The role of biomarkers in diagnosis of COVID-19 - A systematic review. Life Sci. 2020;254:117788. [PMID: 32475810 PMCID: PMC7219356 DOI: 10.1016/j.lfs.2020.117788]

Predictive Factors of Oxygen Therapy Failure in Patients with COVID-19 in the Emergency Department

Background

Most patients with coronavirus disease 2019 (COVID-19) pneumonia require oxygen therapy, including standard oxygen therapy and a high-flow nasal cannula (HFNC), in the Emergency Department (ED), and some patients develop respiratory failure. In the COVID-19 pandemic, the intensive care unit (ICU) was overburdening. Therefore, prioritizing patients who require intensive care is important. This study aimed to find predictors and develop a model to predict patients at risk of requiring an invasive mechanical ventilator (IMV) in the ED.

Methods

We performed a retrospective, single-center, observational study. Patients aged ≥18 years who were diagnosed with COVID-19 and required oxygen therapy in the ED were enrolled. Cox regression and Harrell's C-statistic were used to identifying predictors of requiring IMV. The predictive model was developed by calculated coefficients and the ventilator-free survival probability. The predictive model was internally validated using the bootstrapping method.

Results

We enrolled 333 patients, and 97 (29.1%) had required IMV. Most 66 (68.0%) failure cases were initial oxygen therapy with HFNC. Respiratory rate-oxygenation (ROX) index, interleukin-6 (IL-6) concentrations ≥20 pg/mL, the SOFA (Sequential Organ Failure Assessment) score without a respiratory score, and the patient's age were independent risk factors of requiring IMV. These factors were used to develop the predictive model. ROX index and the predictive model at 2 hours showed a good performance to predict oxygen therapy failure; the c-statistic was 0.814 (95% confidence level [CI] 0.767-0.861) and 0.901 (95% CI 0.873-0.928), respectively. ROX index ≤5.1 and the predictive model score ≥8 indicated a high probability of requiring IMV.

Conclusion

The COVID-19 pandemic was limited resources, ROX index, IL-6 ≥20 pg/mL, the SOFA score without a respiratory score, and the patient's age can be used to predict oxygen therapy failure. Moreover, the predictive model is good at discriminating patients at risk of requiring IMV and close monitoring.

Risk Estimation of Severe COVID-19 Based on Initial Biomarker Assessment Across Racial and Ethnic Groups

CONTEXT.—

Disease courses in COVID-19 patients vary widely. Prediction of disease severity on initial diagnosis would aid appropriate therapy, but few studies include data from initial diagnosis.

OBJECTIVE.—

To develop predictive models of COVID-19 severity based on demographic, clinical, and laboratory data collected at initial patient contact after diagnosis of COVID-19.

DESIGN.—

We studied demographic data and clinical laboratory biomarkers at time of diagnosis, using backward logistic regression modeling to determine severe and mild outcomes. We used deidentified data from 14 147 patients who were diagnosed with COVID-19 by polymerase chain reaction SARS-CoV-2 testing at Montefiore Health System, from March 2020 to September 2021. We generated models predicting severe disease (death or more than 90 hospital days) versus mild disease (alive and fewer than 2 hospital days), starting with 58 variables, by backward stepwise logistic regression.

RESULTS.—

Of the 14 147 patients, including Whites, Blacks, and Hispanics, 2546 (18%) patients had severe outcomes and 3395 (24%) had mild outcomes. The final number of patients per model varied from 445 to 755 because not all patients had all available variables. Four models (inclusive, receiver operating characteristic, specific, and sensitive) were identified as proficient in predicting patient outcomes. The parameters that remained in all models were age, albumin, diastolic blood pressure, ferritin, lactic dehydrogenase, socioeconomic status, procalcitonin, B-type natriuretic peptide, and platelet count.

CONCLUSIONS.—

These findings suggest that the biomarkers found within the specific and sensitive models would be most useful to health care providers on their initial severity evaluation of COVID-19.

Glucose metabolism disorder: a potential accomplice of SARS-CoV-2

Globally, 265,713,467 confirmed cases of SARS-CoV-2 (CoV-2), including 5,260,888 deaths, have been reported by the WHO. It is important to study the mechanism of this infectious disease. A variety of evidences show the potential association between CoV-2 and glucose metabolism. Notably, people with type 2 diabetes mellitus (T2DM) and other metabolic complications were prone to have a higher risk of developing a more severe infection course than people who were metabolically normal. The correlations between glucose metabolism and CoV-2 progression have been widely revealed. This review will discuss the association between glucose metabolism disorders and CoV-2 progression, showing the promoting effect of diabetes and other diseases related to glucose metabolism disorders on the progression of CoV-2. We will further conclude the effects of key proteins and pathways in glucose metabolism regulation on CoV-2 progression and potential interventions by targeting glucose metabolism disorders for CoV-2 treatment. Therefore, this review will provide systematic insight into the treatment of CoV-2 from the perspective of glucose metabolism.

The Effects and Outcomes of the COVID-19 Pandemic on Pregnant Women in Kuwait

OBJECTIVES

This retrospective study reports the effects of COVID-19 among hospitalized pregnant women infected with COVID-19 and compares them to a control group.

METHODS

A multi-center retrospective cohort study, in which data of pregnant women with COVID-19, admitted to five different hospitals in Kuwait, were collected. Two groups were included, the first group were women with COVID-19 who were admitted between March 22, 2020 until December 31, December 2020. The second (control) group included pregnant women without COVID-19, who were admitted between March 1, 2019 and March 21, 2020. Data were collected using a standardized data collection survey, entered using Excel Software and analyzed using Analysis of Variance (ANOVA).

RESULTS

We compared 764 patients, with confirmed COVID-19 with 765 uninfected control subjects. The majority of pregnant women were diagnosed with COVID-19 infection during the third trimester. Tachycardia was the most frequent sign at admission and dry cough and fever were the most common presenting symptoms. ICU admission and respiratory failure were significantly higher in the COVID-19 group. Cesarean section (CS) rate was higher in the COVID-19 group, with obstetric and maternal complications being the most common indications in comparison to fetal indications in the control group.

CONCLUSIONS

This study was conducted to identify the effects of COVID-19 in pregnancy. Most patients were asymptomatic. The rate of normal vaginal delivery was higher in the control group. The main indications for Cesarean section in the COVID-19 group were obstetrical and maternal complications compared to fetal complications in the control group.  .

Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .

Predictive values of immune indicators on respiratory failure in the early phase of COVID-19 due to Delta and precedent variants

Background

Immune response indicators in the early phase of COVID-19, including interferon and neutralizing responses against SARS-CoV-2, which predict hypoxemia remains unclear.

Methods

This prospective observational study recruited patients hospitalized with COVID-19 (before emergence of omicron variant). As the immune indicators, we assessed the serum levels of IFN-I/III, IL-6, CXCL10 and VEGF, using an ELISA at within 5 days after the onset of symptoms, and serum neutralizing responses using a pseudovirus assay. We also assessed SARS-CoV-2 viral load by qPCR using nasal-swab specimens and serum, to assess the association of indicators and viral distribution.

Results

The study enrolled 117 patients with COVID-19, of which 28 patients developed hypoxemia. None received vaccine before admission. Serum IFN-I levels (IFN-α and IFN-β), IL-6, CXCL10, LDH and CRP were significantly higher in patients who developed hypoxemia. A significant association with nasopharyngeal viral load was observed only for IFN-I. The serum levels of IFN-α, IL-6, CXCL10 were significantly associated with the presence of RNAemia. Multivariable analysis showed higher odds ratio of IFN-α, with cut-off value of 107 pg/ml, in regard to hypoxemia (Odds ratio [OR]=17.5; 95% confidence interval [CI], 4.7-85; p<0.001), compared to those of IL-6, >17.9 pg/ml (OR=10.5; 95% CI, 2.9-46; p<0.001).

Conclusions

This study demonstrated that serum IFN-α levels in the early phase of SARS-CoV-2 infection strongly predict hypoxemic respiratory failure in a manner different from that of the other indicators including IL-6 or humoral immune response, and instead sensitively reflect innate immune response against SARS-CoV-2 invasion.

COVID-19 and Multiorgan Response: The Long-Term Impact

In late December 2019, severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) was discovered following a cluster of pneumonia cases in Wuhan, China. During the early stages of the COVID-19 pandemic in 2020, it was unclear how this virus would manifest into a multiorgan impacting disease. After over 750 million cases worldwide, it has become increasingly evident that SARS-CoV-2 is a complex multifaceted disease we continue to develop our understanding of the pathophysiology of COVID-19 and how it affects these systems has many theories, ranging from direct viral infection via ACE2 receptor binding, to indirect coagulation dysfunction, cytokine storm, and pathological activation of the complement system. Since the onset of the pandemic, disease presentation, management, and manifestation have changed significantly. This paper intends to expand on the long-term impacts of COVID-19 on the cardiovascular, respiratory, urinary, gastrointestinal, and vascular systems of the body and the changes in clinical management. It is evident that the pharmacological, nonpharmacological and psychological management of COVID-19 patients require clearer guidelines to improve the survival odds and long-term clinical outcomes of those presenting with severe disease.

Pathological Effects of SARS-CoV-2 Associated with Hematological Abnormalities

The SARS coronavirus 2 (SARS-CoV-2) is the causative agent of the 2019 coronavirus disease (COVID-19) pandemic that has claimed the lives of 6.9 million people and infected over 765 million. It has become a major worldwide health problem and is also known to cause abnormalities in various systems, including the hematologic system. COVID-19 infection primarily affects the lower respiratory tract and can lead to a cascade of events, including a cytokine storm, intravascular thrombosis, and subsequent complications such as arterial and venous thromboses. COVID-19 can cause thrombocytopenia, lymphopenia, and neutrophilia, which are associated with worse outcomes. Prophylactic anticoagulation is essential to prevent complications and death rates associated with the virus's effect on the coagulation system. It is crucial to recognize these complications early and promptly start therapeutic anticoagulation to improve patient outcomes. While rare, COVID-19-induced disseminated intravascular coagulation (DIC) exhibits some similarities to DIC induced by sepsis. Lactate dehydrogenase (LDH), D-dimer, ferritin, and C-reactive protein (CRP) biomarkers often increase in serious COVID-19 cases and poor prognosis. Understanding the pathophysiology of the disease and identifying risk factors for adverse outcomes is critical for effective management of COVID-19.

Virtual reality for assessing stereopsis performance and eye characteristics in Post-COVID

In 2019, we faced a pandemic due to the coronavirus disease (COVID-19), with millions of confirmed cases and reported deaths. Even in recovered patients, symptoms can be persistent over weeks, termed Post-COVID. In addition to common symptoms of fatigue, muscle weakness, and cognitive impairments, visual impairments have been reported. Automatic classification of COVID and Post-COVID is researched based on blood samples and radiation-based procedures, among others. However, a symptom-oriented assessment for visual impairments is still missing. Thus, we propose a Virtual Reality environment in which stereoscopic stimuli are displayed to test the patient's stereopsis performance. While performing the visual tasks, the eyes' gaze and pupil diameter are recorded. We collected data from 15 controls and 20 Post-COVID patients in a study. Therefrom, we extracted features of three main data groups, stereopsis performance, pupil diameter, and gaze behavior, and trained various classifiers. The Random Forest classifier achieved the best result with 71% accuracy. The recorded data support the classification result showing worse stereopsis performance and eye movement alterations in Post-COVID. There are limitations in the study design, comprising a small sample size and the use of an eye tracking system.

Association between the severity of periodontitis, COVID-19, C-reactive protein and interleukin-6 levels in hospitalized patients: a case‒control study

BACKGROUND

The COVID-19 pandemic is perhaps one of the most important events of the 21st century. Periodontitis is one of the most prevalent diseases of the oral cavity. Due to possible pathways of interaction between these two diseases, we investigated their association.

METHODS

The study population consisted of hospitalized patients with established COVID-19 diagnoses. Patients with mild to moderate COVID-19 were considered controls, while cases had severe to critical COVID-19. Periodontal examination and serum and saliva sampling were performed for each patient. Relevant medical data were extracted from patients' hospital files.

RESULTS

Of the enrolled patients, 122 were included in the statistical analyses. The severity of periodontitis was directly and significantly correlated with the severity of COVID-19 (P < 0.001). Patients with generalized stage III or IV periodontitis displayed an adjusted odds ratio of 4.24 for severe to critical COVID-19. Salivary and serum interleukin-6 levels were significantly associated with COVID-19 severity (P values: 0.002 and 0.004, respectively). Hospitalization length was significantly associated with the severity of periodontitis (P = 0.004). Clinical attachment level and gingival index were associated with increased odds for adverse events (P values: 0.004 and 0.035, respectively), while number of remaining teeth was associated with decreased odds for adverse events (P = 0.023).

CONCLUSIONS

This study showed that the severity of periodontitis is associated with the severity of COVID-19. This association might manifest as increased odds of adverse events. COVID-19 severity was associated with higher levels of salivary and serum interleukin-6 levels.

Cytokines, chemokines, and cells growth factors in patients with mild to moderate SARS-CoV-2 infection: A case-control study

Several biomarkers have been evaluated as predictors of severity or in directing the treatment of COVID-19, however there are no conclusive results. In this study, we evaluated serum levels of cytokines, chemokines, and cell growth factors in association with the pathobiology of mild to moderate SARS-CoV-2 infection. Serum levels of SARS-CoV-2 infected patients (n = 113) and flu symptoms individuals negative for SARS-CoV-2 (n = 58), tested by the RT-qPCR test-nasal swab were compared to healthy controls (n = 53). Results showed that the proinflammatory cytokines IL-1β, MCP-3, TNF-α, and G-CSF were increased in symptomatic patients and the cytokines IL-6 and IL-10 were associated with patients positive for SARS-CoV-2 when compared to healthy controls. Symptoms associated with COVID-19 were fever, anosmia, ageusia, and myalgia. For patients without SARS-CoV-2 infection, their major symptom was sore throat. The pathobiology of mild to moderate SARS-CoV-2 infection was associated with increasing proinflammatory cytokines and a pleiotropic IL-6 and anti-inflammatory IL-10 cytokines compared to healthy controls. Thus, knowledge about the pathophysiology and the involvement of biomarkers in the mild to moderate profile of the disease should be evaluated. Monitoring these biomarkers in patients with mild to moderate disease can help establish adequate treatment and prevention strategies for long-term COVID-19.

Thrombocytopenia as an important determinant of poor prognosis in patients with pyogenic liver abscess: a retrospective case series

BACKGROUND

Thrombocytopenia and poor prognosis in severe conditions are associated. However, the clinical significance of thrombocytopenia in pyogenic liver abscess (PLA) has not been evaluated.

OBJECTIVE

To evaluate the association between thrombocytopenia and the prognosis of patients with PLA.

METHODS

A consecutive case series of 458 adult patients with PLA hospitalized at Tongji Hospital (Wuhan, China) between October 2011 and June 2021 was included in this cross-sectional analysis. Patient data were compared between the thrombocytopenia and non-thrombocytopenia groups. Multivariate logistic regression, receiver operating characteristic (ROC) curve and propensity score -matched analyses (PSM) were performed.

RESULTS

Of the 458 patients with PLA, 94 (20.5%) developed thrombocytopenia, 19 (4.1%) developed septic shock, 14 (3.1%) were admitted to the ICU, and 15 (3.3%) died during hospitalization. Thrombocytopenia was independently associated with shock (95%CI = 3.529-57.944, P < 0.001), ICU admission (95%CI = 1.286-25.733, P = 0.022), and mortality (95%CI = 1.947-34.223, P = 0.004) in multivariate regression analysis. ROC analysis showed that thrombocytopenia may be an identified marker of shock [area under the ROC curve (AUC), 0.8119; cut-off, 92.50; P < 0.0001], ICU admission (AUC, 0.7484; cut-off, 82.50; P < 0.0015), and mortality (AUC, 0.7827; cut-off, 122.50; P < 0.002). These findings remained consistent across 86 pairs of patients analyzed for PSM analyses.

CONCLUSIONS

Thrombocytopenia is an independent risk factor for poor prognosis in PLA and patients may be more prone to adverse outcomes.

Distinct Features of Vascular Diseases in COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic was declared in early 2020 after several unexplained pneumonia cases were first reported in Wuhan, China, and subsequently in other parts of the world. Commonly, the disease comprises several clinical features, including high temperature, dry cough, shortness of breath, and hypoxia, associated with findings of interstitial pneumonia on chest X-ray and computer tomography. Nevertheless, severe forms of acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) are not limited to the respiratory tract but also may be extended to other systems, including the cardiovascular system. The bi-directional relationship between atherosclerosis and COVID-19 is accompanied by poor prognosis. The immune response hyperactivation due to SARS-CoV-2 infection causes an increased secretion of cytokines, endothelial dysfunction, and arterial stiffness, which promotes the development of atherosclerosis. Also, due to the COVID-19 pandemic, access to healthcare amenities was reduced, resulting in increased morbidity and mortality in patients at risk. Furthermore, as lockdown measures were largely adopted worldwide, the sedentary lifestyle and the increased consumption of processed nutrients or unhealthy food increased, and in the consequence, we might observe even 70% of overweight and obese population. Altogether, with the relatively low ratio of vaccinated people in many countries, and important health debt appeared, which is now and will be for next decade a large healthcare challenge. However, the experience gained in the COVID-19 pandemic and the new methods of patients' approaching have helped the medical system to overcome this crisis and will hopefully help in the case of new possible epidemics.

Predictors of pulmonary embolism in hospitalized patients with COVID-19

BACKGROUND

High venous thromboembolism (VTE) rates have been described in critically ill patients with COVID-19. We hypothesized that specific clinical characteristics may help differentiate hypoxic COVID-19 patients with and without a diagnosed pulmonary embolism (PE).

METHODS

We performed a retrospective observational case-control study of 158 consecutive patients hospitalized in one of four Mount Sinai Hospitals with COVID-19 between March 1 and May 8, 2020, who received a Chest CT Pulmonary Angiogram (CTA) to diagnose a PE. We analyzed demographic, clinical, laboratory, radiological, treatment characteristics, and outcomes in COVID-19 patients with and without PE.

RESULTS

92 patients were negative (CTA-), and 66 patients were positive for PE (CTA+). CTA + had a longer time from symptom onset to admission (7 days vs. 4 days, p = 0.05), higher admission biomarkers, notably D-dimer (6.87 vs. 1.59, p < 0.0001), troponin (0.015 vs. 0.01, p = 0.01), and peak D-dimer (9.26 vs. 3.8, p = 0.0008). Predictors of PE included time from symptom onset to admission (OR = 1.11, 95% CI 1.03-1.20, p = 0.008), and PESI score at the time of CTA (OR = 1.02, 95% CI 1.01-1.04, p = 0.008). Predictors of mortality included age (HR 1.13, 95% CI 1.04-1.22, p = 0.006), chronic anticoagulation (13.81, 95% CI 1.24-154, p = 0.03), and admission ferritin (1.001, 95% CI 1-1.001, p = 0.01).

CONCLUSIONS

In 158 hospitalized COVID-19 patients with respiratory failure evaluated for suspected PE, 40.8% patients had a positive CTA. We identified clinical predictors of PE and mortality from PE, which may help with early identification and reduction of PE-related mortality in patients with COVID-19.

Spotlight on contributory role of host immunogenetic profiling in SARS-CoV-2 infection: Susceptibility, severity, mortality, and vaccine effectiveness

BACKGROUND

The SARS-CoV-2 virus has spread continuously worldwide, characterized by various clinical symptoms. The immune system responds to SARS-CoV-2 infection by producing Abs and secreting cytokines. Recently, numerous studies have highlighted that immunogenetic factors perform a putative role in COVID-19 pathogenesis and implicate vaccination effectiveness.

AIM

This review summarizes the relevant articles and evaluates the significance of mutation and polymorphism in immune-related genes regarding susceptibility, severity, mortality, and vaccination effectiveness of COVID-19. Furthermore, the correlation between host immunogenetic and SARS-CoV-2 reinfection is discussed.

METHOD

A comprehensive search was conducted to identify relevant articles using five databases until January 2023, which resulted in 105 total articles.

KEY FINDINGS

Taken to gather this review summarized that: (a) there is a plausible correlation between immune-related genes and COVID-19 outcomes, (b) the HLAs, cytokines, chemokines, and other immune-related genes expression profiles can be a prognostic factor in COVID-19-infected patients, and (c) polymorphisms in immune-related genes have been associated with the effectiveness of vaccination.

SIGNIFICANCE

Regarding the importance of mutation and polymorphisms in immune-related genes in COVID-19 outcomes, modulating candidate genes is expected to help clinical decisions, patient outcomes management, and innovative therapeutic approach development. In addition, the manipulation of host immunogenetics is hypothesized to induce more robust cellular and humoral immune responses, effectively increase the efficacy of vaccines, and subsequently reduce the incidence rates of reinfection-associated COVID-19.

The effect of hyperbaric oxygen therapy on myocardial function in post-COVID-19 syndrome patients: a randomized controlled trial

Post-COVID-19 condition refers to a range of persisting physical, neurocognitive, and neuropsychological symptoms following SARS-CoV-2 infection. Recent evidence revealed that post-COVID-19 syndrome patients may suffer from cardiac dysfunction and are at increased risk for a broad range of cardiovascular disorders. This randomized, sham-control, double-blind trial evaluated the effect of hyperbaric oxygen therapy (HBOT) on the cardiac function of post-COVID-19 patients with ongoing symptoms for at least three months after confirmed infection. Sixty patients were randomized to receive 40 daily HBOT or sham sessions. They underwent echocardiography at baseline and 1-3 weeks after the last protocol session. Twenty-nine (48.3%) patients had reduced global longitudinal strain (GLS) at baseline. Of them, 13 (43.3%) and 16 (53.3%) were allocated to the sham and HBOT groups, respectively. Compared to the sham group, GLS significantly increased following HBOT (- 17.8 ± 1.1 to - 20.2 ± 1.0, p = 0.0001), with a significant group-by-time interaction (p = 0.041). In conclusion, post-COVID-19 syndrome patients despite normal EF often have subclinical left ventricular dysfunction that is characterized by mildly reduced GLS. HBOT promotes left ventricular systolic function recovery in patients suffering from post COVID-19 condition. Further studies are needed to optimize patient selection and evaluate long-term outcomes.This study was registered with ClinicalTrials.gov, number NCT04647656 on 01/12/2020.

Fucosylated N-glycans as early biomarkers of COVID-19 severity

Background

The pathological mechanisms of SARS-CoV-2 in humans remain unclear and the unpredictability of COVID-19 progression may be attributed to the absence of biomarkers that contribute to the prognosis of this disease. Therefore, the discovery of biomarkers is needed for reliable risk stratification and to identify patients who are more likely to progress to a critical stage.

Methods

Aiming to identify new biomarkers we analysed N-glycan traits in plasma from 196 patients with COVID-19. Samples were classified into three groups according to their severity (mild, severe and critical) and obtained at diagnosis (baseline) and at 4 weeks of follow-up (postdiagnosis), to evaluate their behaviour through disease progression. N-glycans were released with PNGase F and labelled with Rapifluor-MS, followed by their analysis by LC-MS/MS. The Simglycan structural identification tool and Glycostore database were employed to predict the structure of glycans.

Results

We determined that plasma from SARS-CoV-2-infected patients display different N-glycosylation profiles depending on the disease severity. Specifically, levels of fucosylation and galactosylation decreased with increasing severity and Fuc1Hex5HexNAc5 was identified as the most suitable biomarker to stratify patients at diagnosis and distinguish mild from critical outcomes.

Conclusion

In this study we explored the global plasma glycosignature, reflecting the inflammatory state of the organs during the infectious disease. Our findings show the promising potential of glycans as biomarkers of COVID-19 severity.

A novel scale based on biomarkers associated with COVID-19 severity can predict the need for hospitalization and intensive care, as well as enhanced probabilities for mortality

Prognostic scales may help to optimize the use of hospital resources, which may be of prime interest in the context of a fast spreading pandemics. Nonetheless, such tools are underdeveloped in the context of COVID-19. In the present article we asked whether accurate prognostic scales could be developed to optimize the use of hospital resources. We retrospectively studied 467 files of hospitalized patients after COVID-19. The odds ratios for 16 different biomarkers were calculated, those that were significantly associated were screened by a Pearson's correlation, and such index was used to establish the mathematical function for each marker. The scales to predict the need for hospitalization, intensive-care requirement and mortality had enhanced sensitivities (0.91 CI 0.87-0.94; 0.96 CI 0.94-0.98; 0.96 CI 0.94-0.98; all with p < 0.0001) and specificities (0.74 CI 0.62-0.83; 0.92 CI 0.87-0.96 and 0.91 CI 0.86-0.94; all with p < 0.0001). Interestingly, when a different population was assayed, these parameters did not change considerably. These results show a novel approach to establish the mathematical function of a marker in the development of highly sensitive prognostic tools, which in this case, may aid in the optimization of hospital resources. An online version of the three algorithms can be found at: http://benepachuca.no-ip.org/covid/index.php.

Clinical Features and Treatment Outcomes of COVID-19 Admissions in the Can Tho City Hospital of Tuberculosis and Respiratory Diseases, Vietnam: A Hospital-Based Observational Study

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, is among the top global health crises. As confirmed by the Vietnam Ministry of Health on 25th January 2023, Vietnam had a cumulative total of more than 11.52 million COVID-19 patients, including 10.61 million recoveries and 43,186 deaths.

OBJECTIVES

This study aimed to describe the clinical and subclinical characteristics, treatment progress, and outcomes of 310 cases of SARS-CoV-2 infection.

METHODS

A total of 310 patients with medical records of SARS-CoV-2 were admitted to Can Tho City Hospital of Tuberculosis and Lung Diseases, Can Tho city, Vietnam, between July 2021 and December 2021. Demographic and clinical data, including laboratory examinations, of all the patients were collected and analyzed.

RESULTS

The median duration of hospital stay was 16.4 ± 5.3 days. There were 243 (78.4%) patients with clinical symptoms of COVID-19 and 67 (21.6%) patients without clinical symptoms. The common symptoms included cough (71.6% of 310 patients), fever (35.4%), shortness of breath (22.6%), sore throat (21.4%), loss of smell/taste (15.6%), and diarrhea (14.4%). Regarding treatment outcomes, 92.3% of the patients were discharged from the hospital, 1.9% of the patients suffered a more severe illness and were transferred to a higher-level hospital, and 5.8% of the patients died. The RT-PCR results were negative in 55.2% of the patients, and 37.1% of the patients had positive RT-PCR results with Ct values of >30 on the discharge/transfer day. Multivariate logistic regression analyses showed that comorbidity and decreased blood pH were statistically significantly related to the treatment outcomes of the patients with COVID-19 (p < 0.05).

CONCLUSIONS

This study provides useful information (i.e., the clinical characteristics and treatment outcomes) on the COVID-19 pandemic in Vietnam during its biggest outbreak; the information may be used for reference and for making improvements in the handling of future health crises.

Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh

Background

Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection.

Methodology

In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models.

Result

The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection.

Conclusion

Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Clinical Characteristics and Serum Cytokines Profiling in Hospitalized COVID-19 Patients in Lebanon

Since its emergence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains a public health threat worldwide. While the majority of patients recover in 3-4 weeks, complications in severely ill patients, including acute respiratory distress syndrome, cardiac injury, thrombosis, and sepsis, can lead to death. Several biomarkers, in addition to cytokine release syndrome (CRS), have been associated with severe and fatal outcomes in coronavirus disease 2019 (COVID-19) patients. The aim of this study is to assess clinical characteristics and cytokines profiles in hospitalized COVID-19 patients in Lebanon. A total of 51 hospitalized COVID-19 patients were recruited between February 2021 and May 2022. Clinical data and sera were collected at two time points: at hospital presentation (T0) and last collected results during hospitalization (T1). Our results showed that 49% of participants were >60 years with males accounting for the majority (72.5%). Hypertension, followed by diabetes and dyslipidemia, were the most frequent comorbid conditions among study participants accounting for 56.9% and 31.4%, respectively. Chronic obstructive pulmonary disease (COPD) was the only significantly different comorbid condition between intensive care unit (ICU) and non-ICU patients. Our results also showed that the median level of D-dimer was significantly elevated among patients in ICU and those who died compared to non-ICU patients and those who survived. Moreover, C-reactive protein (CRP) levels were significantly higher at T0 compared to T1 in ICU and non-ICU patients. The median level of IL-12p70 was significantly higher in patients >60 years compared to those ≤60 years (p = 0.0209). Our data are in agreement with previous reports suggesting the importance of IL-6, CRP, and IL-12p70 in the assessment of risk of severe disease and mortality.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Admission Serum Magnesium Levels Is Associated with Short and Long-Term Clinical Outcomes in COVID-19 Patients

BACKGROUND

In the face of the global pandemic that the coronavirus disease 2019 (COVID-19) has created, readily available prognostic markers may be of great use.

OBJECTIVE

To evaluate the association between serum magnesium (sMg) levels on admission and clinical outcomes in hospitalized COVID-19 patients.

METHODS

We retrospectively analyzed all patients admitted to a single tertiary center with a primary de novo diagnosis of COVID-19. Patients were followed for a mean of 10 ± 7 months. Demographic, clinical and laboratory data were collected and compared between five groups of patients according to sMg quintiles on hospital admission.

RESULTS

The cohort included 1522 patients (58% male, 69 ± 17 years old). A low sMg level (1st quintile) was associated with higher rates of diabetes and steroid use, whereas a high sMg level (5th quintile) was associated with dyslipidemia, renal dysfunction, higher levels of inflammatory markers and stay in the intensive care unit. All-cause in-hospital and long-term mortality was higher in patients with both low and high sMg levels, compared with mid-range sMg levels (2nd, 3rd and 4th quintiles; 19% and 30% vs. 9.5%, 10.7% and 17.8% and 35% and 45.3% vs. 23%, 26.8% and 27.3% respectively; p < 0.001 for all). After adjusting for significant clinical parameters indicating severe disease and renal dysfunction, only low sMg state was independently associated with increased mortality (HR = 1.57, p < 0.001).

CONCLUSIONS

Both low and high sMg levels were associated with increased mortality in a large cohort of hospitalized COVID-19 patients. However, after correction for renal dysfunction and disease severity, only low sMg maintained its prognostic ability.

Effects of COVID-19 Disease on DNA Damage, Oxidative Stress and Immune Responses

Coronavirus disease 2019 (COVID-19) has posed a great threat to public health and has caused concern due to its fatal consequences over the last few years. Most people with COVID-19 show mild-to-moderate symptoms and recover without the need for special treatment, while others become seriously ill and need medical attention. Additionally, some serious outcomes, such as heart attacks and even stroke, have been later reported in patients who had recovered. There are limited studies on how SARS-CoV-2 infection affects some molecular pathways, including oxidative stress and DNA damage. In this study, we aimed to evaluate DNA damage, using the alkaline comet assay, and its relationship with oxidative stress and immune response parameters in COVID-19-positive patients. Our results show that DNA damage, oxidative stress parameters and cytokine levels significantly increased in SARS-CoV-2-positive patients when compared with healthy controls. The effects of SARS-CoV-2 infection on DNA damage, oxidative stress and immune responses may be crucial in the pathophysiology of the disease. It is suggested that the illumination of these pathways will contribute to the development of clinical treatments and to reduce adverse effects in the future.

Recombinant production, purification, and biochemical characterization of a novel L-lactate dehydrogenase from Bacillus cereus NRC1 and inhibition study of mangiferin

Lactate dehydrogenase (LDH, EC 1.1.1.27) is one of the vital glycolytic conditions, especially during anaerobic conditions. It is a significant diagnostic, prognostic, and monitoring biomarker parameter. A 950-bp DNA fragment containing the gene (LDH) encoding LDH was amplified from Bacillus cereus NRC1. The deduced amino acid sequence reveals that B. cereus LDH (Bc-LDH) is highly homologous to the LDHs of Bacillus organisms. All LDH enzymes have a significant degree of conservation in their active site and several additional domains with unidentified functions. The gene for LDH, which catalyzes lactate synthesis, was cloned, sequenced (accession number: LC706200.1), and expressed in Escherichia coli BL21 (DE3). In this investigation, Bc-LDH was purified to homogeneity with a specific activity of 22.7 units/mg protein and a molecular weight of 35 kDa. It works optimally at pH 8.0. The purified enzyme was inhibited by FeCl2, CuCl2, ZnCl2, and NiCl, whereas CoCl2 was found to boost the activity of Bc-LDH. The molecular docking of the 3D model of the Bc-LDH structure with a natural inhibitor, mangiferin, demonstrated excellent LDH inhibition, with a free binding energy of −10.2 kcal/mol. Moreover, mangiferin is a potent Bc-LDH inhibitor that inhibits Bc-LDH competitively and has one binding site with a Ki value of 0.075 mM. The LDH-mangiferin interaction exhibits a low RMSF value (&gt;1.5 Å), indicating a stable contact at the residues. This study will pave the way for more studies to improve the understanding of mangiferin, which could be considered an intriguing candidate for creating novel and improved LDH inhibitors.

Impact of COVID-19 on the cardiovascular health of women: a review by the Italian Society of Cardiology Working Group on 'gender cardiovascular diseases'

The coronavirus disease 19 (COVID-19), due to coronavirus 2 (SARS-CoV-2) infection, presents with an extremely heterogeneous spectrum of symptoms and signs. COVID-19 susceptibility and mortality show a significant sex imbalance, with men being more prone to infection and showing a higher rate of hospitalization and mortality than women. In particular, cardiovascular diseases (preexistent or arising upon infection) play a central role in COVID-19 outcomes, differently in men and women. This review will discuss the potential mechanisms accounting for sex/gender influence in vulnerability to COVID-19. Such variability can be ascribed to both sex-related biological factors and sex-related behavioural traits. Sex differences in cardiovascular disease and COVID-19 involve the endothelial dysfunction, the innate immune system and the renin-angiotensin system (RAS). Furthermore, the angiotensin-converting enzyme 2 (ACE2) is involved in disease pathogenesis in cardiovascular disease and COVID-19 and it shows hormone-dependent actions. The incidence of myocardial injury during COVID-19 is sex-dependent, predominantly in association with a greater degree of inflammation and coagulation disorders among men. Its pathogenesis is not fully elucidated, but the main theories foresee a direct role for the ACE2 receptor, the hyperimmune response and the RAS imbalance, which may also lead to isolated presentation of COVID-19-mediated myopericarditis. Moreover, the latest evidence on cardiovascular diseases and their relationship with COVID-19 during pregnancy will be discussed. Finally, authors will analyse the prevalence of the long-covid syndrome between the two sexes and its impact on the quality of life and cardiovascular health.

The differential expression of toll like receptors and RIG-1 correlates to the severity of infectious diseases

The toll like receptors (TLRs) and RIG-1 are proteins involved in the initial reaction of the innate immune system to infectious diseases and, thus, can provide much information to the surgical pathologist in terms of the molecular dynamics of the infection. The TLRs (TLR1, 2, 3, 4, 7, 8) and RIG-1 distribution as determined by immunohistochemistry was examined in the following diseases: human papillomavirus (n = 30 including 15 squamous intraepithelial lesions (SIL), 5 cancers, and 10 controls); molluscum contagiosum (n = 8 including 4 controls), SARS-CoV2 (n = 52 including 20 mild, 5 fatal, and 27 controls) and reovirus infection as oncolytic therapy. Mild, regressing infection (molluscum contagiosum, mild SARS-CoV2 and low grade SIL) each showed the same pattern: marked up regulation of at least three of the TLRs/RIG-1 with decreased expression of none compared to the controls. Severe infection (fatal SARS-CoV2, and cervical cancer) each showed marked decrease expression in at least three of the TLRs/RIG-1. We recently documented an equivalent marked decrease expression of the TLRs/RIG-1 in the placenta in fatal in utero infections. The reoviral infected tissues showed an overall pattern of marked increase expression of TLRs/RIG-1, consistent with a strong anti-viral response. Thus, the in situ testing of infectious diseases by a panel of these early infectious disease recognition proteins may allow the surgical pathologist to predict the outcome of the disease which, in turn, may assist in the understanding of the role of the TLRs/RIG-1 in determining the fate of a given infectious process.

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.

LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage

INTRODUCTION

The COVID-19 outbreak has put enormous pressure on the scientific community to detect infection rapidly, identify the status of disease severity, and provide an immediate vaccine/drug for the treatment. Relying on immunoassay and a real-time reverse transcription polymerase chain reaction (rRT-PCR) led to many false-negative and false-positive reports. Therefore, detecting biomarkers is an alternative and reliable approach for determining the infection, its severity, and disease progression. Recent advances in liquid chromatography and mass spectrometry (LC-MS/MS) enable the protein biomarkers even at low concentrations, thus facilitating clinicians to monitor the treatment in hospitals.

AREAS COVERED

This review highlights the role of LC-MS/MS in identifying protein biomarkers and discusses the clinically significant protein biomarkers such as Serum amyloid A, Interleukin-6, C-Reactive Protein, Lactate dehydrogenase, D-dimer, cardiac troponin, ferritin, Alanine transaminase, Aspartate transaminase, gelsolin and galectin-3-binding protein in COVID-19, and their analysis by LC-MS/MS in the early stage.

EXPERT OPINION

Clinical doctors monitor significant biomarkers to understand, stratify, and treat patients according to disease severity. Knowledge of clinically significant COVID-19 protein biomarkers is critical not only for COVID-19 caused by the coronavirus but also to prepare us for future pandemics of other diseases in detecting by LC-MS/MS at the early stages.

Serum Amyloid A Levels and Severity of COVID-19 in Children

OBJECTIVE

The aim of this study was to determine the availability of serum amyloid A (SAA) in the diagnosis of coronavirus disease 2019 (COVID-19), to asses disease severity and to predict hospitalization status.

METHODS

Between March, 2020 and March, 2021, a total of 80 children (40 cases with COVID-19 and 40 cases in healthy group) were included in this study. Patients were divided into two groups (mild and moderate/severe) to evaluate SAA levels in terms of clinical severity and also hospitalization status.

RESULTS

Comparisons between the two groups revealed that median SAA values were significantly higher in children with COVID-19 than in their healthy peers (21.45vs3.05 mg/L, P=0.002). There was no significant difference in the median serum SAA levels between mild and moderate/severe clinical disease (P=0.837). The SAA difference between the two groups with regards to hospitalization was not statistically significant (P=0.098).

CONCLUSIONS

Although SAA level was found to be higher in children with COVID 19 compared to healthy controls, the sensitivity of SAA for the disease was found to be low. In addition, there was no difference between the groups in terms of clinical severity.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

COVID-19 and Cardiovascular Diseases: A Literature Review From Pathogenesis to Diagnosis

The coronavirus disease 2019 (COVID-19) took the world by storm after the first case of COVID-19 emerged in China on December 8, 2019. The disease is generally considered as an infection of the respiratory system, but serious life-threatening myocardial injuries have been reported with this infection. Coronavirus can damage cardiac myocytes by entering the cell through angiotensin-converting enzyme 2 (ACE-2) receptor binding. Myocardial infarction, myocarditis, heart failure, cardiac arrhythmias, and Takotsubo cardiomyopathy are cardiac clinical manifestations commonly seen among patients affected by COVID-19. These cardiac pathologies are seen both during ongoing infection and post-infection. Elevated levels of myoglobin, troponin, creatine kinase-MB, plasma interleukin-6, lactate dehydrogenase (LDH), and N-terminal pro-b-type natriuretic peptide (NT-proBNP) have been found in COVID-19-associated myocardial injuries. The diagnostic modalities used in myocardial injuries due to COVID-19 include electrocardiography (ECG), cardiac magnetic resonance imaging (CMR), endomyocardial biopsy, echocardiography (Echo), and computerized tomography (CT-Scan). This literature review will discuss, in detail, the pathogenesis, clinical manifestations, and diagnosis of myocardial injuries due to COVID-19.

Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes

The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has been now directing the efforts towards the development of methods that can predict the progression of the disease. This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset have shown that chest X-ray scans and clinical information are useful to identify patients at risk of severe outcomes. While deep learning has shown superior performance in several medical fields, in most of the cases it considers unimodal data only. In this respect, when, which and how to fuse the different modalities is an open challenge in multimodal deep learning. To cope with these three questions here we present a novel approach optimizing the setup of a multimodal end-to-end model. It exploits Pareto multi-objective optimization working with a performance metric and the diversity score of multiple candidate unimodal neural networks to be fused. We test our method on the AIforCOVID dataset, attaining state-of-the-art results, not only outperforming the baseline performance but also being robust to external validation. Moreover, exploiting XAI algorithms we figure out a hierarchy among the modalities and we extract the features' intra-modality importance, enriching the trust on the predictions made by the model.

The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility

Background

Coronavirus disease 2019 (COVID-19) can cause a wide clinical spectrum, ranging from asymptomatic to severe disease with a high mortality rate. In view of the current pandemic and the increasing influx of patients into healthcare facilities, there is a need to identify simple and reliable tools for stratifying patients.

Objective

Study aimed to analyze whether hemogram-derived ratios (HDRs) can be used to identify patients with a risk of developing a severe clinical form and admission to hospital.

Methods

This cross-sectional and observational study included 500 patients with a confirmed diagnosis of COVID-19. Data on clinical features and laboratory parameters were collected from medical records and 13 HDRs were calculated and analyzed. Descriptive and inferential statistics were included in the analysis.

Results

Of the 500 patients, 43.8% had a severe form of the disease. Lymphocytopenia, monocytopenia, higher C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR) were found in severe patients (p < 0.05). Significantly higher neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), neutrophil-to-platelet ratio (NPR), neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and CRP-to-lymphocyte ratio (CRP/Ly) values were found in severe patients (p < 0.001). In addition, they have statistically significant prognostic potential (p < 0.001). The area under the curve (AUC) for CRP/Ly, dNLR, NLPR, NLR, and NPR were 0.693, 0.619, 0.619, 0.616, and 0.603, respectively. The sensitivity and specificity were 65.7% and 65.6% for CRP/Ly, 51.6% and 70.8 for dNLR, 61.6% and 57.3% for NLPR, 40.6% and 80.4% for NLR, and 48.8% and 69.1% for NPR.

Conclusion

The results of the study suggest that NLR, dNLR, CRP/Ly, NPR, and NLPR can be considered as potentially useful markers for stratifying patients with a severe form of the disease. HDRs derived from routine blood tests results should be included in common laboratory practice since they are readily available, easy to calculate, and inexpensive.

Correlation of Viral Load With the Biochemical and Hematological Profiles of COVID-19 Patients in Al Buraimi Hospital, Sultanate of Oman: A Cross-Sectional Study

Background Rapid identification of COVID-19 is crucial during the pandemic for the treatment and management of patients. Thus, early diagnosis of the disease using laboratory parameters can help in the rapid management of infected patients. This study aimed to investigate the correlation of viral load with hematological and biochemical parameters. This will ultimately help physicians to better understand the dynamics of this novel virus and aid in the management of patients. Methodology Laboratory confirmation of SARS-CoV-2 was performed by reverse transcription-polymerase chain reaction (RT-PCR) at the Al-Buraimi Hospital Laboratory Department using oropharyngeal and nasopharyngeal swabs. Positive cases were collected from July 2020 to January 2021 to be enrolled in this study. Results In this study, 264 confirmed positive patients were included initially and divided into three groups according to their cycle threshold (Ct) values obtained by PCR. Out of the total 264 patients, 174 (65.9%) were male, while 90 (34.1%) were female. However, the final sample was only 253 patients who met the inclusion criteria. With regard to Ct values, the study population was divided into the following three groups: Group 1 with Ct values of 9-20 (n = 87; 34.4%), group 2 with Ct values of 21-30 (n = 122; 47.8%), and group 3 with Ct values of 31-42 (n = 44; 17.4%). Conclusions We found that the proportion of male patients infected with COVID-19 was higher compared to females. In addition, the highest incidence was among patients in the age group of 51-70 years. The ferritin and alanine transaminase levels were highest in the initial stage of the infection (group 1) and decreased at the recovery stage. However, neutrophil, lymphocyte, alkaline phosphatase, and C-reactive protein showed an increasing trend from high viral load groups to low viral load groups. The values of the rest of the parameters, such as albumin, total bilirubin, lactate dehydrogenase, and D-dimer, were slightly higher in the initial stage of the infection but the decreasing trend was low; therefore, they were not considered helpful in predicting the disease severity reflected by their Ct value in the three different groups.

Risk of mortality in COVID-19 patients: a meta- and network analysis

Understanding the most relevant hematological/biochemical characteristics, pre-existing health conditions and complications in survivors and non-survivor will aid in predicting COVID-19 patient mortality, as well as intensive care unit (ICU) referral and death. A literature review was conducted for COVID-19 mortality in PubMed, Scopus, and various preprint servers (bioRxiv, medRxiv and SSRN), with 97 observational studies and preprints, consisting of survivor and non-survivor sub-populations. This meta/network analysis comprised 19,014 COVID-19 patients, consisting of 14,359 survivors and 4655 non-survivors. Meta and network analyses were performed using META-MAR V2.7.0 and PAST software. The study revealed that non-survivors of COVID-19 had elevated levels of gamma-glutamyl transferase and creatinine, as well as a higher number of neutrophils. Non-survivors had fewer lymphocytes and platelets, as well as lower hemoglobin and albumin concentrations. Age, hypertension, and cerebrovascular disease were shown to be the most influential risk factors among non-survivors. The most common complication among non-survivors was heart failure, followed by septic shock and respiratory failure. Platelet counts, creatinine, aspartate aminotransferase, albumin, and blood urea nitrogen levels were all linked to ICU admission. Hemoglobin levels preferred non-ICU patients. Lower levels of hemoglobin, lymphocytes, and albumin were associated with increased mortality in ICU patients. This meta-analysis showed that inexpensive and fast biochemical and hematological tests, as well as pre-existing conditions and complications, can be used to estimate the risk of mortality in COVID-19 patients.

Study to Determine a Prognostic Cutoff Values of the Coagulation Analyte D-dimer for ICU Admission among COVID-19 Patients

Introduction

The measured D-dimer levels in coronavirus disease-2019 (COVID-19) patients have no specific cutoff to find the progression of coagulopathy and severity.

Aim

This study aimed to determine prognostic cutoff values of D-dimer for intensive care unit (ICU) admission among COVID-19 patients.

Materials and methods

This cross-sectional study was conducted in Sree Balaji Medical College and Hospital, Chennai, during a period of 6 months. This study included 460 COVID-19-positive individuals.

Results

The mean age was 52.2 ± 12.53 years. Patients with mild illness have D-dimer value 461.8 ± 221, whereas moderate and severe COVID illness patients have D-dimer values of 1915.2 ± 699.9 and 7937.6 ± 2045.2, respectively. D-dimer cutoff value of 1036.9 is shown to be a prognostic cutoff value for COVID-19 patients admitted in the ICU with 99% sensitivity and 17% specificity. The area under curve (AUC) was considered excellent (AUC = 0.827, 95% Cl: 0.78-0.86, p-value < 0.0001) indicative of high sensitivity.

Conclusion

The D-dimer value of 1036.9 ng/mL was found to be the optimum cutoff for the patients to predict the severity of the COVID-19-positive patients admitted in the ICU.

How to cite this article

Anton MC, Shanthi B, Vasudevan E. Study to Determine a Prognostic Cutoff Values of the Coagulation Analyte D-dimer for ICU Admission among COVID-19 Patients. Indian J Crit Care Med 2023;27(2):135-138.

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

Nutritional biomarkers as predictors of clinical outcomes between COVID-19 severity groups in a tertiary government hospital

BACKGROUND

Malnutrition, as defined by the World Health Organization (WHO), includes undernutrition. In the Philippines, malnutrition is common due to several factors. The nutritional biomarkers can be used as an alternative indicator of dietary intake and nutritional status that can detect deficiencies in support to clinical management of COVID-19 patients. Apart from that, biomarkers are potentially useful for screening, clinical management, and prevention of serious complications of COVID-19 patients. Serum albumin, c-reactive protein (CRP), leukocyte count, lymphocyte count, blood urea nitrogen (BUN) to compute the nutritional prognostic indices (Prognostic nutritional index (PNI) score, BUN/Albumin ratio (BAR) and CRP/Albumin ratio (CAR).

OBJECTIVES

To compare the nutritional biomarkers of patients with COVID-19 based on case severity and determine the nutritional prognostic indices and associate to patients' clinical outcome during hospital stay.

METHODS

A single center, cross-sectional study was performed between June 2021 to August 2021 in a COVID-19 designated referral center in CALABARZON which comprised of 167 patients as part of the study. Clinicodemographic profile including patients' age, sex, co-morbidities, weight, height, laboratory, and serum biomarkers during the first 48 h of admission (serum albumin, leukocyte count, lymphocytes count, CRP, and BUN) were collated wherein the nutritional prognostic indices were computed and analyzed. Clinical outcomes of the patients were based on the patients' final diagnoses (recovered, length of hospital stay (LOHS), progression of severity and mortality).

RESULTS

167 non-critically ill COVID-19 patients were included in the analysis, of which 52.7% are admitted under the COVID-19 severe group and 47.3% for COVID-19 Mild/Moderate. Mostly are male (53.3%) with an average body mass index (BMI) of 24.26 (SD = 3.52) and have hypertension (55.1%) and diabetes (42.5%). Among the nutritional biomarker, albumin (p = 0.028; p = 0.004), total lymphocyte count (TLC) (p = 0.013; p = 0.005) and BUN (p = 0.001; p=<0.001) were shown to be significantly associated with progression of severity and mortality. Univariate logistic regression analysis showed the following nutritional prognostic score were correlated. (1.) progression of COVID-19 severity: PNI score (OR 0.928, 95% CI 0.886, 0.971, p=<0.001), and BAR value (OR 1.130, 95% CI 1.027, 1.242, p = 0.012); (2.) Mortality: PNI score (OR 0.926, 95% CI 0.878, 0.977, p = 0.005), CAR (OR 1.809, 95% CI 1.243, 2.632, p = 0.002), and BAR (OR 1.180, 95% CI 1.077, 1.292, p=<0.001). The average LOHS of COVID-19 patients was 12 days (SD = 7.72). However, it does not show any significant correlation between any nutritional biomarker, prognostic indices and LOHS.

CONCLUSION

This study demonstrated that deranged level of nutritional biomarkers can affect patient's COVID-19 severity and associated with patient's clinical outcome. Low albumin (≤2.5  g/dL), low level of TLC (≤1500 cells/mm3), elevated BUN (≥7.1 mmol/L) are associated with patient's case severity progression and mortality while low PNI score (<42.49), high BAR value (≥2.8) and CAR value (≥2.04) provided an important nutritional prognostic information and could predict mortality which can be a useful parameter in admission, hence it is recommended to screen all COVID-19 patients to reduce mortality.

Combination of WFDC2, CHI3L1, and KRT19 in Plasma Defines a Clinically Useful Molecular Phenotype Associated with Prognosis in Critically Ill COVID-19 Patients

BACKGROUND

COVID-19 is now a common disease, but its pathogenesis remains unknown. Blood circulating proteins reflect host defenses against COVID-19. We investigated whether evaluation of longitudinal blood proteomics for COVID-19 and merging with clinical information would allow elucidation of its pathogenesis and develop a useful clinical phenotype.

METHODS

To achieve the first goal (determining key proteins), we derived plasma proteins related to disease severity by using a first discovery cohort. We then assessed the association of the derived proteins with clinical outcome in a second discovery cohort. Finally, the candidates were validated by enzyme-linked immunosorbent assay in a validation cohort to determine key proteins. For the second goal (understanding the associations of the clinical phenotypes with 28-day mortality and clinical outcome), we assessed the associations between clinical phenotypes derived by latent cluster analysis with the key proteins and 28-day mortality and clinical outcome.

RESULTS

We identified four key proteins (WFDC2, GDF15, CHI3L1, and KRT19) involved in critical pathogenesis from the three different cohorts. These key proteins were related to the function of cell adhesion and not immune response. Considering the multicollinearity, three clinical phenotypes based on WFDC2, CHI3L1, and KRT19 were identified that were associated with mortality and clinical outcome.

CONCLUSION

The use of these easily measured key proteins offered new insight into the pathogenesis of COVID-19 and could be useful in a potential clinical application.

COVID-19 diagnostic approaches with an extensive focus on computed tomography in accurate diagnosis, prognosis, staging, and follow-up

Although a long time has passed since its outbreak, there is currently no specific treatment for COVID-19, and it seems that the most appropriate strategy to combat this pandemic is to identify and isolate infected individuals. Various clinical diagnosis methods such as molecular techniques, serologic assays, and imaging techniques have been developed to identify suspected patients. Although reverse transcription-quantitative PCR (RT-qPCR) has emerged as a reference standard method for diagnosis of SARS-CoV-2, the high rate of false-negative results and limited supplies to meet current demand are the main shortcoming of this technique. Based on a comprehensive literature review, imaging techniques, particularly computed tomography (CT), show an acceptable level of sensitivity in the diagnosis and follow-up of COVID-19. Indeed, because lung infection or pneumonia is a common complication of COVID-19, the chest CT scan can be an alternative testing method in the early diagnosis and treatment assessment of the disease. In this review, we summarize all the currently available frontline diagnostic tools for the detection of SARS-CoV-2-infected individuals and highlight the value of chest CT scan in the diagnosis, prognosis, staging, management, and follow-up of infected patients.

Sustained Cytotoxic Response of Peripheral Blood Mononuclear Cells from Unvaccinated Individuals Admitted to the ICU Due to Critical COVID-19 Is Essential to Avoid a Fatal Outcome

The main objective of this study was to determine the influence of the cytotoxic activity of peripheral blood mononuclear cells (PBMCs) on the outcome of unvaccinated individuals with critical COVID-19 admitted to the ICU. Blood samples from 23 individuals were collected upon admission and then every 2 weeks for 13 weeks until death (Exitus group) (n = 13) or discharge (Survival group) (n = 10). We did not find significant differences between groups in sociodemographic, clinical, or biochemical data that may influence the fatal outcome. However, direct cellular cytotoxicity of PBMCs from individuals of the Exitus group against pseudotyped SARS-CoV-2-infected Vero E6 cells was significantly reduced upon admission (-2.69-fold; p = 0.0234) and after 4 weeks at the ICU (-5.58-fold; p = 0.0290), in comparison with individuals who survived, and it did not improve during hospitalization. In vitro treatment with IL-15 of these cells did not restore an effective cytotoxicity at any time point until the fatal outcome, and an increased expression of immune exhaustion markers was observed in NKT, CD4+, and CD8+ T cells. However, IL-15 treatment of PBMCs from individuals of the Survival group significantly increased cytotoxicity at Week 4 (6.18-fold; p = 0.0303). Consequently, immunomodulatory treatments that may overcome immune exhaustion and induce sustained, efficient cytotoxic activity could be essential for survival during hospitalization due to critical COVID-19.

CO-RADS score and its correlation with clinical and laboratory parameters in patients with COVID-19

Background

Polymerase chain reaction (PCR) based SARS-CoV-2 RNA detection and serological antibody tests give a proof of Coronavirus Disease 2019 (COVID-19) infection. Several variables can influence the consequences of these tests. Inflammatory markers among mild and severe patients of COVID-19 showed dissimilarity in inflammatory markers while computed tomography (CT) in patients infected with COVID-19 used to evaluate infection severity. The aim of this study is to investigate the application of the COVID-19 Reporting and Data System (CO-RADS) classification in COVID-19 patients and its relation to clinical and laboratory finding.

Results

One hundred patients suspected to have COVID-19 infection were involved. Their age was 49.6 ± 14.7. Fever and cough were the frequent presenting symptoms. Patients with positive PCR were significantly associated with dyspnea and higher inflammatory markers. Lymphopenia had sensitivity of 63.6% and specificity of 91.7%. Combination of PCR and lymphopenia increased both sensitivity and specificity. CT findings in relation to PCR showed sensitivity of 90.5% and specificity of 25%. CO-RADS score showed positive correlation with age and inflammatory biomarkers and negative correlation with absolute lymphocyte count (ALC).

Conclusions

CT finding was more prominent in older patients with COVID-19 and associated with higher inflammatory biomarkers and lower ALC which were correlated with CO-RADS score. Patients with positive PCR had more symptoms and inflammatory marker. Combination of PCR with either lymphopenia or CT finding had more sensitivity, specificity and accuracy in diagnosis

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.

Analysis of Biochemical and Inflammatory Markers for Predicting COVID-19 Severity: Insights From a Tertiary Healthcare Institution of Eastern India

Background Coronavirus disease-19 (COVID-19) patients often deteriorate rapidly based on viral infection-related inflammation and the subsequent cytokine storm. The clinical symptoms were found to be inconsistent with laboratory findings. There is a need to develop biochemical severity score to closely monitor COVID-19 patients. Methods This study was conducted in the department of biochemistry at All India Institute of Medical Sciences (AIIMS) Bhubaneswar in collaboration with the intensive care unit. Laboratory data of 7,395 patients diagnosed with COVID-19 during the first three waves of the pandemic were analyzed. The serum high sensitivity high-sensitivity C-reactive protein (hs-CRP, immuno-turbidity method), lactate dehydrogenase (LDH, modified Wacker et al. method), and liver enzymes (kinetic-UV method) were estimated by fully automated chemistry analyzer. Serum ferritin and interleukin-6 (IL-6) were measured by one-step immunoassay using chemiluminescence technology. Three models were used in logistic regression to check for the predictive potential of biochemical parameters, and a COVID-19 biochemical severity score was calculated using a non-linear regression algorithm. Results The receiver operating characteristic curve found age, urea, uric acid, CRP, ferritin, IL6, and LDH with the highest odds of predicting ICU admission for COVID-19 patients. COVID-19 biochemical severity scores higher than 0.775 were highly predictive (odds ratio of 5.925) of ICU admission (AUC=0.740, p<0.001) as compared to any other individual parameter. For the validation, 30% of the total dataset was used as testing data (n=2095) with a sensitivity of 68.3%, specificity of 74.5%, and odds ratio of 6.304. Conclusion Age, urea, uric acid, ferritin, IL6, LDH, and CRP-based predictive probability algorithm calculating COVID-19 severity was found to be highly predictive of ICU admission for COVID-19 patients.

Impact of clinico-biochemical and inflammatory biomarkers on the immunogenicity and efficacy of SARS-CoV-2 adenoviral vaccine: a longitudinal study

Purpose

Due to a lack of effective antiviral treatment, several vaccines have been put forth to curb SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection and to reduce the mortality and morbidity rate by eliciting a protective immune response, primarily through virus-neutralizing antibodies specific for SARS-CoV-2 spike protein. This longitudinal study was designed to evaluate the vaccine effectiveness and immune response following the administration of adenoviral vaccine, COVISHIELD, in Indian population who were previously uninfected with SARS-CoV-2 and to reveal the effect of various sociodemographic, inflammatory and biochemical factors on antibody response.

Methods

Briefly, the total immunoglobulin G (IgG) against SARS-CoV-2 spike and nucleocapsid protein along with the immunological markers were estimated by chemiluminescent microparticle immunoassay (CMIA) technology. Biochemical parameters were estimated by spectrometry.

Results

A total of 348 subjects received two doses of COVISHIELD (224 males, 124 females). The mean age of the study subjects was 42.03 ± 13.54 years. Although both the doses of COVISHIELD against SARS-CoV-2 spike protein induced a robust immune response that lasted for months in all the subjects, the total IgG titer against SARS-CoV-2 spike protein was found significantly higher in subjects ≥50 years of age, and those with obesity, elevated triglycerides and elevated lactate dehydrogenase levels.

Conclusions

There is a definite effect of age and biochemical factors on the immunogenicity of COVISHIELD. An understanding of these factors could not only impact the design of vaccines and help improve vaccine immunogenicity and efficacy but also assist in decisions on vaccination schedules, in order to combat this deadly pandemic.