Proinflammatory and Hepatic Features Related to Morbidity and Fatal Outcomes in COVID-19 Patients

Association of haemato-biochemical indices and blood composite ratios with microfilaridermia in Onchocerciasis patients

BACKGROUND

Onchocerciasis causes chronic systemic inflammation. Several studies have used markers such as haemato-biochemical indices to predict the occurrence of systemic inflammation. This study assessed the variability and predictability of haemato-biochemical indices and blood composite ratios (BCRs) in microfilariae positive (MF+) and microfilariae negative (MF-) subgroups of onchocercomata participants.

METHODS

One hundred and five (105) MF + and 34 MF- participants were retrospectively recruited into the study. Screening for the presence of O. volvulus microfilariae was done from skin snips taken from the left and right iliac crests of participants using established and approved protocols. Haematological and biochemical indices were measured using standard laboratory automated analyzers. Blood composite ratios (BCRs) were calculated as ratios of the absolute parameters involved.

RESULTS

A significantly increased total WBC, absolute eosinophil, eosinophil percent and absolute basophil were observed in the MF + participants compared to MF- participants. Reduced gamma-glutamyl transferase (GGT) with increased estimated glomerular filtration rate (eGFR) was significantly associated with MF + participants compared to MF- participants. BCRs were significantly higher for eosinophil-to-neutrophil ratio (ENR), eosinophil-to-monocyte ratio (EMR), eosinophil-to-basophil ratio (EBR) and eosinophil-to-lymphocyte ratio (ELR) in MF + participants compared to MF- participants. After multivariate adjustment, onchocercomata participants with increased eosinophil counts (aOR = 13.86, 95% CI [2.07-92.90], p = 0.007), ENR x10 (aOR = 1.42, 95% CI [1.05-1.93], p = 0.025), EMR (aOR = 2.64, 95% CI [1.25-5.60], p = 0.011), EBR (aOR = 1.07, 95% CI [1.01-1.10], p = 0.020) and ELR x10 (aOR = 1.69, 95% CI [1.14-2.51], p = 0.009) were more likely to have microfilaridermia.

CONCLUSIONS

Elevated eosinophil counts with higher ENR, EMR, EBR and ELR levels are significantly associated with microfilaridermia in onchocercomata participants. Combining BCRs with eosinophil count significantly led to an improvement in the conventional model for predicting microfilaridermia.

An Updated Systematic Review and Meta-Analysis of the Association between the De Ritis Ratio and Disease Severity and Mortality in Patients with COVID-19

Patients with Coronavirus disease 2019 (COVID-19) often have elevations in markers of liver injury, particularly serum aspartate transaminase (AST) and alanine transaminase (ALT). Such alterations may affect the AST/ALT ratio (De Ritis ratio) and, potentially, clinical outcomes. We conducted an updated systematic review and meta-analysis of the association between the De Ritis ratio and COVID-19 severity and mortality in hospitalized patients. PubMed, Web of Science, and Scopus were searched between 1 December 2019 and 15 February 2023. The Joanna Briggs Institute Critical Appraisal Checklist and the Grading of Recommendations, Assessment, Development, and Evaluation were used to assess the risk of bias and the certainty of the evidence, respectively. Twenty-four studies were identified. The De Ritis ratio on admission was significantly higher in patients with severe disease and non-survivors vs. patients with non-severe disease and survivors (15 studies, weighted mean difference = 0.36, 95% CI 0.24 to 0.49, p < 0.001). The De Ritis ratio was also associated with severe disease and/or mortality using odds ratios (1.83, 95% CI 1.40 to 2.39, p ˂ 0.001; nine studies). Similar results were observed using hazard ratios (2.36, 95% CI 1.17 to 4.79, p = 0.017; five studies). In six studies, the pooled area under the receiver operating characteristic curve was 0.677 (95% CI 0.612 to 0.743). In our systematic review and meta-analysis, higher De Ritis ratios were significantly associated with severe disease and mortality in COVID-19 patients. Therefore, the De Ritis ratio can be useful for early risk stratification and management in this patient group (PROSPERO registration number: CRD42023406916).

An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO2) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO2 presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO2 in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO2 in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Comparison of liver function test- and inflammation-based prognostic scores for coronavirus disease 2019: a single center study

BACKGROUND

Although several liver- and inflammation-based scores to predict the clinical course of patients with coronavirus disease 2019 (COVID-19) have been evaluated, no direct comparison regarding their predictive ability has been performed.

METHODS

1038 patients (608 males, age 63.5 ± 17 years) hospitalized with documented COVID-19 infection to the non-ICU ward, were included retrospectively. Clinical and laboratory characteristics on admission including evaluation of Fibrosis-4 (FIB-4) score and C-Reactive Protein (CRP) to albumin ratio (CAR) were recorded.

RESULTS

One hundred and twenty-four patients (11.9%) died during hospitalization after 8 (3-72) days. In multivariate analysis, FIB-4 (hazard ratio, 1.11; 95% confidence interval (CI), 1.034-1.19; P = 0.004), was independently associated with mortality, with very good discriminative ability (area under the receiver operating characteristic curve curve, 0.76). The patients with FIB-4 &amp;gt;2.67 (n = 377), compared to those with ≤2.67 (n = 661), had worse survival (log-rank 32.6; P &amp;lt; 0.001). Twenty-four (6.8%) of 352 patients with possible nonalcoholic fatty liver disease (NAFLD) (defined as Hepatic Steatosis Index &amp;gt;36) died during hospitalization. In multivariate analysis, CAR was an independent risk factor (1) for mortality (hazard ratio, 1.014; 95% CI, 1.002-1.025; P = 0.021), (2) the need for high-flow nasal cannula with or without intubation (hazard ratio, 1.016; 95% CI, 1.004-1.027; P = 0.007) and (3) development of acute kidney injury (hazard ratio, 1.017; 95% CI, 1.006-1.028; P = 0.002). In addition, the patients with possible NAFLD and CAR &amp;gt;12 (n = 154), compared to those with CAR ≤12 (n = 198), had worse survival (log-rank 5.1; P = 0.024).

CONCLUSIONS

FIB-4 was an independent factor for mortality with better performance compared to other liver function test- and inflammation-based scores in patients with COVID-19, while CAR was the only score independently associated with the clinical course in COVID-19 patients with possible NAFLD.

Fibrosis-4 (FIB-4) Index and mortality in COVID-19 patients admitted to the emergency department

Liver damage worsens the prognosis of coronavirus 19 disease (COVID-19). However, the best strategy to stratify mortality risk according to liver damage has not been established. The aim of this study is to test the predictive value of the validated Fibrosis-4 (FIB-4) Index and compared it to liver transaminases and to the AST-to-Platelet ratio index (APRI). Multicenter cohort study including 992 consecutive COVID-19 patients admitted to the Emergency Department. FIB-4 > 3.25 and APRI > 0.7 were used to define liver damage. Multivariable Cox regression and ROC curve analysis for mortality were performed. Secondary endpoints were (1) need for high-flow oxygen and (2) mechanical ventilation. 240 (24.2%) patients had a FIB-4 > 3.25. FIB-4 > 3.25 associated with an increased mortality (n = 119, log-rank test p < 0.001 and adjusted hazard ratio (HR) 1.72 (95% confidence interval [95%CI] 1.14-2.59, p = 0.010). ROC analysis for mortality showed that FIB-4 (AUC 0.734, 95% CI 0.705-0.761) had a higher predictive value than AST (p = 0.0018) and ALT (p < 0.0001). FIB-4 > 3.25 was also superior to APRI > 0.7 (AUC 0.58, 95% CI 0.553-0.615, p = 0.0008). Using an optimized cut-off > 2.76 (AUC 0.689, 95% CI 0.659-0.718, p < 0.0001), FIB-4 was superior to FIB-4 > 3.25 (p = 0.0302), APRI > 0.7 (p < 0.0001), AST > 51 (p = 0.0119) and ALT > 42 (p < 0.0001). FIB-4 was also associated with high-flow oxygen use (n = 255, HR 1.69, 95% CI 1.25-2.28, p = 0.001) and mechanical ventilation (n = 39, HR 2.07, 95% CI 1.03-4.19, p = 0.043). FIB-4 score predicts mortality better than liver transaminases and APRI score. FIB-4 score may be an easy tool to identify COVID-19 patients at worse prognosis in the emergency department.

Hematological- and Immunological-Related Biomarkers to Characterize Patients with COVID-19 from Other Viral Respiratory Diseases

COVID-19 has overloaded health system worldwide; thus, it demanded a triage method for an efficient and early discrimination of patients with COVID-19. The objective of this research was to perform a model based on commonly requested hematological variables for an early featuring of patients with COVID-19 form other viral pneumonia. This investigation enrolled 951 patients (mean of age 68 and 56% of male) who underwent a PCR test for respiratory viruses between January 2019 and January 2020, and those who underwent a PCR test for detection of SARS-CoV-2 between February 2020 and October 2020. A comparative analysis of the population according to PCR tests and logistic regression model was performed. A total of 10 variables were found for the characterization of COVID-19: age, sex, anemia, immunosuppression, C-reactive protein, chronic obstructive pulmonary disease, cardiorespiratory disease, metastasis, leukocytes and monocytes. The ROC curve revealed a sensitivity and specificity of 75%. A deep analysis showed low levels of leukocytes in COVID-19-positive patients, which could be used as a primary outcome of COVID-19 detection. In conclusion, this investigation found that commonly requested laboratory variables are able to help physicians to distinguish COVID-19 and perform a quick stratification of patients into different prognostic categories.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

Interaction of ACEI antihypertensive agent's administration with the inflammatory status at admission concerning COVID-19 clinical stay outcomes

Interactions between anti-hypertensive agents (ACEI), comorbidities, inflammation, and stress status may impact hospital stay duration in COVID-19 patients. This retrospective study analyzed epidemiological data, comorbidities, metabolic/inflammatory markers, and clinical information from 165 SARS-CoV-2 positive patients. In a multiple linear regression model, an IL-6 higher than 100 mg/L, glucose at admission (baseline levels at the hospital entry), and the interaction between ACEI administration and LDH predicted the days of hospital admission (P < 0.001). In conclusion, hypertensive patients suffering more severe inflammatory condition assessed by LDH levels clinically benefited more and reduced the hospital stay when prescribed ACEI agents than those with lower systemic baseline inflammation at admission.

Physiological Network Is Disrupted in Severe COVID-19

The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism's homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else. The goal of this study is to determine the differences in the relationships that link physiological variables that characterize the elderly and men, and those that characterize fatal outcomes in young men. To accomplish this, we examined a database of patients with moderate to severe COVID-19 (471 men and 277 women) registered at the "Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán" in March 2020. The sample was stratified by outcome, age, and sex. Physiological networks were built using 67 physiological variables (vital signs, anthropometric, hematic, biochemical, and tomographic variables) recorded upon hospital admission. Individual variables and system behavior were examined by descriptive statistics, differences between groups, principal component analysis, and network analysis. We show how topological network properties, particularly clustering coefficient, become disrupted in disease. Finally, anthropometric, metabolic, inflammatory, and pulmonary cluster interaction characterize the deceased young male group.

Elevated De Ritis Ratio Is Associated With Poor Prognosis in COVID-19: A Systematic Review and Meta-Analysis

Objective: This meta-analysis aims to assess whether elevated De Ritis ratio is associated with poor prognosis in patients with coronavirus 2019 (COVID-19). Methods: A systematic literature search was performed using PubMed, Embase, and EuropePMC databases up until September 17, 2021. De Ritis ratio is also known as Aspartate aminotransferase/alanine transaminase (AST/ALT) ratio. The main outcome was poor prognosis, a composite of mortality, severity, the need for ICU care, and intubation. The effect measure was odds ratios (ORs) and mean differences. We generated sensitivity and specificity, negative and positive likelihood ratio (NLR and PLR), diagnostic odds ratio (DOR), and area under curve (AUC). Results: There were eight studies with 4,606 patients. De Ritis ratio was elevated in 44% of the patients. Patients with poor prognosis have higher De Ritis ratio [mean difference 0.41 (0.31, 0.50), p < 0.001; I 2: 81.0%] and subgroup analysis showed that non-survivors also have higher De Ritis Ratio [mean difference 0.47 (0.46, 0.48), p < 0.001; I 2: 0%]. Elevated De Ritis ratio was associated with poor prognosis [OR 3.28 (2.39, 4.52), p < 0.001; I 2: 35.8%]. It has a sensitivity of 55% (36-73), specificity of 71% (52-85), PLR 1.9, NLR.63, DOR of 3 (2-4), and AUC of.67 (0.63-0.71). The posterior probability of poor prognosis was 38% if De Ritis is elevated, while 17% if De Ritis is not elevated. Conclusion: Elevated De Ritis ratio is associated with poor prognosis in patients with COVID-19. Systematic Review Registration: PROSPERO ID: CRD42020216634.