Predication of oxygen requirement in COVID-19 patients using dynamic change of inflammatory markers: CRP, hypertension, age, neutrophil and lymphocyte (CHANeL)

High plasma soluble CLEC-2 level predicts oxygen therapy requirement in patients with COVID-19

Predicting the clinical course and allocating limited medical resources appropriately is crucial during the COVID-19 pandemic. Platelets are involved in microthrombosis, a critical pathogenesis of COVID-19; however, the role of soluble CLEC-2 (sCLEC-2), a novel platelet activation marker, in predicting the prognosis of COVID-19 remains unexplored. We enrolled 108 patients with COVID-19, hospitalized between January 2021 and May 2022, to evaluate the clinical use of sCLEC-2 as a predictive marker. sCLEC-2 levels were measured in plasma sampled on admission, as well as interleukin-6, cell-free DNA, von Willebrand factor, and thrombomodulin. We retrospectively classified the patients into two groups - those who required oxygenation during hospitalization (oxygenated group) and those who did not (unoxygenated group) - and compared their clinical and laboratory characteristics. The correlation between sCLEC-2 and the other parameters was validated. The sCLEC-2 level was significantly higher in the oxygenated group (188.8 pg/mL vs. 296.1 pg/mL). Multivariate analysis identified high sCLEC-2 levels (odds ratio per 10 pg/mL:1.25) as an independent predictor of oxygen therapy requirement. sCLEC-2 was positively correlated with cell-free DNA, supporting the association between platelet activation and neutrophil extracellular traps. In conclusion, sCLEC-2 is a clinically valuable marker in predicting oxygen therapy requirements for patients with COVID-19.

GPX3 Variant Genotype Affects the Risk of Developing Severe Forms of COVID-19

In SARS-CoV-2 infection, excessive activation of the immune system intensively increases reactive oxygen species levels, causing harmful hyperinflammatory and oxidative state cumulative effects which may contribute to COVID-19 severity. Therefore, we assumed that antioxidant genetic profile, independently and complemented with laboratory markers, modulates COVID-19 severity. The study included 265 COVID-19 patients. Polymorphism of GSTM1, GSTT1, Nrf2 rs6721961, GSTM3 rs1332018, GPX3 rs8177412, GSTP1 rs1695, GSTO1 rs4925, GSTO2 rs156697, SOD2 rs4880 and GPX1 rs1050450 genes was determined with appropriate PCR-based methods. Inflammation (interleukin-6, CRP, fibrinogen, ferritin) and organ damage (urea, creatinine, transaminases and LDH) markers, complete blood count and coagulation status (d-dimer, fibrinogen) were measured. We found significant association for COVID-19 progression for patients with lymphocytes below 1.0 × 109/L (OR = 2.97, p = 0.002). Increased IL-6 and CRP were also associated with disease progression (OR = 8.52, p = 0.001, and OR = 10.97, p < 0.001, respectively), as well as elevated plasma AST and LDH (OR = 2.25, p = 0.021, and OR = 4.76, p < 0.001, respectively). Of all the examined polymorphisms, we found significant association with the risk of developing severe forms of COVID-19 for GPX3 rs8177412 variant genotype (OR = 2.42, p = 0.032). This finding could be of particular importance in the future, complementing other diagnostic tools for prediction of COVID-19 disease course.

Laboratory markers of severity across three COVID-19 outbreaks in Australia: has Omicron and vaccinations changed disease presentation?

COVID-19 has rapidly evolved since it was first discovered in December 2019. We aimed to retrospectively review our experience with COVID-19 infection across 2020-2022, focusing on differences in laboratory markers at presentation. Consecutive adult patients admitted to hospital with confirmed COVID-19 infection were retrospectively reviewed across three periods (29/3/2020-29/9/2020, 16/8/2021-13/10/2021 and 1/1/2022-31/1/2022), correlating with the lineages B.1.338, Delta (B.1.617.2) and Omicron (B.1.1.159), respectively. Laboratory findings of the first requested blood test within 24 h of presentation were recorded and correlated with patient outcome. The primary outcome was requirement for oxygen therapy at any point. Inflammatory markers, namely serum ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP) were significantly lower on presentation during 2022 compared to 2021, corresponding to a milder disease course. More than 80% of 2022 patients had received 2 or more vaccine doses and fully vaccinated patients displayed significantly lower inflammatory markers at presentation. Using 2022 data, a multivariate prediction model was constructed to predict for oxygen requirement, with c-statistic 0.86. Patients in 2022, corresponding with the Omicron variant, displayed a milder disease course, even in hospitalised patients, with the majority not requiring oxygen and lower inflammatory markers. We constructed a simple-to-use risk prediction model with c-statistic 0.86 which may identify individuals who can be safely managed as outpatients in the era of highly transmissible variants.

Evaluation of emerging inflammatory markers for predicting oxygen support requirement in COVID-19 patients

Coronavirus disease 2019 (COVID-19), a highly contagious pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly and remains a challenge to global public health. COVID-19 patients manifest various symptoms from mild to severe cases with poor clinical outcomes. Prognostic values of novel markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and C-reactive protein to lymphocyte ratio (CLR) calculated from routine laboratory parameters have recently been reported to predict severe cases; however, whether this investigation can guide oxygen therapy in COVID-19 patients remains unclear. In this study, we assessed the ability of these markers in screening and predicting types of oxygen therapy in COVID-19 patients. The retrospective data of 474 COVID-19 patients were categorized into mild and severe cases and grouped according to the types of oxygen therapy requirement, including noninvasive oxygen support, high-flow nasal cannula and invasive mechanical ventilator. Among the novel markers, the ROC curve analysis indicated a screening cutoff of CRP ≥ 30.0 mg/L, NLR ≥ 3.0 and CLR ≥ 25 in predicting the requirement of any type of oxygen support. The NLR and CLR with increasing cut-off values have discriminative power with high accuracy and specificity for more effective oxygen therapy with a high-flow nasal cannula (NLR ≥ 6.0 and CLR ≥ 60) and mechanical ventilator (NLR ≥ 8.0 and CLR ≥ 80). Our study thus identifies potential markers to differentiate the suitable management of oxygen therapy in COVID-19 patients at an earlier time for improving disease outcomes with limited respiratory support resources.

In-hospital Mortality Rates in SARS-CoV-2 Patients Treated with Enoxaparin and Heparin

OBJECTIVES

This study aimed to investigate in-hospital mortality rates in patients with coronavirus disease (COVID-19) according to enoxaparin and heparin use.

METHODS

This retrospective cohort study included 962 patients admitted to two hospitals in Kuwait with a confirmed diagnosis of COVID-19. Cumulative all-cause mortality rate was the primary outcome.

RESULTS

A total of 302 patients (males, 196 [64.9%]; mean age, 57.2 ± 14.6 years; mean body mass index, 29.8 ± 6.5 kg/m²) received anticoagulation therapy. Patients receiving anticoagulation treatment tended to have pneumonia (n = 275 [91.1%]) or acute respiratory distress syndrome (n = 106 [35.1%]), and high D-dimer levels (median [interquartile range]: 608 [523;707] ng/mL). The mortality rate in this group was high (n = 63 [20.9%]). Multivariable logistic regression, the Cox proportional hazards, and Kaplan-Meier models revealed that the use of therapeutic anticoagulation agents affected the risk of all-cause cumulative mortality.

CONCLUSION

Age, hypertension, pneumonia, therapeutic anticoagulation, and methylprednisolone use were found to be strong predictors of in-hospital mortality. In elderly hypertensive COVID-19 patients on therapeutic anticoagulation were found to have 2.3 times higher risk of in-hospital mortality. All cause in-hospital mortality rate in the therapeutic anticoagulation group was up to 21%.

Novel Scoring Systems to Predict the Need for Oxygenation and ICU Care, and Mortality in Hospitalized COVID-19 Patients: A Risk Stratification Tool

Introduction: A rapid surge in cases during the COVID-19 pandemic can overwhelm any healthcare system. It is imperative to triage patients who would require oxygen and ICU care, and predict mortality. Specific parameters at admission may help in identifying them.

Methodology: A prospective observational study was undertaken in a COVID-19 ward of a tertiary care center. All baseline clinical and laboratory data were captured. Patients were followed till death or discharge. Univariable and multivariable logistic regression was used to find predictors of the need for oxygen, need for ICU care, and mortality. Objective scoring systems were developed for the same using the predictors.

Results: The study included 209 patients. Disease severity was mild, moderate, and severe in 98 (46.9%), 74 (35.4%), and 37 (17.7%) patients, respectively. The neutrophil-to-lymphocyte ratio (NLR) >4 was a common independent predictor of the need for oxygen (p<0.001), need for ICU transfer (p=0.04), and mortality (p=0.06). Clinical risk scores were developed (10*c-reactive protein (CRP) + 14.8*NLR + 12*urea), (10*aspartate transaminase (AST) + 15.7*NLR + 14.28*CRP), (10*NLR + 10.1*creatinine) which, if ≥14.8, ≥25.7, ≥10.1 predicted need for oxygenation, need for ICU transfer and mortality with a sensitivity and specificity (81.6%, 70%), (73.3%, 75.7%), (61.1%, 75%), respectively. 

Conclusion: The NLR, CRP, urea, creatinine, and AST are independent predictors in identifying patients with poor outcomes. An objective scoring system can be used at the bedside for appropriate triaging of patients and utilization of resources.

HbA1c level may be a risk factor for oxygen therapy requirement in patients with coronavirus disease 2019

INTRODUCTION

Many clinical studies have identified significant predictors or risk factors for the severity or mortality of coronavirus disease 2019 (COVID-19) cases. However, there are very limited reports on the risk factors for requiring oxygen therapy during hospitalization. In particular, we sought to investigate whether plasma glucose and HbA1c levels could be risk factors for oxygen therapy requirement.

MATERIALS AND METHODS

A single-center, retrospective study was conducted of 131 COVID-19 patients hospitalized at Saitama Medical University Hospital between March 2020 and November 2020. To identify the risk factors for oxygen therapy requirement during hospitalization, a stepwise multivariate binary logistic regression analysis was performed using several clinical parameters commonly obtained on admission, including plasma glucose and HbA1c levels.

RESULTS

Of the 131 patients with COVID-19, 33.6% (44/131) received oxygen therapy during hospitalization. According to the logistic regression analysis, male sex (odds ratio [OR]: 8.76, 95% confidence interval [CI]: 1.65-46.5, P < 0.05), age (OR: 1.07, 95% CI: 1.02-1.12, P < 0.01), HbA1c levels (OR: 1.94, 95% CI: 1.09-3.44, P < 0.05), and serum C-reactive protein (CRP) levels (OR: 2.22, 95% CI: 1.54-3.20, P < 0.01) emerged as independent variables associated with oxygen therapy requirement during hospitalization.

CONCLUSIONS

In addition to male sex, age, and serum CRP levels, HbA1c levels on admission may serve as a risk factor for oxygen therapy requirement during the clinical course of COVID-19, irrespective of diabetes history and status. This may contribute to the efficient delegation of limited numbers of hospital beds to patients at risk for oxygen therapy requirement.

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient’s background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient’s background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.

Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.