predictor of Mortality for COVID-19 Patients in Indonesia

Coronavirus Disease 2019 (COVID-19) confirmed cases and deaths continue to rise. When a virus infects the body, the immune system tries to eliminate the virus. C-Reactive Protein (CRP) is a substance produced in the body in response to infection and inflammation. The study aimed to determine the role of CRP in predicting COVID-19 patients’ mortality. From the 1st of March to the 31st of August 2020, data on patients confirmed with COVID-19 were collected from medical records. The correlation between CRP levels and patient mortality was determined using a Chi-Square test. A Receiver Operator Curve (ROC) analysis was used to determine the best CRP cut-off point, and a survival analysis was used to assess the patient outcome. This study included a total of 210 eligible patients. Survivors and non-survivors were divided into two groups of patients (159 patients and 51 patients, respectively). The CRP cut-off was 54 mg/L, with an AUC of 0.817 (p<0.001). C-reactive protein levels were related to COVID-19 patient mortality (p=0.000). According to the survival analysis, patients with CRP levels > 54 mg/L had a lower chance of 30-day survival (p=0.0001). This study presented that CRP levels can be used to predict mortality in COVID-19 patients.

Clinical Profile and Risk Factors for Severe COVID-19 in Hospitalized Patients from Rio de Janeiro, Brazil: Comparison between the First and Second Pandemic Waves

Since COVID-19 was declared a pandemic, Brazil has become one of the countries most affected by this disease. A year into the pandemic, a second wave of COVID-19 emerged, with a rapid spread of a new SARS-CoV-2 lineage of concern. Several vaccines have been granted emergency-use authorization, leading to a decrease in mortality and severe cases in many countries. However, the emergence of SARS-CoV-2 variants raises the alert for potential new waves of transmission and an increase in pathogenicity. We compared the demographic and clinical data of critically ill patients infected with COVID-19 hospitalized in Rio de Janeiro during the first and second waves between July 2020 and October 2021. In total, 106 participants were included in this study; among them, 88% had at least one comorbidity, and 37% developed severe disease. Disease severity was associated with older age, pre-existing neurological comorbidities, higher viral load, and dyspnea. Laboratory biomarkers related to white blood cells, coagulation, cellular injury, inflammation, renal, and liver injuries were significantly associated with severe COVID-19. During the second wave of the pandemic, the necessity of invasive respiratory support was higher, and more individuals with COVID-19 developed acute hepatitis, suggesting that the progression of the second wave resulted in an increase in severe cases. These results can contribute to understanding the behavior of the COVID-19 pandemic in Brazil and may be helpful in predicting disease severity, which is a pivotal for guiding clinical care, improving patient outcomes, and defining public policies.

Pilot Study Results on Antibodies to the S- and N-Proteins of SARS-CoV-2 in Paired Sera from COVID-19 Patients with Varying Severity

In this retrospective cohort study, we investigated the formation of individual classes of antibodies to SARS-CoV-2 in archived serial sera from hospitalized patients with the medium–severe (n = 17) and severe COVID-19 (n = 11). The serum/plasma samples were studied for the presence of IgG, IgM and IgA antibodies to the recombinant S- and N-proteins of SARS-CoV-2. By the 7th day of hospitalization, an IgG increase was observed in patients both with a positive PCR test and without PCR confirmation of SARS-CoV-2 infection. Significant increases in the anti-spike IgG levels were noted only in moderate COVID-19. The four-fold increase of IgM to N-protein was obtained more often in the groups with mild and moderate infections. The IgA levels decreased during the infection to both the S- and N-proteins, and the most pronounced decrease was in the severe COVID-19 patients. The serum IgG to S-protein one week after hospitalization demonstrated a high-power relationship (rs = 0.75) with the level of RBD antibodies. There was a medium strength relationship between the levels of CRP and IgG (rs = 0.43). Thus, in patients with acute COVID-19, an increase in antibodies can develop as early as 1 week of hospital stay. The SARS-CoV-2 antibody conversions may confirm SARS-CoV-2 infection in PCR-negative patients.

Clinical Value of Platelets and Coagulation Parameters in Predicting the Severity of Delta Variant SARS-CoV-2

INTRODUCTION

The present study aimed to analyze the clinical features and laboratory markers of patients with Delta variant SARS-CoV-2 and explore the role of platelet in predicting the severity of Delta.

METHODS

This retrospective, observational study was conducted on 863 patients laboratory-confirmed Delta variant SARS-CoV-2. These cases were sub-classified based on disease severity into mild (n = 304), moderate (n = 537), and severe (n = 22). A series of laboratory findings and clinical data were collected and analyzed during hospitalization.

RESULTS

Of 863 hospitalized patients with Delta, the median age was 38 years (interquartile range, 30-51 years) and 471 (54.58%) were male. The most common clinical symptoms mainly included cough, fever, pharyngalgia, expectoration, dyspnea, fatigue, and headache, and the commonest comorbidities were hypertension and diabetes. Among the hematological variables, neutrophil count, red blood cell count, and hemoglobin, were found to be statistically significant with regard to subcategories based of disease severity (p < 0.05). Among coagulation parameters, there was a statistically significant difference in D-dimer, fibrinogen, international normalized ratio, and prothrombin time (p < 0.05). Statistically significant differences were observed in platelet markers including platelet count, large platelet count, and plateletcrit (p < 0.05). Additionally, there was strong correlation between platelet and other parameters with disease severity. Logistical regression analysis and ROC curves showed that D-dimer was a single best marker of disease severity (p = 0.005, p < 0.0001); however, platelet (p = 0.009, p = 0.002) and plateletcrit (p = 0.002, p = 0.001) could also predict severe disease. Platelet was identified as an independent risk factor for severe Delta.

CONCLUSION

Low platelet may be a marker of disease severity in Delta variant SARS-CoV-2 and may contribute to determine the severity of patients infected with Delta.

Predicting factors associated with prolonged intensive care unit stay of patients with COVID-19

BACKGROUND

Predicting the length of stay (LOS) for coronavirus disease 2019 (COVID-19) patients in the intensive care unit (ICU) is essential for efficient use of ICU resources. We analyzed the clinical characteristics of patients with severe COVID-19 based on their clinical care and determined the predictive factors associated with prolonged LOS.

METHODS

We included 96 COVID-19 patients who received oxygen therapy at a high-flow nasal cannula level or above after ICU admission during March 2021 to February 2022. The demographic characteristics at the time of ICU admission and results of severity analysis (Sequential Organ Failure Assessment [SOFA], Acute Physiology and Chronic Health Evaluation [APACHE] II), blood tests, and ICU treatments were analyzed using a logistic regression model. Additionally, blood tests (C-reactive protein, D-dimer, and the PaO2 to FiO2 ratio [P/F ratio]) were performed on days 3 and 5 of ICU admission to identify factors associated with prolonged LOS.

RESULTS

Univariable analyses showed statistically significant results for SOFA score at the time of ICU admission, C-reactive protein level, high-dose steroids, mechanical ventilation (MV) care, continuous renal replacement therapy, extracorporeal membrane oxygenation, and prone position. Multivariable analysis showed that MV care and P/F ratio on hospital day 5 were independent factors for prolonged ICU LOS. For D-dimer, no significant variation was observed at admission; however, after days 3 and 5 days of admission, significant between-group variation was detected.

CONCLUSIONS

MV care and P/F ratio on hospital day 5 are independent factors that can predict prolonged LOS for COVID-19 patients.

Autoimmune bullous diseases during COVID-19 pandemic: 2022 update on rituximab and vaccine

Autoimmune bullous diseases (AIBDs) are a heterogeneous group of life-threatening disorders associated with subepidermal or intraepidermal blistering. Skin barrier alterations and prolonged immunosuppressive treatments increase the risk of infections in patients with AIBDs, who are considered fragile. COVID-19 pandemic had a heavy impact on these patients. Although advances have been made in terms of prevention and treatment of COVID-19, this topic remains significant as the pandemic and its waves could last several years and, so far, a relevant proportion of the population worldwide is not vaccinated. This review is a 2022 update that summarizes and discusses the pandemic's burden on AIBD patients mainly considering relevant studies in terms of: (i) sample dimension; (ii) quality of control populations; (iii) possible standardization by age, gender and country. The findings show that: (i) the risk of COVID-19 infection and its severe course were comparable in AIBD patients and in the general population, except for rituximab-treated patients that presented a higher risk of infection and severe disease; (ii) the mortality rate in COVID-19-infected bullous pemphigoid patients was higher than in the general population, (iii) 121 cases of AIBD onset and 185 cases of relapse or exacerbation occurred after COVID-19 vaccination and a causal relationship has not been demonstrated so far. Altogether, acquired knowledge on COVID-19 pandemic could also be important in possible, albeit undesirable, future pandemic scenarios.

Predictors of pulmonary involvement in children with COVID-19: How strongly associated is viral load?

The purpose of this study was to identify risk factors for pulmonary involvement by examining the demographic, clinical, and laboratory characteristics of children with COVID-19. We performed a retrospective single-center study of COVID-19 in children treated at a tertiary care hospital in Turkey from December 2020 to June 2021. During the course of the study, 126 patients were evaluated, of whom 70/126 were male. The patients' ages ranged from 1 to 216 (mean, 4.73 ± 81.11) months. Fever (65.9%), cough (52.4%), and shortness of breath (18.3%) were the most common symptoms of COVID-19. Ten patients required noninvasive mechanical ventilation. Sixty-nine patients (54.8%) had pneumonia. Longer duration of fever, hospitalization, and the presence of cough were significantly associated with pulmonary involvement. Children with pneumonia had significantly higher levels of C-reactive protein (CRP), procalcitonin, erythrocyte sedimentation rate (ESR), and viral load, and significantly lower counts of lymphocytes and thrombocytes. The cutoff viral load, CRP, and procalcitonin values for predicting pulmonary involvement were 26.5 cycle threshold (Ct; 95% confidence interval [CI], 0.54-0.74; sensitivity, 0.65; specificity, 0.56; area under curve [AUC]: 0.647, p = 0.005), 7.85 mg/L (95% CI, 0.56-0.75; sensitivity, 0.66; specificity, 0.64; AUC = 0.656; p = 0.003) and 0.105 ng/ml (95% CI, 0.52-0.72; sensitivity, 0.55; specificity, 0.58; AUC = 0.626; p = 0.02), respectively. High CRP, procalcitonin levels, ESR, and viral load, and low lymphocyte and thrombocyte counts can predict pulmonary involvement in children with COVID-19, so better management may be provided for good prognosis.

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Carnosic acid inhibits reactive oxygen species-dependent neutrophil extracellular trap formation and ameliorates acute respiratory distress syndrome

AIMS

Infiltration of activated neutrophils into the lungs is a hallmark of acute respiratory distress syndrome (ARDS). Neutrophilic inflammation, particularly neutrophil extracellular traps (NETs), is proposed as a useful target for treating ARDS. Carnosic acid (CA) is a food additive; however, its anti-neutrophilic activity in the treatment of ARDS has not been well established. The hypothesis of present study is to confirm that CA alleviates ARDS by suppressing neutrophilic inflammation and oxidative damage.

MAIN METHODS

Generation of superoxide anions and reactive oxygen species (ROS), induction of elastase degranulation, and formation of NETs by human neutrophils were assayed using spectrophotometry, flow cytometry, and immunofluorescent microscopy. Immunoblotting was performed to determine the cellular mechanisms involved. Cell-free radical systems were used to test antioxidant activities. The therapeutic effect of CA was evaluated in a lipopolysaccharide (LPS)-induced ARDS mouse model.

KEY FINDINGS

CA greatly reduced superoxide anion production, ROS production, elastase release, cluster of differentiation 11b expression, and cell adhesion in activated human neutrophils. Mechanistic studies have demonstrated that CA suppresses phosphorylation of extracellular regulated kinase and c-Jun N-terminal kinase in activated neutrophils. CA effectively scavenges reactive oxygen and nitrogen species, but not superoxide anions. This is consistent with the finding that CA is effective against ROS-dependent NET formation. CA treatment significantly improved pulmonary neutrophil infiltration, oxidative damage, NET formation, and alveolar damage in LPS-induced mice.

SIGNIFICANCE

Our data suggested the potential application of CA for neutrophil-associated ARDS therapy.

A Simple Label-Free Aptamer-Based Electrochemical Biosensor for the Sensitive Detection of C-Reactive Proteins

The level of C-reactive protein (CRP) in the human body is closely associated with cardiovascular diseases and inflammation. In this study, a label-free functionalized aptamer sensor was attached to an electrode trimmed with in-gold nanoparticles and carboxylated graphene oxide (AuNPs/GO-COOH) to achieve sensitive measurements relative to CRP. Gold nanoparticles were selected for this study due to super stability, remarkably high electrical conductivity, and biocompatibility. In addition, carboxylated graphene oxide was utilized to promote the anchorage of inducer molecules and to increase detection accuracies. The sensing signal was recorded using differential pulse voltammetry (DPV), and it produced a conspicuous peak current obtained at approximately -0.4 V. Furthermore, the adapted sensor manifested a broad linear span from 0.001 ng/mL to 100 ng/mL. The results also demonstrated that this aptamer sensor had superior stability, specificity, and reproducibility. This aptamer-based electrochemical sensor has enormous potential in complex application situations with interfering substances.

Serum interleukin 1β and sP-selectin as biomarkers of inflammation and thrombosis, could they be predictors of disease severity in COVID 19 Egyptian patients? (a cross-sectional study)

BACKGROUND

Thromboembolism was a chief cause of mortality in 70% of patients with COVID-19. Our objective was to see if serum interleukins 1 beta (IL-1β) and soluble platelets selectin (sP-selectin) could serve as novel markers of thromboembolism in COVID-19 patients.

METHODS

This cross sectional study involved 89 COVID-19 patients who were recruited from 1st of February to 1st of May 2021. Clinical and laboratory data were collected, and chest imaging was performed. The levels of IL-1β and sP-selectin were assessed in all cases through ELISA kits. Comparisons between groups were done using an unpaired t-test in normally distributed quantitative variables. In contrast, a non-parametric Mann-Whitney test was used for non-normally distributed quantitative variables.

RESULTS

Severe COVID-19 infection was associated with higher serum levels of CRP, Ferritin, LDH, D dimer, IL-1β and sP-selectin (P <  0.001) with significant correlation between levels of IL-1β and sP-selectin (r 0.37, P <  0.001), D-dimer (r 0.29, P 0.006) and Ferritin (r 0.5, p <  0.001). Likewise, a positive correlation was also found between levels of sP-selectin, D-dimer and Ferritin (r 0.52, P <  0.001) (r 0.59, P <  0.001). Imaging studies revealed that 9 (10.1%) patients developed venous and 14 (15.7%) developed arterial thrombosis despite receiving anticoagulant therapy. Patients with thrombotic events had significantly higher levels of IL-1β, sP-selectin and LDH serum levels. Meanwhile, there was no statistical significance between CRP, D-dimer or Ferritin levels and the development of thrombotic events.

CONCLUSION

IL-1β and sP-selectin levels can be promising predictors for severe COVID-19 infection and predictable thrombosis.

Predicting roles of IL-27 and IL-32 in determining the severity and outcome of COVID-19

OBJECTIVE

Immune changes play fundamental roles in the pathogenesis and severity of coronavirus disease 2019 (COVID-19). Previous studies have revealed alterations in immune responses of patients with non-severe and severe COVID-19. Therefore, this study investigated whether interleukin-27 (IL-27) and interleukin-32 (IL-32) levels may be considered as predicting factors for determining the severity and outcome of COVID-19.

METHODS

The blood samples were collected from 50 non-severe and severe patients infected with COVID-19 and 25 healthy subjects. The serum samples were isolated from the whole blood. The levels of IL-27 and IL-32 were measured by enzyme-linked immunosorbent assay and percentages of some immune cells were studied by cell counter.

RESULTS

The levels of IL-27 and IL-32 were significantly higher in COVID-19 patients than healthy subjects (p < 0.0001-0.01). IL-27 was significantly reduced in severe COVID-19 patients who needed to undergo ICU therapy (p < 0.05). Disease severity was significantly associated with IL-27 level in patients with COVID-19 (p < 0.05), unlike IL-32 level. There was a significant association between IL-27 and IL-32 in participants (p < 0.0001, odds ratio (OR) = 0.9873; 95% confidence interval (CI) = 0.9998 to 1.000; p < 0.05, OR = 0.4462; 95% CI = 0.08,579 to 0.7802; p < 0.01, OR = 0.6640, 95% CI = 0.3007-0.8590). IL-27 level was significantly higher in the recovered subjects than dead cases (p < 0.0001). IL-27 and IL-32 levels in patients who had fever were significantly higher than those who did not have (p < 0.01-0.05), unlike patients who suffered from cough (p < 0.001-0.01). The IL-27 level in patients with non-severe COVID-19 was directly correlated with CRP value (p < 0.05, OR = 0.5,722,357, 95% CI = 0.06,807,176-0.8,435,928). IL-27 and IL-32 levels in non-severe patients were positively associated with NLR (p < 0.01, OR = 0.7292; 95% CI = 0.2809 to 0.9163; p < 0.01, OR = 0.6537, 95% CI = 0.1425-0.8896). Patients with severe COVID-19 had a significant increase in NLR (p < 0.0001-0.05). NLR was significantly correlated with the disease severity (p < 0.0001-0.05). Survivors had a significant reduction in NLR compared with those who succumbed to COVID-19 (p < 0.05).

CONCLUSION

Change in IL-27 level along with the frequencies of some immune cells may serve as a predictor of the severity and outcome of COVID-19.

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

Neutrophil to lymphocyte ratio and in-hospital mortality among patients with SARS-CoV-2: A retrospective study

The goal of this study was to investigate in-hospital mortality in patients suffering from acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relative to the neutrophil to lymphocyte ratio (NLR) and to determine if there are gender disparities in outcome. Between February 26 and September 8, 2020, patients having SARS-CoV-2 infection were enrolled in this retrospective cohort research, which was categorized by NLR levels ≥9 and < 9. In total, 6893 patients were involved included of whom6591 had NLR <9, and 302 had NLR ≥9. The age of most of the patients in the NLR<9 group was 50 years, on the other hand, the age of most of the NLR ≥9 group patients was between 50 and 70 years. The majority of patients in both groups were male 2211 (66.1%). The ICU admission time and mortality rate for the patients with NLR ≥9 was significantly higher compared to patients with NLR <9. Logistic regression's outcome indicated that NLR ≥9 (odds ratio (OR), 24.9; 95% confidence interval (CI): 15.5–40.0; p < 0.001), male sex (OR, 3.5; 95% CI: 2.0–5.9; p < 0.001) and haemoglobin (HB) (OR, 0.95; 95% CI; 0.94–0.96; p < 0.001) predicted in-hospital mortality significantly. Additionally, Cox proportional hazards analysis (B = 4.04, SE = 0.18, HR = 56.89, p < 0.001) and Kaplan–Meier survival probability plots also indicated that NLR>9 had a significant effect on mortality. NLR ≥9 is an independent predictor of mortality(in-hospital) among SARS-CoV-2 patients.

•

The main finding of this study is that NLR is an autonomous predictor of in-hospital mortality in patients with SARS-CoV-2.

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Fatality in SARS-CoV-2 patients with NLR >9 was 25 times higher than that in patients with NLR <9.

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Patients with NLR >9, the average length of ICU stay was higher.

•

Mortality rate in males was high compared to females with NLR>9.

Lifestyle and environmental factors may induce airway and systemic inflammation in firefighters

Health status depends on multiple genetic and non-genetic factors. Nonheritable factors (such as lifestyle and environmental factors) have stronger impact on immune responses than genetic factors. Firefighters work is associated with exposure to air pollution and heat stress, as well as: extreme physical effort, mental stress, or a changed circadian rhythm, among others. All these factors can contribute to both, short-term and long-term impairment of the physical and mental health of firefighters. Increased levels of some inflammatory markers, such as pro-inflammatory cytokines or C-reactive protein (CRP) have been observed in firefighters, which can lead to local, acute inflammation that promotes a systemic inflammatory response. It is worth emphasizing that inflammation is one of the main hallmarks of cancer and also plays a key role in the development of cardiovascular and respiratory diseases. This article presents possible causes of the development of an inflammatory reaction in firefighters, with particular emphasis on airway inflammation caused by smoke exposure.

The Effects of Laboratory Parameters on the Prognosis of COVID-19

OBJECTIVE

While the coronavirus disease 2019 pandemic is an ongoing issue across the world, understanding the course of the disease is important for early diagnosis and treatment. We aimed, with this study, to determine the differences between laboratory parameters in different clinical pictures of coronavirus disease 2019.

MATERIALS AND METHODS

The study included 443 patients who presented to Atatürk University Medical Faculty Hospital between March 15, 2020, and June 15, 2020, and were diagnosed with coronavirus disease 2019 upon a positive Real Time Polymerase Chain Reaction (RT-PCR) result. The hospitalized patients were divided into 4 groups based on their clinical status. The roles of these markers in determining the severity of coronavirus disease 2019 were statistically evaluated.

RESULTS

A total of 443 patients with RT-PCR confirmation were included in the study. The mean age was 46.0 ± 19.1 years and 54.4% of the patients were male. According to the clinical classification, 16.3% of the cases were asymptomatic, 25.7% uncomplicated, 35.7% mild/moderate, and 22.3% severe. The first 3 most frequent symptoms were cough (21.3%), fever (17.7%), and fatigue (15.5%). Hypertension (36.1%) was the major comorbidity among the patients. During the follow-up of severe cases, 39.4% developed the need for intensive care. The overall mortality rate, on the other hand, was 4.7%. Regarding laboratory parameters, procalcitonin (PCT), serum ferritin, erythrocyte sedimentation rate, C-reactive protein, neutrophil count, D-dimer, troponin, and lactate dehydrogenase were at the highest level in the severe patient group while albumin, platelet, and lymphocyte count were found to be at the lowest level in the same group. A statistically significant difference was detected between the groups (P < .001).

CONCLUSION

The increase in C-reactive protein, PCT, erythrocyte sedimentation rate, ferritin, troponin, D-dimer, lactate dehydrogenase, and neutrophil count and the decrease in albumin, platelet, and lymphocyte count are significant in the severe patient group; it has been concluded that they can be used to determine the severity of coronavirus disease 2019.

A Retrospective Analysis of the Importance of Biochemical and Hematological Parameters for Mortality Prediction in COVID-19 Cases

Introduction The recent coronavirus disease 2019 (COVID-19) pandemic has devastated the world's health and economy and has devastatingly affected social and emotional spheres. Although it was the older population who faced the worst, a good number of the younger population also lost their lives. It was very important to predict who will progress to the worst clinical outcome. Consequently, quick and accurate ways of forecasting mortality in COVID-19 cases are essential to save lives. In this study, 11 predictive parameters, namely, D-dimer, creatinine, C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), systemic inflammatory index (SII), platelet count, absolute neutrophil count (ANC), and absolute lymphocyte count (ALC), were studied for determining their significance as predictive parameters of mortality in COVID-19-affected patients. Methods We conducted a retrospective study of essential parameters, namely, D-dimer, creatinine, CRP, LDH, ferritin, NLR, PLR, SII, platelet count, ANC, and ALC, in confirmed COVID-19 cases for one year between 2020 and 2021. The medical information was obtained from the digital storage sources of the hospital. All cases were segregated into surviving and non-surviving cases. All parameters were collected, results were tabulated, and each individual parameter was then analyzed to see if it showed any significant deviations in non-surviving cases and could help predict mortalities. Statistical analysis was conducted using the latest version of the Statistical Package for the Social Sciences (SPSS) software (IBM SPSS Statistics, Armonk, NY, USA). Results Each of the parameters was individually studied. D-dimer, creatinine, LDH, ferritin, CRP, NLR, PLR, SII, and ANC showed a statistically significant increase in non-surviving cases. Compared to surviving cases, ALC and platelets showed a statistically significant decrease in non-surviving instances. Conclusion All the studied parameters showed significant deviations in non-surviving cases and could help predict mortalities. This study also stresses the utility of readily available hematological ratios such as NLR and SII for prognosis in COVID-19 subjects.

Coagulation parameters predict COVID-19-related thrombosis in a neural network with a positive predictive value of 98

Thrombosis is a major clinical complication of COVID-19 infection. COVID-19 patients show changes in coagulation factors that indicate an important role for the coagulation system in the pathogenesis of COVID-19. However, the multifactorial nature of thrombosis complicates the prediction of thrombotic events based on a single hemostatic variable. We developed and validated a neural net for the prediction of COVID-19-related thrombosis. The neural net was developed based on the hemostatic and general (laboratory) variables of 149 confirmed COVID-19 patients from two cohorts: at the time of hospital admission (cohort 1 including 133 patients) and at ICU admission (cohort 2 including 16 patients). Twenty-six patients suffered from thrombosis during their hospital stay: 19 patients in cohort 1 and 7 patients in cohort 2. The neural net predicts COVID-19 related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α2-Macroglobulin (9%), TG curve width (9%), thrombin-α2-Macroglobulin complexes (9%), plasmin generation lag time (8%), serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). This neural net can predict COVID-19-thrombosis at the time of hospital admission with a positive predictive value of 98%-100%.

Hemostatic system and COVID-19 crosstalk: A review of the available evidence

Since the discovery of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its resultant coronavirus disease 2019 (COVID-19) pandemic, respiratory manifestations have been the mainstay of clinical diagnosis, laboratory evaluations, and radiological investigations. As time passed, other pathological aspects of SARS-CoV-2 have been revealed. Various hemostatic abnormalities have been reported since the rise of the pandemic, which was sometimes superficial, transient, or fatal. Mild thrombocytopenia, thrombocytosis, venous, arterial thromboembolism, and disseminated intravascular coagulation are among the many hemostatic events associated with COVID-19. Venous thromboembolism necessitating therapeutic doses of anticoagulants is more frequently seen in severe cases of COVID-19, especially in patients admitted to intensive care units. Hemorrhagic complications rarely arise in COVID-19 patients either due to a hemostatic imbalance resulting from severe disease or as a complication of over anticoagulation. Although the pathogenesis of coagulation disturbance in SARS-CoV-2 infection is not yet understood, professional societies recommend prophylactic antithrombotic therapy in severe cases, especially in the presence of abnormal coagulation indices. The review article discusses the various available evidence on coagulation disorders, management strategies, outcomes, and prognosis associated with COVID-19 coagulopathy, which raises awareness about the importance of anticoagulation therapy for COVID-19 patients to guard against possible thromboembolic events.

Leukocyte glucose index as a novel biomarker for COVID-19 severity

The severity of coronavirus disease 2019 (COVID-19) quickly progresses with unfavorable outcomes due to the host immune response and metabolism alteration. Hence, we hypothesized that leukocyte glucose index (LGI) is a biomarker for severe COVID-19. This study involved 109 patients and the usefulness of LGI was evaluated and compared with other risk factors to predict COVID 19 severity. LGI was identified as an independent risk factor (odds ratio [OR] = 1.727, 95% confidence interval [CI]: 1.026-3.048, P = 0.041), with an area under the curve (AUC) of 0.749 (95% CI: 0.642-0.857, P < 0.0001). Interestingly, LGI was a potential risk factor (OR = 2.694, 95% CI: 1.575-5.283, Pcorrected < 0.05) for severe COVID-19 in female but not in male patients. In addition, LGI proved to be a strong predictor of the severity in patients with diabetes (AUC = 0.915 (95% CI: 0.830-1), sensitivity = 0.833, and specificity = 0.931). The AUC of LGI, together with the respiratory rate (LGI + RR), showed a considerable improvement (AUC = 0.894, 95% CI: 0.835-0.954) compared to the other biochemical and respiratory parameters analyzed. Together, these findings indicate that LGI could potentially be used as a biomarker of severity in COVID-19 patients.

The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis

Several studies have proposed that the neutrophil−lymphocyte ratio (NLR) is one of the various biomarkers that can be useful in assessing COVID-19 disease-related outcomes. Our systematic review analyzes the relationship between on-admission NLR values and COVID-19 severity and mortality. Six different severity criteria were used. A search of the literature in various databases was conducted from 1 January 2020 to 1 May 2021. We calculated the pooled standardized mean difference (SMD) for the collected NLR values. A meta-regression analysis was performed, looking at the length of hospitalization and other probable confounders, such as age, gender, and comorbidities. A total of sixty-four studies were considered, which included a total of 15,683 patients. The meta-analysis showed an SMD of 3.12 (95% CI: 2.64−3.59) in NLR values between severe and non-severe patients. A difference of 3.93 (95% CI: 2.35−5.50) was found between survivors and non-survivors of the disease. Upon summary receiver operating characteristics analysis, NLR showed 80.2% (95% CI: 74.0−85.2%) sensitivity and 75.8% (95% CI: 71.3−79.9%) specificity for the prediction of severity and 78.8% (95% CI: 73.5−83.2%) sensitivity and 73.0% (95% CI: 68.4−77.1%) specificity for mortality, and was not influenced by age, gender, or co-morbid conditions. Conclusion: On admission, NLR predicts both severity and mortality in COVID-19 patients, and an NLR > 6.5 is associated with significantly greater the odds of mortality.

Clinical characteristics and mortality predictors of patients with cancer hospitalized by COVID-19 in a pediatric third-level referral center

Background

More than 135 million COVID-19 cases (coronavirus disease 2019) have been reported worldwide until today, with over 2.9 million deaths. Several studies have demonstrated that disease severity is lower in the pediatric population than in adults; however, differences are described in patients with chronic diseases, including oncological patients. Current world literature suggests patients with comorbidities, including cancer, have an increased risk of unfortunate outcomes. Therefore, our objective was to describe the clinical characteristics and epidemiological factors associated with mortality in a cohort of pediatric cancer patients hospitalized for COVID-19.

Methods

This is a retrospective, descriptive study of the cases of patients with cancer hospitalized for COVID-19. A total of 40 pediatrics were included in the analysis. Data from pediatric patients with COVID-19 included clinical and epidemiological records, laboratory, imaging studies, COVID-19 diagnostic methods, and medical treatment.

Results

Of the 40 pediatric patients admitted with cancer with a confirmed diagnosis of COVID-19, 42.5% were solid tumors, 40% leukemias, and 17.5% lymphomas. The clinical parameters associated with mortality were stage IV tumor (p = 0.029) and intubation (p < 0.001). The biochemical factors associated with lower survival were thrombocytopenia under 25,000 cells/mm3 (p < 0.001), D-dimer over 1 μg/ml (p = 0.003), clinical malnutrition (p = 0.023), and disseminated intravascular coagulation (p = 0.03).

Conclusion

Our findings showed that the fever was the most frequent symptom, and the clinical parameters associated with mortality were stage IV tumor, intubation, saturation percentage, RDW, platelets, creatinine, ALT, D-dimer, ferritin, and FiO2 percentage. The thrombocytopenia, D-dimer, nutritional status, and disseminated intravascular coagulation were significantly associated with lower survival.

Comparative analysis of neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio with respect to outcomes of in-hospital coronavirus disease 2019 patients: A retrospective study

Introduction and objectives

In patients with coronavirus disease 2019 (COVID-19), several abnormal hematological biomarkers have been reported. The current study aimed to find out the association of neutrophil to lymphocyte ratio (NLR) and derived NLR (dNLR) with COVID-19. The objective was to compare the accuracy of both of these markers in predicting the severity of the disease.

Materials and methods

The study was conducted in a single-center having patients with COVID-19 with a considerable hospital stay. NLR is easily calculated by dividing the absolute neutrophil count (ANC) with the absolute lymphocyte count (ALC) {ANC/ALC}, while dNLR is calculated by ANC divided by total leukocyte count minus ANC {ANC/(WBC-ANC)}. Medians and interquartile ranges (IQR) were represented by box plots. Multivariable logistic regression was performed obtaining an odds ratio (OR), 95% CI, and further adjusted to discover the independent predictors and risk factors associated with elevated NLR and dNLR.

Results

A total of 1,000 patients with COVID-19 were included. The baseline NLR and dNLR were 5.00 (2.91–10.46) and 4.00 (2.33–6.14), respectively. A cut-off value of 4.23 for NLR and 2.63 for dNLR were set by receiver operating characteristic (ROC) analysis. Significant associations of NLR were obtained by binary logistic regression for dependent outcome variables as ICU stay (p < 0.001), death (p < 0.001), and invasive ventilation (p < 0.001) while that of dNLR with ICU stay (p = 0.002), death (p < 0.001), and invasive ventilation (p = 0.002) on multivariate analysis when adjusted for age, gender, and a wave of pandemics. Moreover, the indices were found correlating with other inflammatory markers such as C-reactive protein (CRP), D-dimer, and procalcitonin (PCT).

Conclusion

Both markers are equally reliable and sensitive for predicting in-hospital outcomes of patients with COVID-19. Early detection and predictive analysis of these markers can allow physicians to risk assessment and prompt management of these patients.

Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity

Proteins are direct products of the genome and metabolites are functional products of interactions between the host and other factors such as environment, disease state, clinical information, etc. Omics data, including proteins and metabolites, are useful in characterizing biological processes underlying COVID-19 along with patient data and clinical information, yet few methods are available to effectively analyze such diverse and unstructured data. Using an integrated approach that combines proteomics and metabolomics data, we investigated the changes in metabolites and proteins in relation to patient characteristics (e.g., age, gender, and health outcome) and clinical information (e.g., metabolic panel and complete blood count test results). We found significant enrichment of biological indicators of lung, liver, and gastrointestinal dysfunction associated with disease severity using publicly available metabolite and protein profiles. Our analyses specifically identified enriched proteins that play a critical role in responses to injury or infection within these anatomical sites, but may contribute to excessive systemic inflammation within the context of COVID-19. Furthermore, we have used this information in conjunction with machine learning algorithms to predict the health status of patients presenting symptoms of COVID-19. This work provides a roadmap for understanding the biochemical pathways and molecular mechanisms that drive disease severity, progression, and treatment of COVID-19.