Prognosis Of COVID-19 Research Articles

High inflammatory indices are significant predictors of disease severity in maintenance hemodialysis patients with COVID-19: a cross-sectional study

BackgroundThe patients with kidney failure who undergo maintenance hemodialysis (MHD) at high risk of morbidity and mortality from COVID-19. Previous studies showed that inflammation plays an important role in the progression of COVID-19. This study aimed to evaluate the prognostic value of the inflammatory indices in MHD patients with COVID-19. MethodsWe included 141 patients receive MHD in this single-center. SARS-CoV-2 infection was confirmed by a positive result in RT-PCR analysis of nasal and pharyngeal swab samples, and the demographic, clinical, and laboratory data from December 1, 2022, to January 31, 2023 were reviewed. Inflammatory indices including PLR, NLR and SII were calculated. Binary logistics regression was used to examine the association between inflammatory indices and SARS-CoV-2 infection in MHD patients. The ROC curves were used to detect the sensitivity and specificity of these inflammatory indices in prediction of SARS-CoV-2 infection status in MHD patients. ResultsSARS-CoV-2 infection was detected in 76.43% of the 141 MHD patients. Lymphocyte (LY), aspartate transaminase (AST), blood urea nitrogen (BUN), uric acid (UA), PLR, NLR and SII were significant predictors of no SARS-CoV-2 infection and symptomatic SARS-CoV-2 infection. In addition, LY, serum ferritin (SF), AST, BUN, UA, PLR and NLR were significant predictors of asymptomatic SARS-CoV-2 infection and symptomatic SARS-CoV-2 infection. The ROC curves showed the best sensitivity and specificity of PLR (66.7% sensitivity; 68.8% specificity) and NLR (51.9% sensitivity; 86.3% specificity) in predicting symptomatic SARS-CoV-2 infection. ConclusionPLR and NLR can be used as simple and inexpensive biomarkers in predicting the prognosis of COVID-19 in MHD patients.

T Lymphocyte Mitochondrial Markers as Independent Risk Factors for Poor Prognosis of COVID-19.

Severe Acute Respiratory Syndrome Virus 2 (SARS-CoV-2) primarily targets mitochondria. However, the description of mitochondrial signaling in immune cells remains limited in COVID-19. This study aimed to elucidate the pivotal roles played by immune cells and mitochondria in the pathogenesis of COVID-19 and the resulting clinical outcomes. We obtained epidemiological characteristics, laboratory parameters and T cell mitochondrial damage indicators in 296 COVID-19 patients. And we further evaluated the predictive value of novel T lymphocyte mitochondrial markers and conventional immune inflammatory markers as clinical outcomes in COVID-19 patients. Finally, Binary logistic regression analysis was conducted to identify the independent risk factors associated with the prognosis of patients with COVID-19. The severe group exhibited lower counts of Mito+CD3+, Mito+CD4+, and Mito+CD8+ cells compared to the non-severe group. Significantly higher positive rates of CD3+, CD3+CD4+, and CD3+CD8+T cell mitochondrial damage were observed in the severe group compared to the non-severe group. The CD3+CD8+T cells MMP-low% had the highest AUC value of 0.864 (95% CI =0.794-0.934) to evaluate COVID-19 outcome. Binary logistic regression analysis showed that CD3+T cells MMP-low%, CD3+CD4+T cells MMP-low% and CD3+CD8+T cells MMP-low% were independent risk factors for adverse outcomes in COVID-19 patients. Our research suggests that a substantial proportion of COVID-19 patients exhibited mitochondrial impairment with T-lymphocyte. T cells mitochondrial markers can serve as predictive factors and independent risk factors for predicting adverse outcomes in COVID-19 patients.

Investigation the Role of Calcium and Vitamin D3 And Some Biochemical Parameters in Patients with Renal Failure of Renal Failure

Coronavirus Disease 2019 is a primarily respiratory illness that can cause thrombotic disorders. Elevation of D-dimer is a potential biomarker for poor prognosis in COVID-19, though optimal cutoff value for D-dimer to predict mortality has not yet been established. This study aims to assess the accuracy of admission D-dimer in the prognosis of COVID-19 and to establish the optimal cutoff D-dimer value to predict hospital mortality. Clinical and laboratory parameters and outcomes of confirmed COVID-19 cases admitted to four hospitals in Kathmandu were retrospectively analyzed. Admitted COVID-19 cases with recorded D-dimer and definitive outcomes were included consecutively. D-dimer was measured using immunofluorescence assay and reported in Fibrinogen Equivalent Unit (μg/ml). The receiver operating characteristic curve was used to determine the accuracy of D-dimer in predicting mortality, and to calculate the optimal cutoff value, based on which patients were divided into two groups and predictive value of D-dimer for mortality was measured. D-dimer levels were higher in COVID-19 patients and were related with markers of inflammation, and after treatments, D-dimer levels decreased which was synchronous with hsCRP levels in patients with good clinical prognosis. Also, the low correlation between Padua VTE score and D-dimer levels weakened the role of D-dimer in the prediction of thrombosis.The abnormal changes of D-dimer and inflammatory factors suggest that aggressive anticoagulant therapy might be needed.

Types of pulmonary hypertension and prognosis in COVID-19: 18-months analysis of BNP-PL national database

Abstract Introduction The COVID-19 pandemic significantly increased mortality in the group of patients (pts) with pulmonary hypertension (PH). However, there is no data whether specific types of this disease, such as PAH associated with connective tissue diseases, were more likely to have a poor prognosis. Aim To assess mortality in the course of COVID-19 among pts diagnosed with PH, treated under the Polish ministry therapeutic program and registered in the national BNP-PL database, according to the type of PH. Methods We analyzed the records of the complete population of Polish pts treated under the National Drug Program of PH (PAH and CTEPH) and registered in the national database of BNP-PL from 01 March 2020 to 31 August 2022. The clinical severity of COVID-19 course and mortality were reviewed. Clinical characteristics of infected and deceased patients were compared in relation to the type of PH. Results The analysis included 143 pts with SARS-CoV-2 infection (mean age 61 ± 15 years; 60% female). Mortality rate was 24% (34/143, including 20 out-of-hospital deaths) vs. 2.6% for estimated in the general population. The highest mortality was observed in the group of pts with idiopathic PAH (33%), slightly lower among pts with connective tissue diseases [CTD] (29%). Patients with congenital heart diseases had a fairly good prognosis [CHD] (81% recovered), and the best survival was recorded in those with CTEPH (84%). CTD and IPAH were found to represent "high-risk" subset with mortality risk ~ 32% vs. ~ 17% in others (p=0.032). Etiologic categories had significant trend for different COVID-19 outcomes according to chi2 test for trend (p=0.039). Conclusions The SARS-CoV-2 infection in pulmonary hypertension pts was related to high mortality, with pts with IPAH had the worst prognosis, folloved by pts with CTD who might already have had lung involvement. Interestingly, the best survival was recorded in one of the oldest group - patients with CTEPH. This may be related to the chronic anticoagulant treatment used in this group, as significant disorders of coagulation pathways have been observed in SARS-CoV-2 infection.

Record linkage as a vital key player for the COVID-19 syndemic

Abstract Chronicity, comorbidities, and social context influence COVID-19 risk and prognosis, drawing attention to a syndemic dimension. We discuss how linking routinely collected data and data collected through observational population-based cohorts can contribute to advance knowledge on the association between COVID-19 and chronic diseases and social indicators. Record linkage as a multidimensional tool may ultimately enable defining and optimizing of integrated strategies, which may create multilevel public health measures to foster solidarity on health in all policies when ethical and legal barriers under prerequisite data protection and privacy can be overcome. Legal discrepancies have been proving detrimental to research in member states, including those which already had established a margin for research for the common good without explicit consent. We hence call for further harmonization of data protection requirements for scientific research activities in the EU/EEA, focusing in particular on health-related research. A proposed framework demonstrates the role of record linkage as a trailblazing key player in research optimization due to its multidimensional possibilities calling for harmonization. Key messages • Further harmonization of data protection requirements for scientific research may create multilevel public health measures. • As a multidimensional tool, it optimizes integrated strategies and fosters solidarity on Health in All Policies (HiAP).

BCR, not TCR, repertoire diversity is associated with favorable COVID-19 prognosis.

The SARS-CoV-2 pandemic has had a widespread and severe impact on society, yet there have also been instances of remarkable recovery, even in critically ill patients. In this study, we used single-cell RNA sequencing to analyze the immune responses in recovered and deceased COVID-19 patients during moderate and critical stages. Expanded T cell receptor (TCR) clones were predominantly SARS-CoV-2-specific, but represented only a small fraction of the total repertoire in all patients. In contrast, while deceased patients exhibited monoclonal B cell receptor (BCR) expansions without COVID-19 specificity, survivors demonstrated diverse and specific BCR clones. These findings suggest that neither TCR diversity nor BCR monoclonal expansions are sufficient for viral clearance and subsequent recovery. Differential gene expression analysis revealed that protein biosynthetic processes were enriched in survivors, but that potentially damaging mitochondrial ATP metabolism was activated in the deceased. This study underscores that BCR repertoire diversity, but not TCR diversity, correlates with favorable outcomes in COVID-19.

The Impact of COVID-19 Vaccination and Infection on the Exacerbation of Myasthenia Gravis

Objectives: Myasthenia Gravis (MG) is an autoimmune disorder that can exacerbate for various reasons, including vaccination and infection. This study aimed to investigate the safety of COVID-19 vaccines for MG patients, factors influencing MG exacerbation after COVID-19 infection (MECI), the course and prognosis of MECI, and the impact of COVID-19 vaccine on infected MG patients. Methods: Patients were enrolled from the MG database in the Department of Neurology, Xuanwu Hospital, Capital Medical University. Two questionnaires were administered to collect data concerning COVID-19 vaccination (questionnaire 1, Q1) and infection (questionnaire 2, Q2) during two distinct periods. MG exacerbation was defined as an increase of at least two points in the MG activity of daily living (MG-ADL) score. COVID-19 severity was categorized as “hospitalization” or “home management”; Results: During the first data-collecting period, our database registered 1013 adult patients: 273 (26.9%) had received COVID-19 vaccinations and completed Q1, and 8 (2.9%) experienced MG exacerbation after vaccination. During the second data-collecting period, among the newly registered patients, 366 patients completed Q2. Of these, 244 were infected, with 39 (16.0%) experiencing MECI and 21 (8.6%) requiring hospitalization. Multivariate analysis showed that generalized myasthenia gravis was associated with MECI (OR 3.354, 95% CI: 1.423–7.908, p = 0.006). Among the 244 infected patients, 143 had received COVID-19 vaccinations, including 14 who received their booster dose within 6 months before COVID-19 and 129 who were vaccinated more than 6 months before COVID-19. The remaining 101 were unvaccinated. No significant associations were found between COVID-19 vaccination and COVID-19 severity (p = 0.292) or MECI incidence (p = 0.478); Conclusions: COVID-19 vaccines were found to be safe for MG patients in stable condition. Patients with gMG were more susceptible to experiencing MECI. No significant impact of the vaccine on COVID-19 severity or MECI incidence was observed.

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome.

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk and enhance clinical management. Optimal efficacy of COVID-19 antiviral medications relies on early administration within the initial five days of symptoms, assisting high-risk patients in avoiding hospitalization and improving survival chances. The complete blood count can be an efficient and affordable option to find biomarkers that predict the COVID-19 prognosis due to infection-induced alterations in various blood parameters. This study aimed to associate hematological parameters with different COVID-19 clinical forms and utilize them as disease outcome predictors. We performed a complete blood count in blood samples from 297 individuals with COVID-19 from Belo Horizonte, Brazil. Statistical analysis, as well as ROC Curves and machine learning Decision Tree algorithms were used to identify correlations, and their accuracy, between blood parameters and disease severity. In the initial four days of infection, traditional hematological COVID-19 alterations, such as lymphopenia, were not yet apparent. However, the monocyte percentage and granulocyte-to-lymphocyte ratio proved to be reliable predictors for hospitalization, even in cases where patients exhibited mild symptoms that later progressed to hospitalization. Thus, our findings demonstrate that COVID-19 patients with monocyte percentages lower than 7.7% and a granulocyte-to-lymphocyte ratio higher than 8.75 are assigned to the hospitalized group with a precision of 86%. This suggests that these variables can serve as important biomarkers in predicting disease outcomes and could be used to differentiate patients at hospital admission for managing therapeutic interventions, including early antiviral administration. Moreover, they are simple parameters that can be useful in minimally equipped health care units.

MicroRNAs are enriched at COVID-19 genomic risk regions, and their blood levels correlate with the COVID-19 prognosis of cancer patients infected by SARS-CoV-2

BackgroundCancer patients are more susceptible to an aggressive course of COVID-19. Developing biomarkers identifying cancer patients at high risk of COVID-19-related death could help determine who needs early clinical intervention. The miRNAs hosted in the genomic regions associated with the risk of aggressive COVID-19 could represent potential biomarkers for clinical outcomes.Patients and methodsPlasma samples were collected at The University of Texas MD Anderson Cancer Center from cancer patients (N = 128) affected by COVID-19. Serum samples were collected from vaccinated healthy individuals (n = 23) at the Municipal Clinical Emergency Teaching Hospital in Timisoara, Romania. An in silico positional cloning approach was used to identify the presence of miRNAs at COVID-19 risk-associated genomic regions: CORSAIRs (COvid-19 RiSk AssocIated genomic Regions). The miRNA levels were measured by RT-qPCR.ResultsWe found that miRNAs were enriched in CORSAIR. Low plasma levels of hsa-miR-150-5p and hsa-miR-93-5p were associated with higher COVID-19-related death. The levels of hsa-miR-92b-3p were associated with SARS-CoV-2 test positivity. Peripheral blood mononuclear cells (PBMC) increased secretion of hsa-miR-150-5p, hsa-miR-93-5p, and hsa-miR-92b-3p after in vitro TLR7/8- and T cell receptor (TCR)-mediated activation. Increased levels of these three miRNAs were measured in the serum samples of healthy individuals between one and nine months after the second dose of the Pfizer-BioNTech COVID-19 vaccine. SARS-CoV-2 infection of human airway epithelial cells influenced the miRNA levels inside their secreted extracellular vesicles.ConclusionsMiRNAs are enriched at CORSAIR. Plasma miRNA levels can represent a potential blood biomarker for predicting COVID-19-related death in cancer patients.

Malnutrition is associated with severe outcome in elderly patients hospitalised with COVID-19

Some studies have identified influencing factors of COVID-19 illness in elderly, such as underlying diseases, but research on the effect of nutritional status is still lacking. This study retrospectively examined the influence of nutritional status on the outcome of elderly COVID-19 inpatients. A retrospective analysis of the clinical data of 4241 COVID-19 patients who were admitted to a third-class hospital of Nanchang between November 1, 2022 and January 31, 2023 was conducted. Nutritional status was assessed using the prognostic nutritional index (PNI) and controlling nutritional status score (CONUT). The influence of nutritional status on the outcome of COVID-19 patients was determined through multivariate adjustment analysis, restrictive cubic spline, and receiver operating characteristic curve (ROC). Compared with mild/no malnutrition, severe malnutrition substantially increased the critical outcome of COVID-19. A linear relationship was observed between the odds ratio (OR) and PNI and CONUT (P > 0.05). The area under the ROC curve indicated that PNI was the better predictor. The optimal cutoff value of PNI was 38.04 (95%CI: 0.797 ~ 0.836, AUC = 0.817), with a sensitivity of 70.7% and a specificity of 79.6%. The critical illness of elderly COVID-19 patients shows a linear relationship with malnutrition at admission. The use of PNI to assess the prognosis of COVID-19 eldeely patients is reliable, highlighting the importance for doctors to closely pay attention to the nutritional status of COVID-19 patients. Focusing on nutritional status in clinical practice can effectively reduce the critical illness of elderly COVID-19 patients.

Association of COVID-19 with thyroid dysfunction and autoimmune thyroid disease: A retrospective cohort study

BackgroundSince the end of the COVID-19 pandemic, the potential roles of thyroid-inflammatory derangements in driving or being associated with the prognosis of COVID-19 remain controversial. We aimed to clarify the association between COVID-19 infection and thyroid dysfunction, and highlight the impacts of subsequent autoimmune thyroid disease (AITD) on the prognosis of COVID-19. MethodsThe retrospective, multicenter, cohort study enrolled 2,339 participants with COVID-19 from three hospitals located in the north, middle, and south regions of Shaan Xi Province, China, between December 2022 and July 2023. 464 non-COVID-19 patients within the same period were supplemented, divided into groups with and without AITD. At hospital admission (baseline), 3- and 6-month follow-ups, we presented a dynamic description and correlation analysis of thyroid-inflammatory-autoimmune derangements in patients with AITD. ResultsA total of 2,082 COVID-19 patients diagnosed with AITD and 257 cases without AITD were included in the study, and 464 non-COVID-19 patients were supplemented, dividing into 14 AITD and 450 non-AITD cases. We found that COVID-19 infection was closely associated with thyroid dysfunction (χ2 = 1518.129, p = 0.000). AITD patients with COVID-19 showed a higher prevalence of symptoms and comorbidities and longer hospital stays at baseline than non-AITD patients with COVID-19 (p = 0.000, p = 0.000, and p = 0.000). The baseline free triiodothyronine (FT3), free thyroxine, and radioactive iodine uptake at 24 h in AITD cases significantly decreased (p = 0.000, p = 0.000, and p = 0.000), while thyroid stimulating hormone, thyroglobulin, reverse triiodothyronine (rT3), and thyroid antibodies varying elevated from the baseline to the follow-up (baseline: p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.000, and p = 0.000; 3-month follow-up: p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.030, and p = 0.000). C-reactive protein, calcitonin, interleukin-6, -8, -10, and tumor necrosis factor-α rose significantly at baseline (p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.000, and p = 0.000) in AITD. Interferon-α and interferon-γ at baseline showed a significant decrease (p = 0.000 and p = 0.000), and remained at low levels after 6 months (p = 0.000 and p = 0.000). FT3 and rT3 were positively and negatively correlated with hospitalization, respectively (r = -0.208 and 0.231; p = 0.000 and p = 0.000). ROC curves showed that FT3 and rT3 had better robustness in predicting severe COVID-19 prognosis (AUC = 0.801 and 0.705). Ordered logistic regression revealed that ORs were 0.370, 0.048, and 0.021 for AITD [(subacute thyroiditis, Grave's disease, and Hashimoto's thyroiditis compared to non-thyroidal illness syndrome (NTIS)] with COVID-19 risk, indicating that NTIS was the predominant risk factor for the severity of COVID-19. ConclusionsA robust association has been identified, wherein COVID-19 infection is closely associated with thyroid dysfunction, and the subsequent AITD may aggravate the poor prognosis of COVID-19.

Clinical characteristics of SARS-CoV-2 Omicron pneumonia in immunocompromised and immunocompetent patients: A retrospective cohort study

ObjectivePrevious studies evaluating the differences in COVID-19 mortality rates between immunocompromised patients and other patient groups have shown conflicting findings. This research aimed to compare the mortality rates of immunocompromised and immunocompetent patients during the Omicron-dominant period of the SARS-CoV-2 pandemic, and to identify factors associated with prognosis. MethodsWe conducted a retrospective analysis of 1085 adult patients (aged ≥18 years) admitted with COVID-19 pneumonia to the China-Japan Friendship Hospital between December 1, 2022, and January 31, 2023. We assessed the prevalence of comorbidities, incidence of co-infections and nosocomial infections, and 30-day mortality. ResultsAmong the 1085 patients, 254 were immunocompromised, and 831 were immunocompetent. Immunocompromised patients had higher rates of non-invasive ventilation use (30.3 % vs. 21.1 %), invasive ventilation (12.2 % vs. 5.3 %), and 30-day mortality (19.7 % vs. 13.7 %) compared to immunocompetent patients. However, overall mortality rates did not significantly differ based on immunocompromised status. Cox regression analysis identified that elevated troponin T (≥0.15 ng/mL), respiratory failure, high lactate dehydrogenase (≥272.5 U/L), elevated D-dimer (≥1.295 mg/L), increased C-reactive protein (≥90 mg/L), elevated interleukin-6 (>11.67 ng/L), high peripheral blood neutrophil count (>9.84 × 10⁹/L), and immunocompromised status were independent predictors of poor COVID-19 prognosis. In the immunocompetent group, current smoking and a history of interstitial lung disease were related to a worse prognosis. ConclusionsCOVID-19 pneumonia due to the Omicron variant may lead to worse outcomes in immunocompromised patients. In immunocompetent patients, careful monitoring is essential for those with respiratory failure, smoking history, or interstitial lung disease to prevent adverse outcomes.

Real-world effectiveness of inactivated vaccine on COVID-19 patients with comorbidities.

Patients with underlying diseases do not respond adequately to vaccines. Thus, continued research on the effects of vaccination in patients with comorbidities is crucial to evaluate the necessity of vaccination in this population. This study assessed the protective effects of inactivated vaccines on the severity and prognosis of COVID-19 in patients with comorbidities. A real-world retrospective cohort study was conducted from April 7, 2022, to June 6, 2022, at the Fudan University Pudong Medical Center. The collected data included demographic characteristics, symptoms, clinical severity, and outcomes of the COVID-19 patients. A total of 3,996 indigenous confirmed cases and asymptomatic infections with the Omicron variant were enrolled. Of these, 1322 (33.1%) patients had chronic comorbidities. Compared to others, COVID-19 patients with comorbidities were older, had lower vaccination rates, longer days of nucleic acid conversion and hospitalization, and a higher incidence of severe-critical illness and composite endpoint. Multivariable analyses suggested that in the comorbidity group, two-dose- (odds ratio [OR] 0.38, 95% CI 0.24-0.60; OR 0.20, 95% CI 0.08-0.51) and three-dose vaccinated patients (OR 0.26, 95% CI 0.14-0.47; OR 0.21, 95% CI 0.08-0.58) had a lower risk of aggravation and the composite endpoint; similar results were observed in the non-comorbidity group. Two or more doses of inactivated vaccines could prevent deterioration and poor prognosis in Omicron-infected patients, regardless of the presence of an underlying disease. Our findings support maximizing coverage with inactivated vaccines in highly vaccinated populations, such as those in China.

Bacterial co-infection in COVID-19: a call to stay vigilant.

Co-infection with diverse bacteria is commonly seen in patients infected with the novel coronavirus, SARS-CoV-2. This type of co-infection significantly impacts the occurrence and development of novel coronavirus infection. Bacterial co-pathogens are typically identified in the respiratory system and blood culture, which complicates the diagnosis, treatment, and prognosis of COVID-19, and even exacerbates the severity of disease symptoms and increases mortality rates. However, the status and impact of bacterial co-infections during the COVID-19 pandemic have not been properly studied. Recently, the amount of literature on the co-infection of SARS-CoV-2 and bacteria has gradually increased, enabling a comprehensive discussion on this type of co-infection. In this study, we focus on bacterial infections in the respiratory system and blood of patients with COVID-19 because these infection types significantly affect the severity and mortality of COVID-19. Furthermore, the progression of COVID-19 has markedly elevated the antimicrobial resistance among specific bacteria, such as Klebsiella pneumoniae, in clinical settings including intensive care units (ICUs). Grasping these resistance patterns is pivotal for the optimal utilization and stewardship of antibiotics, including fluoroquinolones. Our study offers insights into these aspects and serves as a fundamental basis for devising effective therapeutic strategies. We primarily sourced our articles from PubMed, ScienceDirect, Scopus, and Google Scholar. We queried these databases using specific search terms related to COVID-19 and its co-infections with bacteria or fungi, and selectively chose relevant articles for inclusion in our review.

Can CRP and Lymphocyte Count be Considered as Predictive Factors for the Prognosis of COVID-19 in Intensive Care? An Analytic Study

Introduction: SARS Cov2 infection still represents a real threat whose clinical severity results from an inadequate immuno-inflammatory reaction. The objective of our study was to determine the prognostic interest of the value of CRP and the rate of lymphocytes in the management of patients contaminated by SARS-Cov2. Material And Methods: This is a prospective, descriptive and analytical study of interest to patients with severe COVID-19, admitted to medical intensive care at the Oued Eddahab military hospital, for a period of one year. The biological parameters were analyzed on admission and during the stay in intensive care. The ROC curve was used to determine the sensitivity and specificity of CRP and lymphocytes as well as their optimal predictive threshold values. Results: 32 patients were included in our study. The average age of admissions was 65 years ± 12.38 with a sex ratio of 5.4 in favor of men. The optimal predictive threshold for the severity found was 147mg/l for CRP with a sensitization of 95% and a specificity of 83.3%, and 807/mm3 for lymphocytes with a sensitivity of 91.7% and a specificity of 95%. The odds ratio (OR) found for CRP and lymphocytes was > 1. Conclusion: The CRP and the level of lymphocytes at the threshold defined above are risk factors for the severity of Sars-Cov2 infection.

Clinical Phenotyping for Prognosis and Immunotherapy Guidance in Bacterial Sepsis and COVID-19.

It is suggested that sepsis may be classified into four clinical phenotypes, using an algorithm employing 29 admission parameters. We applied a simplified phenotyping algorithm among patients with bacterial sepsis and severe COVID-19 and assessed characteristics and outcomes of the derived phenotypes. Retrospective analysis of data from prospective clinical studies. Greek ICUs and Internal Medicine departments. We analyzed 1498 patients, 620 with bacterial sepsis and 878 with severe COVID-19. We implemented a six-parameter algorithm (creatinine, lactate, aspartate transaminase, bilirubin, C-reactive protein, and international normalized ratio) to classify patients with bacterial sepsis intro previously defined phenotypes. Patients with severe COVID-19, included in two open-label immunotherapy trials were subsequently classified. Heterogeneity of treatment effect of anakinra was assessed. The primary outcome was 28-day mortality. The algorithm validated the presence of the four phenotypes across the cohort of bacterial sepsis and the individual studies included in this cohort. Phenotype α represented younger patients with low risk of death, β was associated with high comorbidity burden, and δ with the highest mortality. Phenotype assignment was independently associated with outcome, even after adjustment for Charlson Comorbidity Index. Phenotype distribution and outcomes in severe COVID-19 followed a similar pattern. A simplified algorithm successfully identified previously derived phenotypes of bacterial sepsis, which were predictive of outcome. This classification may apply to patients with severe COVID-19 with prognostic implications.

Optimal choice of indicators for timely assessment of the prognosis of COVID-19 in hospitalized patients

The aim of the study was to identify the indicators that are most significant for predicting the features of the course of COVID-19 in hospitalized patients.Materials and methods. 250 case histories of patients aged 18 to 86 years with COVID-19 hospitalized in the hospital of the city of Nukus, Republic of Uzbekistan, redesigned to provide care to patients with COVID-19 from July 1, 2020 to March 2022, were analyzed. Patients who had a wave–like course of the disease with the development of complications, an increase in the volume of lung damage, were included in the main group (62 patients, 3 of whom were extremely severe). The patients who had stable positive dynamics (188 people) formed a comparison group.Results and discussion. Among patients over 65 years of age, 36% had a complicated course, in the 45–65 age group — 29% (p>0.05), and among patients under 45 years of age — 13% (p<0.05). The main group was dominated by men (79%). Among hospitalized villagers, an increase in the severity of the condition was noted in 30% of cases, and among patients from the city of Nukus, such patients were 20% (p<0.05). The highest values of D-dimer, interleukin-6 and C-reactive protein were in both groups, significant differences between the groups were revealed in the levels of D-dimer and interleukin-6. Significant differences between the groups were found in the levels of D-dimer, interleukin-6, ferritin, ALT, AST, and urea.Conclusion. COVID-19 has become one of the most studied diseases to date, but aspects of the course of this disease in certain population groups are still not sufficiently investigated. The complicated, progressive course of COVID-19, according to the results of our study, was recorded in all age groups of the adult population, more often in men from rural areas aged over 45 years with chronic diseases. The main prognostic markers of the complicated and progressive course of COVID-19 should be considered high levels of D-dimer, interleukin-6 and ferritin.

Clinical data and quantitative CT parameters combined with machine learning to predict short-term prognosis of severe COVID-19 in the elderly

Rationale and objectivesThis study aims to evaluate the effectiveness of integrating clinical data and quantitative CT parameters with machine learning techniques in forecasting the short-term outcomes of severe COVID-19 in elderly patients. Materials and methodsIn this retrospective study, we analyzed the clinical profiles and chest quantitative CT parameters of 239 elderly patients with severe COVID-19 admitted for treatment. The cohort included 61 deceased patients (death group) and 178 who recovered and were discharged (survival group). The participants were randomly assigned into a training group (n = 167) and a validation group (n = 72). Quantitative CT parameters were measured using the 3D-Slicer software. Univariate and multivariate logistic regression analyses identified independent risk factors for mortality. Predictive models were developed employing four machine learning algorithms: Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). ResultsBoth univariate and multivariate logistic regression analyses revealed age, hypersensitive C-reactive protein (hs-CRP), and solid organ volume percentage (SOV%) as independent predictors of mortality. The Area Under the Curve (AUC) values for the LR, RF, DT, and SVM models in the training group were 0.795, 0.726, 0.854, and 0.589, respectively; for the validation group, they were 0.817, 0.634, 0.869, and 0.754, respectively. The DT algorithm outperformed other models in both the training and validation groups, emerging as the most effective predictive model in this study. ConclusionThe combination of clinical data and quantitative CT parameters with machine learning approaches is highly valuable in predicting the short-term prognosis of severe COVID-19 in the elderly. Among the various models tested, the Decision Tree algorithm-based model proved to be the most accurate and reliable in this context.

Prognosis of the post COVID-19 stroke based on D-dimer and inflammatory markers: A mixed model meta-regression analysis

Background & Objective: With regard to the importance of inflammatory biomarker and stroke in COVID-19 prognosis, this study aims to compare the mortality of stroke in COVID-19 patients based on the level of inflammatory biomarkers, considering the temporality of stroke occurrence after COVID-19 infection. Methods: As a secondary study, aggregate data were collected by a systematic review. Mixed model meta-regression analyses were conducted to study the effects of stroke, inflammatory biomarkers and D-dimer on mortality of patient with COVID-19 as main effects and interactions. The effect measure was death rate. The protocol was registered in PROSPERO with registration number CRD42023383065. Results: A total of 2,725 COVID-19 patients, consisting of 96 patients with stroke with a temporal precedence of COVID-19 were investigated from three studies. Among the inflammatory biomarkers, only CRP found eligible for pooled analysis. In the interaction model of CRP, significant positive interactions with CRP were found for both groups of stroke and non-stroke (P <0.001), in which the interaction with stroke group was more severe. In the interaction model of D-dimer, a significant positive interaction with D-dimer was found for the stroke group (P <0.001), while the interaction with the non-stroke group was not statistically significant (P =0.158). Conclusion: The present study found the role CRP and D-dimer in prognosis of stroke in patients with COVID-19 as both main effect and interaction modeling. The evidence obtained from these mixed model meta-regression analyses was of moderate quality.

The relationship between COVID-19 and hyperglycemia: screening and monitoring hospitalized patients

BackgroundElevated blood glucose concentration, also known as hyperglycemia, has been identified as a significant factor influencing the prognosis of COVID-19, alongside the impact of the SARS-CoV-2 infection itself.MethodsThis research is a cross-sectional investigation that examined the relationship between COVID-19 and hyperglycemia in patients admitted to Afzalipour Hospital in Kerman, Iran, from July to September 2021. A standardized data sheet was used to capture demographic data (age, gender) and laboratory information (blood sugar, arterial blood oxygen saturation, and C-reactive protein (CRP)) upon admission.ResultsThe present research evaluated a total of 300 individuals diagnosed with COVID-19, with an average age of 50.19 ± 15.55 years. Among these patients, the majority were male, accounting for 51.67% of the total. Hyperglycemia was seen in 21.67% of patients, but less than 20% had new-onset diabetes. Individuals exhibiting hyperglycemia were typical of advanced age (P < 0.001). Furthermore, there was a slight but statistically significant association between advanced age and elevated blood glucose concentration (R = 0.254, P < 0.001). Gender had no significant impact on the occurrence of hyperglycemia (P = 0.199). There was no significant association between CRP levels and blood glucose concentration (P = 0.524) or the incidence of hyperglycemia (P = 0.473). Although there was no significant disparity in blood oxygen saturation between individuals with or without hyperglycemia (P = 0.06), higher blood glucose concentration was correlated with lower blood oxygen saturation (R = -0.151, P < 0.001).ConclusionConsidering the correlation between blood glucose concentration, advanced age, and disease severity, it is recommended to carefully screen and monitor all COVID-19 patients for hyperglycemia and new-onset diabetes. Effective management of these complications could enhance the control of patients’ overall prognosis and subsequent complications.