Cardiovascular impact of emerging and Re-emerging Viruses: Pathophysiological mechanisms, diagnosis, and management with a pediatric focus

Emerging and re-emerging viruses are currently known as a major public health issue. These viruses can cause various human complications such as cardiovascular diseases (CVDs), both in adults and pediatric populations. Although various CVDs have been previously reported for emerging and re-emerging viruses, the mechanisms underlying these complications remain relatively unknown. Children and infants, while commonly developing less severe symptoms, may experience notable cardiovascular manifestations during infections caused by emerging and re-emerging viral infections, which can result in both acute and long-term complications. The present review aims to discuss various cardiovascular complications linked to emerging and re-emerging viral pathogens (including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), dengue virus (DENV), Zika virus (ZIKV), and chikungunya virus (CHIKV)) such as arrhythmias, myocarditis, vascular disorders, and thromboembolic conditions, particularly among the pediatric population. This review also addresses the potential mechanisms by which SARS-CoV-2, DENV, ZIKV, and CHIKV may impact the cardiovascular system and their clinical implications. Moreover, it discusses the diagnostic challenges for viral-caused cardiovascular disorders in children, owing to their common subtle or atypical manifestations. Finally, it addresses the present therapeutic specifically used for pediatric cases.

Hematological Markers in Thromboembolic Events: A Comparative Study of COVID-19 and Non-COVID-19 Hospitalized Patients

Background/Objectives: SARS-CoV-2 infection increases thrombotic events in hospitalized patients, especially those of greater severity. It has been associated with the cytokine storm and worsening renal and liver function, increased inflammatory markers, and altered coagulation markers. This study analyzes differences in inflammatory, hepatic, renal, and coagulation markers between hospitalized patients with and without COVID-19 who experienced thromboembolic events during the last three years of the pandemic. Methods: This single-center, retrospective observational study, with an inferential component and biomarker analysis, included 663 patients (600 without COVID-19, 63 with COVID-19) admitted between December 2022 and January 2023. Results: Patients with COVID-19 exhibited significantly higher mean glomerular filtration rate (GFR) (100.5 mL/min/1.73 m2; p < 0.01) and alanine aminotransferase (ALT) levels (33.0 IU/L; p < 0.01) compared to those without COVID-19. Ferritin levels were also significantly elevated in COVID-19 patients (441.1; p < 0.01), particularly those with severe disease. Conversely, troponin I was significantly higher in patients without COVID-19 (22.6 × 104 pg/mL; p < 0.001). Among COVID-19 patients, D-dimer levels were significantly higher in those not requiring intensive care unit (ICU) admission (9.0 × 103 ng/mL; p = 0.023). Multivariate analysis revealed a significant association between COVID-19 and sex. Conclusions: Overall, renal function did not differ significantly between COVID-19 and non-COVID-19 patients. However, renal function was better in patients admitted to the ICU, regardless of COVID-19 status. Troponin I levels were elevated in non-COVID-19 patients, while ferritin and ALT levels were higher in COVID-19 patients. D-dimer levels showed no significant difference between the two groups.

Early Identification of Severe COVID-19 Cases and the Need for ICU Care Based on Clinical and Laboratory Risk Factors

Background and objective Treatment in ICUs became extremely difficult due to the growing number of coronavirus disease 2019 (COVID-19) patients at the height of the pandemic. Consequently, prompt patient triage depends on the early categorization of severe cases in such scenarios. This study aimed to provide an evidence-based strategy to ensure the best use of resources by triaging patients based on objective risk factors. Methods This retrospective observational study comprised 500 inpatients (>age 18 years) who were hospitalized between March 20 and April 19, 2020, at the Khyber Teaching Hospital (KTH) and Hayatabad Medical Complex (HMC) in Peshawar, Pakistan. The clinical, laboratory, and radiological parameters were assessed. Real-time polymerase chain reaction (RT-PCR) findings were used to confirm the diagnosis of COVID-19. Results A total of 19 potential clinical and laboratory risk factors associated with ICU admissions were identified. At least one comorbidity among chronic lung disease, cardiovascular disease (CVD), and diabetes was the factor with the strongest association with ICU admission with a univariable odds ratio (OR) of over 27, followed by renal disease and other COVID-19 sequelae such as diarrhea, respiratory rate (>24 breaths/minute), and positive RT-PCR (vs. negative) with an univariable OR between 9 and 15. Furthermore, a multivariate logistic regression model was further developed with five risk factors, including comorbidity, presence of chronic lung disease, presence of diabetes, and RT-PCR (positive vs. negative), male sex (vs. female), and older age (65.0-80.5 years), suggesting a good fit of the model to the data shown by the area under the receiver operator characteristic curve (AUC) of 0.943 (95% CI: 0.917, 0.969). Additionally, a chest CT scan showed the typical COVID-19 pneumonia with pulmonary involvement of 30-40%, which was further evaluated by the COVID-19 Reporting and Data System (CO-RADS). The typical COVID-19 pneumonia was on a scale of four (15/25) or five (19/25) lung lesions. Conclusions Based on our findings, this approach could be used to screen the severe cases of COVID-19 patients and help them to be treated in ICUs on time while preventing others from unnecessarily using ICUs in the setting of limited medical resources, such as the outbreak of a pandemic.

Long COVID Syndrome: A Case-Control Study

BACKGROUND

Acute coronavirus disease 2019 (COVID-19) causes various cardiovascular complications. However, it is unknown if there are cardiovascular sequelae in the medium and long-term. The aim of this study was dual. Firstly, we wanted to investigate symptomatology and health-related quality of life (HRQoL) at medium-term follow-up (6 months post-COVID). Secondly, we wanted to assess whether history of COVID-19 and persistent shortness of breath at medium-term follow-up are associated with ongoing inflammation, endothelial dysfunction, and cardiac injury.

METHODS

A case-control study was performed. Virologically proven COVID-19 cases and age- and gender-matched controls were interviewed to assess symptoms and HRQoL. Biochemical tests were also performed.

RESULTS

The study comprised 174 cases and 75 controls. The mean age of the participants was 46.1±13.8 years. The median follow-up was 173.5 days (interquartile range 129-193.25 days). There was no significant difference in the demographics between cases and controls. At follow-up, cases had a higher frequency of shortness of breath, fatigue, arthralgia, abnormal taste of food (P <.001), and anosmia. Cases also exhibited worse scores in the general health and role physical domains of the Short Form Survey-36. High-sensitivity C-reactive protein (hsCRP) was significantly higher in the cases, and there was a positive correlation of hsCRP with time. Significant determinants of shortness of breath were age, female gender and white cell count, troponin I, and lower hemoglobin levels at follow-up.

CONCLUSION

Post-COVID-19 patients have persistent symptomatology at medium-term follow-up. Higher hsCRP in cases and the positive association of hsCRP with time suggest ongoing systemic inflammation in patients persisting for months after COVID-19.

COVID-19 is associated with cardiac structural and functional remodelling in healthy middle-aged and older individuals

BACKGROUND

Coronavirus disease 2019 (COVID-19) was declared a global pandemic in 2019. It remains uncertain to what extent COVID-19 effects the heart in heathy individuals. To evaluate the effect of the COVID-19 on cardiac structure and function in middle-aged and older individuals.

METHODS

A single-centre prospective observational study enroled a total of 124 participants (84 with history of COVID-19 [COVID-19 group] and 40 without a history of COVID-19 [non-COVID group]). All participants underwent echocardiography with speckle tracking to assess cardiac structure and function at rest and during peak exercise.

RESULTS

There were no differences in left and right ventricular diastolic function (p ≥ 0.05) between the COVID-19 and non-COVID-19 groups. Participants in COVID-19 group demonstrated higher left ventricular mass (130 ± 39.8 vs. 113 ± 27.2 g, p = 0.008) and relative wall thickness (0.38 ± 0.07 vs. 0.36 ± 0.13, p = 0.049). Left ventricular global longitudinal strain was reduced in the COVID-19 group at rest and at peak-exercise (rest: 18.3 ± 2.01 vs. 19.3 ± 1.53%, p = 0.004; peak exercise: 19.1 ± 2.20 vs. 21.0 ± 1.58%, p ≤ 0.001). However, no difference was seen in resting left ventricular ejection fraction (58 ± 2.89 vs. 59 ± 2.51%, p = 0.565) between groups. Right ventricular fractional area change was reduced in the COVID-19 group (p = 0.012).

CONCLUSION

Cardiac structural and functional remodelling was observed in middle-aged and older otherwise healthy individuals with a history of COVID-19.

Establishment and validation of a prognostic model based on common laboratory indicators for SARS-CoV-2 infection in Chinese population

BACKGROUND

At the beginning of December 2022, the Chinese government made major adjustments to the epidemic prevention and control measures. The epidemic infection data and laboratory makers for infected patients based on this period may help with the management and prognostication of COVID-19 patients.

METHODS

The COVID-19 patients hospitalized during December 2022 were enrolled. Logistic regression analysis was used to screen significant factors associated with mortality in patients with COVID-19. Candidate variables were screened by LASSO and stepwise logistic regression methods and were used to construct logistic regression as the prognostic model. The performance of the models was evaluated by discrimination, calibration, and net benefit.

RESULTS

888 patients were eligible, consisting of 715 survivors and 173 all-cause deaths. Factors significantly associated with mortality in COVID-19 patients were: lactate dehydrogenase (LDH), albumin (ALB), procalcitonin (PCT), age, smoking history, malignancy history, high density lipoprotein cholesterol (HDL-C), lactate, vaccine status and urea. 335 of the 888 eligible patients were defined as ICU cases. Seven predictors, including neutrophil to lymphocyte ratio, D-dimer, PCT, C-reactive protein, ALB, bicarbonate, and LDH, were finally selected to establish the prognostic model and generate a nomogram. The area under the curve of the receiver operating curve in the training and validation cohorts were respectively 0.842 and 0.853. In terms of calibration, predicted probabilities and observed proportions displayed high agreements. Decision curve analysis showed high clinical net benefit in the risk threshold of 0.10-0.85. A cutoff value of 81.220 was determined to predict the outcome of COVID-19 patients via this nomogram.

CONCLUSIONS

The laboratory model established in this study showed high discrimination, calibration, and net benefit. It may be used for early identification of severe patients with COVID-19.

Assessment of COVID-19 patients' outcome based on clinical profile, laboratory parameters, and clinical management: A retrospective observational study

Background

The coronavirus disease 2019 (COVID-19) pandemic has presented an unprecedented challenge to the global healthcare system, prompting an urgent need to understand the factors influencing patient outcomes. Critical to improving treatment protocols and reducing mortality rates is an in-depth assessment of the clinical profile, laboratory findings, and management strategies employed in treating COVID-19 patients. This research provides valuable insights that could influence future therapeutic approaches and public health strategies, ultimately aiming to reduce the morbidity and mortality associated with COVID-19. The study aimed to assess mortality predictors in patients admitted to the intensive care unit (ICU) due to COVID-19.

Methods

This study employed a retrospective approach, utilizing patient data from medical records. The collected data encompassed demographic and clinical profiles and details regarding the duration of admission and treatment. The evaluation focused on patients admitted to the ICU for COVID-19 between March 2020 and July 2021, with confirmation through real-time reverse transcriptase polymerase chain reaction (RT-PCR). Rigorous statistical analysis was conducted to compare outcomes between discharged and deceased patients.

Results

The study included a total of 202 ICU patients admitted for COVID-19. Among the cases, 147 (72.8%) were males and 55 (27.2%) were females. The mean age was 58.42 years, with a standard deviation of 15.59 years. Fever (92%) emerged as the most frequently encountered symptom, followed by cough (48.5%) and dyspnea (35%). Patients with underlying comorbidities exhibited a higher susceptibility to developing a severe or critical disease. Hypertension (n = 38) was identified as the most prevalent comorbidity, followed by type 2 diabetes mellitus (n = 36). Hypertension has demonstrated a significant association with disease outcomes. Body temperature, respiratory rate, oxygen saturation, and mechanical ventilation played substantial roles in patient outcomes.

Conclusion

The study revealed that underlying comorbidities and complications, such as acute respiratory distress syndrome (ARDS), were linked to significantly higher mortality rates among COVID-19 patients. Abnormal laboratory parameters also exhibited significant differences in the outcomes of ICU patients.

Unveiling Immunological and Hematological Markers in COVID-19: Insights from a Clinical Study

The COVID-19 pandemic has affected the global health system and economies largely. Therefore, knowledge about the clinical and laboratory profiles of patients with COVID-19 would help in the management and prognosis of the disease. The immunological and hematological indices have emerged as critical determinants for the severity of the disease and the prognosis; however, association with COVID-19 is clouded. The present study is aimed to characterize the immunological and hematological profiles of patients with COVID-19 in correlation with the disease severity. The study included 1,019 polymerase chain reaction (PCR)-confirmed patients with COVID-19 who were classified into serious and nonserious groups, considering severity criteria. Clinical laboratory investigations included hematological, biochemical, and immunological parameters regarding leukocyte counts, hemoglobin levels, and inflammatory markers. Our analysis of immunological and hematological differences between serious and nonserious patients with COVID-19 indicates that serious cases reflected elevated levels of pro-inflammatory markers such as lactate dehydrogenase, C-reactive protein (CRP), D-dimer, and ferritin, representing immune system dysregulation and systemic inflammation. Furthermore, in serious cases, discrepancies had also been noticed for many hematological parameters than nonserious ones, which also contained leukocyte count and hemoglobin level. Additionally, the CRP, D-dimer, blood urea nitrogen, alanine transaminase, and albumin levels could be independent predictors of COVID-19 severity by multivariate logistic regression analysis. Cutoff values for these biomarkers were defined by receiver operating characteristic curve analysis defining optimal parameters for the risk stratification and prognostication. The current investigation provides a comprehensive understanding of immunological and hematological correlation with COVID-19 severity, refining clinical decision-making and therapeutic interventions to improve patient outcomes.

Laboratory profiling of patients with long COVID in the Brazilian Amazon region: A cross-sectional study

The condition commonly referred to as long coronavirus disease (COVID) is characterized by the continuation of symptoms, sometimes accompanied by new symptoms that persist after the resolution of acute coronavirus disease 2019 (COVID-19). This observational cross-sectional study investigated 332 patients with long COVID in the Brazilian Amazon region. The study aimed to elucidate the systemic interactions associated with long COVID by compiling the findings related to hematological, coagulation, immunological, metabolic, hepatic, renal, and muscular profiles. Participants with long COVID were identified using rigorous criteria and underwent thorough laboratory examinations. The obtained data were subsequently analyzed, allowing for comparisons, associations, and correlations between findings within distinct groups in the study. Significant associations were observed between hospitalization during the acute phase and persistent laboratory abnormalities, suggesting a potential link between acute severity and long-term effects. Notably, individuals with long COVID for over a year exhibited elevated levels of monocytes, prolonged prothrombin times, reduced prothrombin activity, high levels of lactate dehydrogenase, and an increased frequency of qualitative C-reactive protein detection. This study provides valuable insights into the laboratory risk profile of patients with long COVID, particularly in the unique context of the Amazon region, where patients exhibit persistent symptoms lasting up to 1261 days.

Dynamic changes of hematological and hemostatic parameters in COVID-19 hospitalized patients: Potential role as severity biomarkers for the Chilean population

Background

COVID-19 is still a global health issue, there is limited evidence in South America regarding laboratory biomarkers associated with severe disease. The objective of our study was to identify hematological and hemostatic changes associated with severe COVID-19.

Methods

A total of 170 hospitalized patients with COVID19 were included in the study, defining their severity according to established criteria. Demographic, clinical, and laboratory (days 1, 3, 7, 15) data were obtained. We performed a statistical analysis, assuming significance with a value of p < 0.05. We analyzed the correlation between severity and biomarkers and established cut-off values for severe patients through ROC curves, estimating Odds Ratio associated with severe disease.

Results

Day 1 was observed significant differences between moderate vs severe patients for leukocytes (WBC), Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and D-dimer, establishing cut-off points for each of them. The markers we found associated to risk of severe disease were WBC (OR=3.2396; p = 0.0003), NLR (OR=5.7084; p < 0.0001), PLR (OR=4.4094; p < 0.0001), Neutrophil (OR=4.1193; p < 0.0001), D-dimer (OR=2.7827; p = 0.0124).

Conclusions

The results allow to establish basic laboratory biomarkers associated to severe disease, which could be used as prognostic markers.

The new framework of innovation biosphere for analysing innovation policies facing COVID-19 grand challenge

BACKGROUND

Facing global grand challenges such as coronavirus disease 2019 (COVID-19) require the participation of various actors in different sectors and systematically directing their innovative efforts. Considering the complexity, non-linear dynamics, and global extent of the COVID-19 challenge, developing and applying a multi-level, resilient, and systematic innovative framework is vital. Therefore, this study aims to apply the "innovation biosphere" framework inspired by ecological studies for examining and analysing the management dimensions of COVID-19.

METHODS

In this research, based on a deductive-inductive approach, the case study methodology is used. In accordance with this strategy, the innovation biosphere metaphor is considered as the basic framework (deductive approach) and subsequently the grand challenge of COVID-19 (inductive approach) is analysed at three levels: micro, meso and macro.

RESULTS

The research findings verify the correspondence between what happened in the management of COVID-19 and the proposed framework of innovation biosphere. In other words, the findings of the research show that the effect of global cooperation, role-playing and co-evolution of different actors and subsystems in facing the grand challenge of COVID-19 under an ecosystemic and eco-innovation approach has been evident. These events subsequently led to the cessation of the pandemic after about four years.

CONCLUSIONS

The main policy implications include the role of self-organization, the capability of global value networks, mission orientation, and co-evolution between actors as the contributions of innovation biosphere framework for managing grand health challenges, and global cohesion, oligopoly market, supporting local innovations, the critical role of basic research, and deregulation as the contributions of the COVID-19 case study for enhancing the innovation biosphere metaphor.

Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients

Amidst the global COVID-19 pandemic, the urgent need for timely and precise patient prognosis assessment underscores the significance of leveraging machine learning techniques. In this study, we present a novel predictive model centered on routine clinical laboratory test data to swiftly forecast patient survival outcomes upon admission. Our model integrates feature selection algorithms and binary classification algorithms, optimizing algorithmic selection through meticulous parameter control. Notably, we developed an algorithm coupling Lasso and SVM methodologies, achieving a remarkable area under the ROC curve of 0.9277 with the use of merely 8 clinical laboratory parameters collected upon admission. Our primary contribution lies in the utilization of straightforward laboratory parameters for prognostication, circumventing data processing intricacies, and furnishing clinicians with an expeditious and precise prognostic assessment tool.

Change in absolute neutrophil count after COVID-19 infection in patients using clozapine versus other antipsychotics

It was reported that patients who contracted COVID-19 while taking clozapine exhibited a distinct hematological response. However, the absence of control groups made it difficult to attribute it to clozapine. The changes in absolute neutrophil counts (ANCs) during the 4 weeks after COVID-19 infection were compared between the two groups of patients with severe mental illnesses (SMIs) (49 patients using clozapine and 54 using other antipsychotics) using generalized additive modeling. Although the pattern of a transient drop in ANC followed by gradual recovery could be demonstrated in both groups, it was more pronounced in the clozapine group ( P  = 0.00025). Nevertheless, overall ANC remained at a higher level in the clozapine group. The results suggested potential interaction between clozapine and COVID-19 at the level of hematological dynamics. However, it did not necessarily indicate that such interaction is inevitably harmful or dangerous. It was more of a concern that some patients using other antipsychotics exhibited decreased ANC, which did not easily recover. Traditionally, clinicians have been concerned about the worsening of hematological side effects in clozapine patients after COVID-19 infection. However, the obtained result highlighted the necessity of hematological monitoring in patients using any type of antipsychotics for SMIs.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study

BACKGROUND AND PURPOSE

The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses.

METHODS

A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values.

RESULTS

Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance.

CONCLUSION

This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

Role of renin-angiotensin system/angiotensin converting enzyme-2 mechanism and enhanced COVID-19 susceptibility in type 2 diabetes mellitus

Coronavirus disease 2019 (COVID-19) is a disease that caused a global pandemic and is caused by infection of severe acute respiratory syndrome coronavirus 2 virus. It has affected over 768 million people worldwide, resulting in approximately 6900000 deaths. High-risk groups, identified by the Centers for Disease Control and Prevention, include individuals with conditions like type 2 diabetes mellitus (T2DM), obesity, chronic lung disease, serious heart conditions, and chronic kidney disease. Research indicates that those with T2DM face a heightened susceptibility to COVID-19 and increased mortality compared to non-diabetic individuals. Examining the renin-angiotensin system (RAS), a vital regulator of blood pressure and pulmonary stability, reveals the significance of the angiotensin-converting enzyme (ACE) and ACE2 enzymes. ACE converts angiotensin-I to the vasoconstrictor angiotensin-II, while ACE2 counters this by converting angiotensin-II to angiotensin 1-7, a vasodilator. Reduced ACE2 expression, common in diabetes, intensifies RAS activity, contributing to conditions like inflammation and fibrosis. Although ACE inhibitors and angiotensin receptor blockers can be therapeutically beneficial by increasing ACE2 levels, concerns arise regarding the potential elevation of ACE2 receptors on cell membranes, potentially facilitating COVID-19 entry. This review explored the role of the RAS/ACE2 mechanism in amplifying severe acute respiratory syndrome coronavirus 2 infection and associated complications in T2DM. Potential treatment strategies, including recombinant human ACE2 therapy, broad-spectrum antiviral drugs, and epigenetic signature detection, are discussed as promising avenues in the battle against this pandemic.

Prevalence of Diabetes, Ketosis, and Ketoacidosis and Their Correlation With Mortality in Critical COVID-19 Patients: A Single-Center Retrospective Study

Aim We aimed to find out the prevalence of diabetes, ketosis, and ketoacidosis in coronavirus disease 2019 (COVID-19) critically ill patients and to explore the clinical impact of the development of ketosis and ketoacidosis on the outcome of COVID-19 critically ill patients and identify them as potential risk factors for these patients. Methods We collected data on COVID-19 patients admitted to the intensive care unit (ICU) retrospectively. The study population will be classified into two groups based on the presence of diabetes or ketosis. Results The study comprises data on 253 ICU patients admitted with COVID-19 pneumonia. Two hundred patients (79.05%) had diabetes or prediabetes on admission. Seventy-six patients (30%) presented with ketosis. Nine patients had progressed to diabetic ketoacidosis during their ICU stay. Concerning the outcome, among 150 patients who died (59.3%), there was significantly higher mortality among the ketotic patients (69.7%) compared to nonketotic patients (54.8%) with a P-value < 0.027. We noted that the peak blood glucose level during ICU stay was statistically significantly higher in nonsurvivors (mean 345 mg/dl) compared to survivors (mean 298 mg/dl) with a P-value of 0.006. Our data showed that peak serum levels of lactate, procalcitonin (PCT), C-reactive protein, white blood cells (WBC), D dimer, and lactate dehydrogenase strongly positively correlated to the length of ICU stay. We used the ROC curve (receiver operating characteristic curve) to assess the relation between many laboratories and mortality. We noted that uncontrolled hyperglycemia and other laboratory variables are significant predictors of mortality of COVID-19 patients (e.g., peak blood glucose (P = 0.004), PCT (P = 0.047), and P < 0.001 of other laboratories (e.g. lactate, PH, WBC, D dimer, ferritin). Conclusion We reported a high prevalence of diabetes and ketosis among COVID-19 patients admitted to the ICU. Ketosis is associated with an increased mortality risk. Uncontrolled hyperglycemia is a significant predictor of mortality in critically ill COVID-19 patients.

Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2

First described in December 2019 in Wuhan (China), COVID-19 disease rapidly spread worldwide, constituting the biggest pandemic in the last 100 years. Even if SARS-CoV-2, the agent responsible for COVID-19, is mainly associated with pulmonary injury, evidence is growing that this virus can affect many organs, including the heart and vascular endothelial cells, and cause haemostasis, CNS, and kidney and gastrointestinal tract abnormalities that can impact in the disease course and prognosis. In fact, COVID-19 may affect almost all the organs. Hence, SARS-CoV-2 is essentially a systemic infection that can present a large number of clinical manifestations, and it is variable in distribution and severity, which means it is potentially life-threatening. The goal of this comprehensive review paper in the series is to give an overview of non-pulmonary involvement in COVID-19, with a special focus on underlying pathophysiological mechanisms and clinical presentation.

Immunoregulatory effects of nanocurcumin in inflammatory milieu: Focus on COVID-19

The use of natural compounds, such as curcumin, to treat infections caused by bacteria, viruses, fungi, parasites, inflammatory diseases, and various types of cancer is an active and dynamic area of research. Curcumin has a long history of use in the food industry, and there is currently a growing interest in its therapeutic applications. Numerous clinical trials have consistently shown that curcumin, a polyphenolic compound, is safe and well-tolerated even at high doses. There is no toxicity limit. However, the clinical efficacy of curcumin has been limited by its constraints. However, scientific evidence indicates that the use of adjuvants and carriers, such as nanoparticles, exosomes, micelles, and liposomes, can help overcome this limitation. The properties, functions, and human benefits of using nanocurcumin are well-supported by scientific research. Recent evidence suggests that nanocurcumin may be a beneficial therapeutic modality due to its potential to decrease gene expression and secretion of specific inflammatory biomarkers involved in the cytokinestorm seen in severe COVID-19, as well as increase lymphocyte counts. Nanocurcumin has demonstrated the ability to improve clinical manifestations and modulate immune response and inflammation in various autoinflammatory diseases. Additionally, its efficacy, affordability, and safety make it a promising replacement for residual cancer cells after tumor removal. However, further studies are necessary to evaluate the safety and efficacy of nanocurcumin as a new therapeutic in clinical trials, including appropriate dosage, frequency, and duration.

Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples

Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3-4 h for results, with a notable false-negative rate of 15%-20%. This study proposes a stacked deep-learning approach for detecting COVID-19 in blood samples to distinguish uninfected individuals from those infected with the virus. Three stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms, are introduced to improve the detection quality of single deep learning models. To counter the class imbalance phenomenon in the training data, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is also implemented, resulting in increased specificity and sensitivity. The efficacy of the methods is assessed by utilizing blood samples obtained from hospitals in Brazil and Italy. Results revealed that the StackMax method greatly boosted the deep learning and traditional machine learning methods' capability to distinguish COVID-19-positive cases from normal cases, while SMOTE increased the specificity and sensitivity of the stacked models. Hypothesis testing is performed to determine if there is a significant statistical difference in the performance between the compared detection methods. Additionally, the significance of blood sample features in identifying COVID-19 is analyzed using the XGBoost (eXtreme Gradient Boosting) technique for feature importance identification. Overall, this methodology could potentially enhance the timely and precise identification of COVID-19 in blood samples.

Decreased phrenic nerve compound muscle action potential, inspiratory muscle strength, and exercise capacity after COVID-19

Objectives

Respiratory muscle weakness with higher ventilatory demands were reported even in patients recovering from only mild COVID-19 symptoms. Aim of this study was to assess the function of phrenic nerve and inspiratory respiratory muscle as well as cardiopulmonary exercise capacity in patients with prolonged exertional dyspnea after COVID-19 infection.

Methods

In this observational exploratory study, electrophysiological examination of the phrenic nerve, inspiratory muscle capacity as well as lung function test, 6-min walk distance (6MWD) and cardiopulmonary exercise test, have been performed in 22 patients post COVID-19 diagnosis (post-CoV).

Results

Exercise capacity (peak workload, Wpeak % predicted and peak oxygen uptake, VO2peak % predicted) were significantly affected in the post-CoV patients (61.8 ± 23.3 Wpeak % and 70.9 ± 22.3 VO2peak %). Maximum inspiratory pressure (MIP) was reduced (60.1 ± 25.5 mbar). In 6 of the 22 patients the electrophysiological response of the phrenic nerve was pathologically decreased (reduced compound muscle action potential, CMAP), while nerve conduction velocity (NCV) was normal, which corresponds to reduced muscle fiber contraction capacity. Positive relationships were demonstrated between 6MWD and MIP (rs = 0.88) as well as quality of life questionnaire (CRQ) and MIP (rs = 0.71) only in patients with reduced CMAP.

Discussion

Respiratory muscle weakness and exercise capacity is associated with reduced phrenic nerve CMAP without signs of neuropathy. This indicates that muscle fiber pathology of the diaphragm may be one pathophysiological factor for the prolonged respiratory symptoms after COVID-19 infections.

Innovation and Patenting Activities During COVID-19 and Advancement of Biochemical and Molecular Diagnosis in the Post- COVID-19 Era

The COVID-19 pandemic is to escalate globally and acquire new mutations quickly, so accurate diagnostic technologies play a vital role in controlling and understanding the epidemiology of the disease. A plethora of technologies acquires diagnosis of individuals and informs clinical management of COVID. Some important biochemical parameters for COVID diagnosis are the elevation of liver enzymes, creatinine, and nonspecific inflammatory markers such as C-reactive protein (CRP) and Interleukin 6 (IL-6). The main progression predictors are lymphopenia, elevated D-dimer, and hyperferritinemia, although it is also necessary to consider LDH, CPK, and troponin in the marker panel of diagnosis. Owing to the greater sensitivity and accuracy, molecular technologies such as conventional polymerase chain reaction (PCR), reverse transcription (RT)-PCR, nested PCR, loop-mediated isothermal amplification (LAMP), and xMAP technology have been extensively used for COVID diagnosis for some time now. To make so many diagnostics accessible to general people, many techniques may be exploited, including point of care (POC), also called bedside testing, which is developing as a portable promising tool in pathogen identification. Some other lateral flow assay (LFA)-centered techniques like SHERLOCK, CRISPR-Cas12a (AIOD-CRISPR), and FNCAS9 editor limited uniform detection assay (FELUDA), etc. have shown auspicious results in the rapid detection of pathogens. More recently, low-cost sequencing and advancements in big data management have resulted in a slow but steady rise of next-generation sequencing (NGS)-based approaches for diagnosis that have potential relevance for clinical purposes and may pave the way toward a better future. Due to the COVID-19 pandemic, various institutions provided free, specialized websites and tools to promote research and access to critically needed advanced solutions by alleviating research and analysis of data within a substantial body of scientific and patent literature regarding biochemical and molecular diagnosis published since January 2020. This circumstance is unquestionably unique and difficult for anyone using patent information to find pertinent disclosures at a specific date in a trustworthy manner.

Temporal trends in laboratory parameters in survivors and non‑survivors of critical COVID‑19 illness and the effect of dexamethasone treatment

Although coronavirus disease 2019 (COVID-19)-induced changes in laboratory parameters in patients upon admission have been well-documented, information on their temporal changes is limited. The present study describes the laboratory trends and the effect of dexamethasone treatment on these parameters, in patients with COVID-19 in the intensive care unit (ICU). Routine laboratory parameters, namely white blood cell (WBC), neutrophil, lymphocyte and platelet (PLT) counts, fibrinogen, C-reactive protein (CRP), lactate dehydrogenase (LDH) and albumin concentrations, were recorded upon admission to the ICU and, thereafter, on days 3, 5, 10, 15 and 21; these values were compared between survivors and non-survivors, as well as between those who were treated with dexamethasone and those who were not. Among the 733 patients in the ICU, (mean age, 65±13 years; 68% males; ICU mortality rate 45%; 76% of patients treated with dexamethasone), the WBC and neutrophil counts were persistently high in all patients, without significant differences over the first 15 days. Initially, low lymphocyte counts exhibited increasing trends, but remained higher in survivors compared to non-survivors (P=0.01). The neutrophil-to-lymphocyte ratio (NLR) was persistently elevated in all patients, although it was significantly higher in non-survivors compared to survivors (P<0.001). The PLT count was initially increased in all patients, although it was significantly decreased in non-survivors over time. The fibrinogen and LDH values remained similarly elevated in all patients. However, the increased levels of CRP, which did not differ between patients upon admission, further increased in non-survivors compared to survivors after day 10 (P=0.001). Declining trends in albumin levels over time, overall, with a significant decrease in non-survivors compared to survivors, were observed. Dexamethasone treatment significantly affected the temporal progression of fibrinogen and CRP in survivors and that of NLR in non-survivors. On the whole, the present study demonstrates that patients in the ICU with COVID-19 present persistently abnormal laboratory findings and significant differences in laboratory trends of NLR, CRP, PLT and albumin, but not in WBC and neutrophil count, and fibrinogen and LDH levels, between survivors and non-survivors. The temporal progression of fibrinogen, CRP and NLR is affected by dexamethasone treatment.

Death and Venous Thromboembolism Analyses among Hospitalized COVID-19-Positive Patients: A Multicenter Study

BACKGROUND

Coagulation disorders are frequently encountered among patients infected with coronavirus disease 2019 (COVID-19), especially among admitted patients with more severe symptoms. This study aims to determine the mortality rate and incidence and risk factors for venous thromboembolism (VTE) in hospitalized patients with COVID-19.

METHODS

This retrospective observational cohort study was conducted from March to July 2020 using a hospital database. All adult patients (>18 years old) with laboratory-confirmed COVID-19 were included. Laboratory data and the real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) for SARS-CoV-2 were obtained from medical records. The mortality rate and the incidence of VTE were established as study results. A multivariate logistic regression analysis was performed to identify predictors of thrombotic events.

RESULTS

rA total of 1024 confirmed COVID-19 patients were treated, of whom 110 (10.7%) were deceased and 58 patients (5.7%) developed VTE. Death occurred more frequently in patients older than 50 years and those admitted to the intensive care unit (ICU, 95%) and who received mechanical ventilation (62.7%). Multivariate analysis revealed that cancer patients were two times more likely to have VTE (adjusted odds ratio = 2.614; 95% CI = (1.048-6.519); p = 0.039). Other chronic diseases, such as diabetes, hypertension, and chronic kidney disease, were not associated with an increased risk of VTE.

CONCLUSIONS

One-tenth of hospitalized COVID-19 patients were deceased, and VTE was prevalent among patients with chronic conditions, such as cancer, despite anticoagulation therapy. Healthcare professionals should closely monitor individuals with a high risk of developing VTE to prevent unwanted complications.

Glycemic and lipid profile of patients with COVID-19: Impact on morbidity and mortality

INTRODUCTION

Recent studies have shown that patients with COVID-19, who have underlying diabetes mellitus, develop a severe clinical course and have increased mortality. Similarly, dyslipidemia is a common complication in patients with COVID-19, indicating that there may be a pathophysiological interaction between lipid metabolism and SARS-CoV-2. The main manifestation is hypocholesterolemia, which is associated with severe illness and unfavorable outcome.

AIM

The aim of the present study was to assess the impact of glucose and lipid levels on the progression and outcome of patients with COVID-19 infection.

MATERIAL AND METHODS

The study sample consisted of 301 patients who became ill with SARS-CoV-2 and hospitalized during the calendar year 2021. Data were collected from the patients themselves and from their clinical and laboratory follow-up.

RESULTS

A total of 301 patients were included in the study, 56.8 % were male and 82.4 % of patients were not vaccinated for SARS-CoV-2 at baseline. The mean age was 66.7 years and the body mass index was 28.8. More specifically 27.6 % had diabetes mellitus and 34.2 % reported known dyslipidemia. The comparison of the course of the disease with the laboratory data of patients, on the day of admission, found that death was more frequently observed in the elderly [83.0 ± 11.0 (p < 0.001)], in patients with abnormal glucose values [159.8 ± 51.4 (p = 0.039)], abnormal creatinine values (p < 0.001), low glomerular filtration rate (p < 0.001), lower total cholesterol values (p = 0.044), higher triglycerides values [124 ± 64,8 (p = 0,003)], abnormal CRP values (p < 0.001), cTnT (p = 0.001), D-dimers (p < 0.001), and SatO2 (p < 0.001).

CONCLUSION

The present study showed that death was more frequently observed in subjects with hyperglycemia and hypocholesterolemia, and other pathological findings. It is therefore evident that these patients should be a priority in the development of studies and guidelines.

It's in the blood: a review of the hematological system in SARS-CoV-2-associated COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global healthcare crisis. While SARS-CoV-2-associated COVID-19 affects primarily the respiratory system, patients with COVID-19 frequently develop extrapulmonary manifestations. Notably, changes in the hematological system, including lymphocytopenia, neutrophilia and significant abnormalities of hemostatic markers, were observed early in the pandemic. Hematological manifestations have since been recognized as important parameters in the pathophysiology of SARS-CoV-2 and in the management of patients with COVID-19. In this narrative review, we summarize the state-of-the-art regarding the hematological and hemostatic abnormalities observed in patients with SARS-CoV-2-associated COVID-19, as well as the current understanding of the hematological system in the pathophysiology of acute and chronic SARS-CoV-2-associated COVID-19.

Simple demographic, laboratory and chest radiograph variables can identify COVID-19 patients with pulmonary thromboembolism: a retrospective multicentre United Kingdom study

OBJECTIVES

To (1) identify discriminatory demographic, laboratory and initial CXR findings; (2) explore correlation between D-dimer and radiographic severity scores; and (3) assess accuracy of published D-dimer thresholds to identify pulmonary thromboembolism (PTE) in COVID-19 patients.

METHODS

Retrospective study including all COVID-19 patients admitted from 1st to 30th April 2020 meeting inclusion criteria from 25 (blinded) hospitals. Demographics, blood results, CXR and CTPA findings were compared between positive and negative PTE cohorts using uni- and multivariable logistic regression. Published D-dimer cut-offs were applied.

RESULTS

389 patients were included [median age 63; 237 males], of which 26.2% had a PTE. Significant univariable discriminators for PTE were peak D-dimer, sex, neutrophil count at the time of the D-dimer and at admission, abnormal CXR, and CXR zonal severity score. Only neutrophil count at peak D-dimer remained significant for predicting PTE on multivariable analysis (p = 0.008). When compared with the published literature, sensitivity for PTE were lower than those published at all cut-off values, however specificity at different cut-offs was variable.

CONCLUSIONS

In this multicentre COVID-19 cohort, univariable admission factors that could indicate pulmonary thromboembolism were male sex, high neutrophil count and abnormal CXR with a greater CXR zonal severity score. The accuracy levels of published D-dimer thresholds were not reproducible in our population.

ADVANCES IN KNOWLEDGE

This is a large multicentre study looking at the discriminatory value of simple variables to determine if a patient with COVID-19 has PTE or not, in addition to comparing D-dimer cut off values against published values.

Frequency of dysnatremia in patients admitted with COVID-19 infection and its prognostic implication

OBJECTIVE

We aimed to investigate the frequency of dysnatremia among patients admitted with COVID-19 infection and its association with inpatient mortality.

METHODS

This retrospective longitudinal study was conducted for 12 weeks. Serum sodium levels were recorded at admission, during the hospital stay, and within 48 hours of discharge or death. Logistic regression was used to determine the predictors of mortality.

RESULTS

This study included 574 patients (69.7% men, age 55.6 ± 14.4 years). On admission, mean sodium was 135.9 ± 6.4 mEq/L; 39% had hyponatremia and 4.7% had hypernatremia. During admission, hypernatremia increased to 18.8%; maximum sodium in patients who survived was 140.6 ± 5.0 mEq/L versus 151.0 ± 9.9 mEq/L in those who died. The final sodium was 145.4 ± 9.4 mEq/L in patients who died versus 137.7 ± 3.7 mEq/L in those who survived (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.13-1.32). Other predictors of mortality included ischemic heart disease (OR: 3.65, 95% CI: 1.39-9.61), acute kidney injury (OR: 6.07, 95% CI: 2.39-15.42), invasive ventilation (OR: 28.4, 95% CI: 11.14-72.40), and length of stay (OR: 0.91, 95% CI: 0.86-0.97).

CONCLUSION

Hypernatremia was frequently observed in patients who were critically ill and died and may be considered a predictor of mortality in COVID-19 infection.

The value of the neutrophil to lymphocyte ratio and PLT count for the diagnosis and prediction of COVID-19 severity

BACKGROUND

COVID-19 and influenza A can cause severe respiratory illness. Differentiating between the two diseases and identifying critically ill patients in times of epidemics become a challenge for frontline medical staff. We sought to investigate whether both diseases and their severity could be recognized by routine blood parameters.

METHODS

Our retrospective study analysed the clinical data and first-time routine blood parameters of 80 influenza A patients and 123 COVID-19 patients. COVID-19 patients were divided into three groups according to treatment modalities and outcomes: outpatient group, inpatient without invasive mechanical ventilation (IMV) group, and inpatient with IMV group. We used the Mann-Whitney and Kruskal-Wallis tests to analyze the differences in routine blood parameters between the two or three groups. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to assess the diagnostic accuracy.

RESULTS

Compared with outpatient influenza A patients, outpatient COVID-19 patients had a higher neutrophil to lymphocyte ratio (NLR) (6.63 vs 3.55). ROC analysis showed that the NLR had a high diagnostic value for differentiating COVID-19 from influenza A (AUC = 0.739). The best cut-off point of the NLR was 6.48, the diagnostic sensitivity was 0.523, and the specificity was 0.925. The median platelet (PLT) count in the different COVID-19 groups was as follows: outpatient group (189×109/L), inpatient without IMV group (161×109/L), and inpatient with IMV group (94×109/L). Multivariate logistic regression analysis found a significant association between PLT and treatment modality and outcome in COVID-19 patients (p<0.001).

CONCLUSIONS

NLR can be used as a potential biological indicator to distinguish COVID-19 and influenza A. Decreased PLT predicts the critical condition of COVID-19 patients and helps stratify the treatment of COVID-19 patients.

Effects of COVID-19 Lockdown on Nutritional, Functional and Frailty Biomarkers of People Living in Nursing Homes. A Prospective Study

BACKGROUND

Nursing home residences suffered a lockdown from the beginning of the COVID-19 pandemic. The present study prospectively evaluates the frailty, functional, and nutritional statuses of nursing home residents.

METHODS

Three hundred and one residents from three nursing homes took part in the study. Frailty status was measured using the FRAIL scale. Functional status was evaluated using the Barthel Index. Additionally, Short Physical Performance Batter (SPPB), SARC-F, hand-grip strength, and gait speed were also evaluated. Nutritional status was determined using the mini nutritional assessment test (MNA) and several anthropometrical and biochemical markers.

RESULTS

Mini Nutritional Assessment test scores decreased in 20% throughout the confinement (p < .001). Barthel index, SPPB and SARC-F scores also decreased, although to a lesser extent, reflecting a decrease in functional capacity. However, both anthropometric parameters, hand grip strength and gait speed, remained stable throughout confinement (p > .050 in all cases). Morning cortisol secretion significantly decreased by 40% from baseline to post-confinement. A significant reduction in daily cortisol variability was observed, which may suggest increased distress. Fifty-six residents died during the period of confinement (81.4% survival rate). Sex, FRAIL and Barthel Index scores were significant predictors of resident survival.

CONCLUSION

After the first COVID-19 blockade, several alterations in residents' frailty markers were observed, which were small and potentially reversible. However, many of the residents were pre-frail after the lockdown. This fact highlights the need for preventive strategies to reduce the impact of future social and physical stressors on these vulnerable individuals.

Solanum nigrum L. in COVID-19 and post-COVID complications: a propitious candidate

COVID-19 is caused by severe acute respiratory syndrome coronavirus-2, SARS-CoV-2. COVID-19 has changed the world scenario and caused mortality around the globe. Patients who recovered from COVID-19 have shown neurological, psychological, renal, cardiovascular, pulmonary, and hematological complications. In some patients, complications lasted more than 6 months. However, significantly less attention has been given to post-COVID complications. Currently available drugs are used to tackle the complications, but new interventions must address the problem. Phytochemicals from natural sources have been evaluated in recent times to cure or alleviate COVID-19 symptoms. An edible plant, Solanum nigrum, could be therapeutic in treating COVID-19 as the AYUSH ministry of India prescribes it during the pandemic. S. nigrum demonstrates anti-inflammatory, immunomodulatory, and antiviral action to treat the SARS-CoV-2 infection and its post-complications. Different parts of the plant represent a reduction in proinflammatory cytokines and prevent multi-organ failure by protecting various organs (liver, kidney, heart, neuro, and lung). The review proposes the possible role of the plant S. nigrum in managing the symptoms of COVID-19 and its post-COVID complications based on in silico docking and pharmacological studies. Further systematic and experimental studies are required to validate our hypothesis.

Assessing the Utility of Multiplexed Polymerase Chain Reaction in Detecting Microorganisms Causing Infections in Critically ill Patients

Early sepsis diagnosis is crucial for implementing adequate antibiotic therapy and for patient survival. This study investigated whether using multiplexed PCR for detecting microorganisms in critical septic patients affects initial antibiotic treatment and compared it to microbiological culture. It also explored scenarios where PCR is more effective in clinical practice. One hundred nineteen specimens (83 blood and 36 respiratory specimens) belonging to 93 patients were analyzed. Multiplexed PCR determinations were performed using the FA-BCID Panel (bioMérieux) for blood samples and the FA-Pneumo for respiratory samples. The mean turnaround times were 1.7 h for the FA-BCID and 1.5h for the FA-Pneumo. Conversely, they were 96.1 h for blood cultures and 72.3 h for respiratory cultures. FA-BCID showed a mean sensitivity of 97% and specificity of 100%. FA-Pneumo showed a sensitivity of 100% and specificity of 90%. However, the positive predictive value was only 39%. Discrepancies were common in polymicrobial samples. Based on the PCR results, initial empirical treatment should have been changed in 71% of patients with bloodstream infections and 61% with respiratory infections. We conclude that multiplexed PCR improves the response time in identifying germs with a high degree of coincidence for blood cultures and moderate for respiratory cultures. These results highlight the importance of PCR in choosing an appropriate antibiotic therapy.

Pattern-recognition receptors (PRRs) in SARS-CoV-2

Pattern recognition receptors (PRRs) are specific sensors that directly recognize various molecules derived from viral or bacterial pathogens, senescent cells, damaged cells, and apoptotic cells. These sensors act as a bridge between nonspecific and specific immunity in humans. PRRs in human innate immunity were classified into six types: toll-like receptors (TLR), C-type lectin receptors (CLRs), nucleotide-binding and oligomerization domain (NOD)-like receptors (NLRs), absent in melanoma 2 (AIM2)-like receptors (ALRs), retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), and cyclic GMP-AMP (cGAMP) synthase (cGAS). Numerous types of PRRs are responsible for recognizing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which is immensely effective in prompting interferon responses. Detection of SARS-CoV-2 infection by PRRs causes the initiation of an intracellular signaling cascade and subsequently the activation of various transcription factors that stimulate the production of cytokines, chemokines, and other immune-related factors. Therefore, it seems that PRRs are a promising potential therapeutic approach for combating SARS-CoV-2 infection and other microbial infections. In this review, we have introduced the current knowledge of various PRRs and related signaling pathways in response to SARS-CoV-2.

Outcomes of COVID-19 in immunocompromised patients: a single center experience

Malignancy, bone marrow and organ transplantation are associated with deficient and defective immune systems. Immunocompromised patients are at risk for severe and chronic complication of COVID-19 infection. However, the pathogenesis, diagnosis and management of this comorbidity remain to be elucidated. The purpose of the present study was to describe key aspects of COVID-19 infection in immunocompromised patients. In this retrospective, cross-sectional study, lab findings and outcomes of 418 COVID-19 patients with secondary immunodeficiency disorders admitted to Taleghani Hospital in Tehran, from March 2020 to September 2022 were investigated. Of the 418 immunocompromised patients with COVID-19, 236 (56.5%) ‌ were male and the median age of all studied patients was 56.6 ± 16.4 with range of 14 to 92 years. Totally, 198 (47.4%) of the patients died during hospitalization. Remdesivir was used for treatment of all patients. Mortality rate among patients admitted to ICU ward (86.8%) was significantly higher than non ICU admission (p < 0.001). The death rate in patients with CKD was substantially higher than other underlying disease (p < 0.001). In terms of laboratory finding, there was a significant relationship between ICU admission and worse outcome with WBC count (HR = 1.94, 95% CI = 1. 46-2.59, p < 0.001), PMN count (HR = 1.93, 95% CI = 1.452.56, p < 0.001), Hb (HR = 1.49, 95% CI = 1.042.13, p = 0.028), AST (HR = 2.55, 95% CI = 1.913.41, p < 0.001), BUN (HR = 2.56, 95% CI = 2.063.69, p < 0.001), Cr (HR = 2.63, 95% CI = 1.89-3.64, p < 0.001), Comorbidities index (HR = 1.71, 95% CI = 1.29-2.27, p < 0.001) and aging (HR = 1.91, 95% CI = 1.4-2.54, p < 0.001). Immunocompromised status increased the risk of mortality or worse outcome in patients diagnosed with COVID-19. Our finding showed outcome predicting markers in whom the waned immune system encounter new emerging disease and improved our understanding of COVID-19 virus behavior in immunocompromised individuals.

Temporal evolution of laboratory characteristics in patients critically ill with COVID‑19 admitted to the intensive care unit (Review)

In the context of coronavirus disease 2019 (COVID-19), laboratory medicine has played a crucial role in both diagnosis and severity assessment. Although the importance of baseline laboratory findings has been extensively reported, data regarding their evolution over the clinical course are limited. The aim of the present narrative review was to provide the dynamic changes of the routine laboratory variables reported in patients with severe COVID-19 over the course of their critical illness. A search was made of the literature for articles providing data on the time-course of routine laboratory tests in patients with severe COVID-19 during their stay in the intensive care unit (ICU). White blood cell, neutrophil and lymphocyte counts, neutrophil to lymphocyte ratio, platelet counts, as well as D-dimer, fibrinogen, C-reactive protein, lactate dehydrogenase and serum albumin levels were selected as disease characteristics and routine laboratory parameters. A total of 25 research articles reporting dynamic trends in the aforementioned laboratory parameters over the clinical course of severe COVID-19 were identified. During the follow-up period provided by each study, the majority of the laboratory values remained persistently abnormal in both survivors and non-survivors. Furthermore, in the majority of studies, the temporal trends of laboratory values distinctly differentiated patients between survivors and non-survivors. In conclusion, there are distinct temporal trends in selected routine laboratory parameters between survivors and non-survivors with severe COVID-19 admitted to the ICU, indicating their importance in the prognosis of clinical outcome.

Investigating the impact of metformin on severity of COVID-19 in patients with Type 2 diabetes mellitus: Focusing on laboratory findings

BACKGROUND

In the terrifying pandemic caused by SARS-CoV-2, diabetic patients exhibiting more severe outcomes and mortality rate is high among them. Based on recent studies, metformin as the most prescribed drug for T2DM treatment may improve severe outcomes in diabetic patients infected with SARS-CoV-2. On the other hand, abnormal laboratory findings can help to differentiate between the severe and non-severe form of COVID-19. According to the mentioned issues, the effect of metformin on severity of COVID-19 was examined in T2DM patients with SARS-CoV-2 infection.

METHODS

The study included 187 individuals diagnosed with COVID-19, 104 patients were diabetic and divided into two groups according to their anti-diabetic drugs: patients who were treated only with metformin and patients who were treated with other anti-diabetic drugs. The other participants were non-diabetic and diagnosed with COVID-19. Biochemical parameters were measured by routine laboratory methods before, during and after SARS-CoV-2 infection.

RESULTS

During infection, FBS, creatinine, ALT, AST, Ferritin and LDH were significantly lower in metformin users than non-users (p-value: .02, .01, .03, .04, .0009 and .01, respectively). Also, after recovery, there were statistically significant differences between metformin users and non-users with respect to most of the study parameters, except FBS, BUN and ALP (p-value: .51, .28 and .35, respectively).

CONCLUSION

Our result suggested that metformin might be associated with better outcomes in diabetic patients infected with SARS-CoV-2.

Procalcitonin-Guided Antibiotic Prescription in Patients With COVID-19: A Multicenter Observational Cohort Study

BACKGROUND

Despite the low rate of bacterial coinfection, antibiotics are very commonly prescribed in hospitalized patients with COVID-19.

RESEARCH QUESTION

Does the use of a procalcitonin (PCT)-guided antibiotic protocol safely reduce the use of antibiotics in patients with a COVID-19 infection?

STUDY DESIGN AND METHODS

In this multicenter cohort, three groups of patients with COVID-19 were compared in terms of antibiotic consumption, namely one group treated based on a PCT-algorithm in one hospital (n = 216) and two control groups, consisting of patients from the same hospital (n = 57) and of patients from three similar hospitals (n = 486) without PCT measurements during the same period. The primary end point was antibiotic prescription in the first week of admission.

RESULTS

Antibiotic prescription during the first 7 days was 26.8% in the PCT group, 43.9% in the non-PCT group in the same hospital, and 44.7% in the non-PCT group in other hospitals. Patients in the PCT group had lower odds of receiving antibiotics in the first 7 days of admission (OR, 0.33; 95% CI, 0.16-0.66 compared with the same hospital; OR, 0.42; 95% CI, 0.28-0.62 compared with the other hospitals). The proportion of patients receiving antibiotic prescription during the total admission was 35.2%, 43.9%, and 54.5%, respectively. The PCT group had lower odds of receiving antibiotics during the total admission only when compared with the other hospitals (OR, 0.23; 95% CI, 0.08-0.63). There were no significant differences in other secondary end points, except for readmission in the PCT group vs the other hospitals group.

INTERPRETATION

PCT-guided antibiotic prescription reduces antibiotic prescription rates in hospitalized patients with COVID-19, without major safety concerns.

Coronavirus Disease-2019 and Stroke: Pathophysiology and Management

Severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease-2019, has been associated with an increased risk for ischemic and hemorrhagic stroke. As data emerge about the underlying mechanisms, it is important to synthesize current knowledge to improve effective treatment options. In this review, we highlight the known pathophysiology, discuss the relationship between ischemic and hemorrhagic stroke, and address emerging implications for patient management. The information here is compiled to be a user-friendly, quick guide to help practitioners select management options for these patients.

Effects of COVID-19 disease on PAI-1 antigen and haematological parameters during disease management: A prospective cross-sectional study in a regional Hospital in Ghana

BACKGROUND

Individuals with COVID-19 experience thrombotic events probably due to the associated hypofibrinolysis resulting from the upregulation of plasminogen activator inhibitor-1 (PAI-1) antigen. This study evaluated plasma PAI-1 antigen levels and haematological parameters before treatment and after recovery from severe COVID-19 in Ghana.

MATERIALS AND METHODS

This cross-sectional study was conducted at Sunyani Regional Hospital, and recruited 51 patients who had RT-PCR-confirmed SARS-CoV-2. Participants' sociodemographic data and clinical characteristics were taken from the hospital records. Venous blood was taken before COVID-19 treatment commenced for FBC, PAI-1 and ferritin assays. FBC was assessed using an automated haematology analyzer, whilst plasma PAI-1 Ag and serum ferritin levels were assessed with sandwich ELISA. All the tests were repeated immediately after participants recovered from COVID-19.

RESULTS

Of the 51 participants recruited into the study, 78.4% (40) had non-severe COVID-19 whiles 21.6% (11) experienced a severe form of the disease. Severe COVID-19 participants had significantly lower haemoglobin (g/dL): 8.1 (7.3-8.4) vs 11.8 (11.0-12.5), p<0.001; RBC x 1012/L: 2.9 (2.6-3.1) vs 3.4 (3.1-4.3), p = 0.001; HCT%: 24.8 ± 2.6 vs 35.3 ± 6.7, p<0.001 and platelet x 109/L: 86.4 (62.2-91.8) vs 165.5 (115.1-210.3), p<0.001, compared with the non-severe COVID-19 group. But WBC x 109/L: 11.6 (9.9-14.2) vs 5.4 (3.7-6.6), p<0.001 and ferritin (ng/mL): 473.1 (428.3-496.0) vs 336.2 (249.9-386.5), p<0.001, were relatively higher in the participants with severe COVID-19 than the non-severe COVID-19 counterparts. Also, the severely ill SARS-CoV-2-infected participants had relatively higher plasma PAI-1 Ag levels (ng/mL): 131.1 (128.7-131.9) vs 101.3 (92.0-116.8), p<0.001, than those with the non-severe form of the disease. Participants had lower haemoglobin (g/dL): 11.4 (8.8-12.3 vs 12.4 (11.5-13.6), p<0.001; RBC x 1012/L: 3.3 (2.9-4.0) vs 4.3 (3.4-4.6), p = 0.001; absolute granulocyte count x 109/L: 2.3 ± 1.0 vs 4.6 ± 1.8, p<0.001, and platelet x 109/L: 135.0 (107.0-193.0) vs 229.0 (166.0-270.0), p<0.001 values at admission before treatment commenced, compared to when they recovered from the disease. Additionally, the median PAI-1 Ag (ng/mL): 89.6 (74.9-100.8) vs 103.1 (93.2-128.7), p<0.001 and ferritin (ng/mL): 242.2 (197.1-302.1) vs 362.3 (273.1-399.9), p<0.001 levels were reduced after a successful recovery from COVID-19 compared to the values at admission.

CONCLUSION

Plasma PAI-1 Ag level was higher among severe COVID-19 participants. The COVID-19-associated inflammation could affect red blood cell parameters and platelets. Successful recovery from COVID-19, with reduced inflammatory response as observed in the decline of serum ferritin levels restores the haematological parameters. Plasma levels of PAI-1 should be assessed during the management of severe COVID-19 in Ghana. This will enhance the early detection of probable thrombotic events and prompts Physicians to provide interventions to prevent thrombotic complications associated with COVID-19.

Ovarian Cancer Biomarkers in the COVID-19 Era

Ovarian Cancer (OC) diagnosis is entrusted to CA125 and HE4. Since the latter has been found increased in COVID-19 patients, in this study, we aimed to evaluate the influence of SARS-CoV-2 infection on OC biomarkers. HE4 values above the cut-off were observed in 65% of OC patients and in 48% of SARS-CoV-2-positive patients (not oncologic patients), whereas CA125 values above the cut-off were observed in 71% of OC patients and in 11% of SARS-CoV-2 patients. Hence, by dividing the HE4 levels into quartiles, we can state that altered levels of HE4 in COVID-19 patients were mostly detectable in quartile I (151-300 pmol/L), while altered levels in OC patients were mostly clustered in quartile III (>600, pmol/L). In light of these observations, in order to better discriminate women with ovarian cancer versus those with COVID-19, we established a possible HE4 cut-off of 328 pmol/L by means of a ROC curve. These results demonstrate that the reliability of HE4 as a biomarker in ovarian cancer remains unchanged, despite COVID-19 interference; moreover, it is important for a proper diagnosis that whether the patient has a recent history of SARS-CoV-2 infection is determined.

New Generation of Systemic Inflammatory Markers for Respiratory Syncytial Virus Infection in Children

AIM

This study evaluated the relationship between the systemic immune-inflammatory index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) with clinical findings of respiratory syncytial virus (RSV) infection among children with a diagnosis of lower respiratory tract infection (LRTI).

METHODS

The study was conducted between 1 January 2020 and 1 January 2022 in a pediatric clinic. This retrospective study included 286 consecutive patients between 0 and 12 years of age, 138 of whom were RSV (+) (48.25%) and 148 of whom were RSV (-) (51.75%). The detection of the RSV antigen was carried out using the chromatographic immunoassay method on nasopharyngeal swabbing samples.

RESULTS

CRP content was significantly higher in patients with RSV (+) than in children with RSV (-), while NLR, PLR, and SII, as inflammatory parameters, were significantly lower. Fever, coughs, and wheezing were the most common symptoms in the RSV (+) groups (100%). RSV infections were the highest in November, October, and December, in that order. The AUC was statistically significant for parameters in all groups. AUC values were 0.841 (95%: 0.765-0.917) for leukocytes, 0.703 (95%: 0.618-0.788) for lymphocytes, 0.869 (95%: 0.800-0.937) for CRP, 0.706 (95%: 0.636-0.776) for NLR, 0.779 (95%: 0.722-0.836) for PLR, and 0.705 (95%: 0.633-0.776) for SII. CRP was found to have both high sensitivity (80.4%) and high specificity (82.4%) among all parameters. While the ROC analysis results showed similar results for children under two years old, only CRP and NLR were statistically significant in this group.

CONCLUSION

CRP performed better than other blood parameters as a marker. The NLR, PLR, and SII index were significantly lower in LRTI patients with RSV (+) than in those with RSV (-), which implies a higher grade of inflammation. If the cause of the disease can be determined by this method, disease management will be easier, and unnecessary antibiotics could be avoided.

[Risk of thrombosis recurrence among patients with COVID-19- and surgery-associated venous thromboembolism]

Introduction

Recent surgery is a well-known major transient risk factor for venous thromboembolism (VTE) due to the low risk of VTE recurrence after anticoagulation is discontinued. On the other hand, the risk of VTE recurrence among patients with COVID-19-associated VTE is unknown. This study aimed to compare the risk of VTE recurrence between patients with COVID-19- and surgery-associated VTE.

Methods

A prospective observational single-center study was performed including consecutive patients diagnosed with VTE in a tertiary hospital from January 2020 to May 2022 and followed up for at least 90 days. Baseline characteristics, clinical presentation, and outcomes were assessed. The incidence of VTE recurrence, bleeding, and death was compared between both groups.

Results

A total of 344 patients were included in the study: 111 patients with surgery-associated VTE and 233 patients with COVID-19-associated VTE. Patients with COVID-19-associated VTE were more frequently men (65.7% vs 48.6%, p = 0.003). VTE recurrence was 3% among COVID-19 patients and 5.4% among surgical patients, with no significant differences (p = 0.364). The incidence rate of recurrent VTE was 1.25 per 1000 person-months in COVID-19 patients and 2.29 person-months in surgical patients, without significant differences (p = 0.29). In the multivariate analysis, COVID-19 was associated with higher mortality (HR 2.34; 95% CI 1.19-4.58), but not with a higher risk of recurrence (HR 0.52; 95% CI 0.17-1.61). No differences were found in recurrence in the multivariate competing risk analysis (SHR 0.82; 95% CI 0.40 - 2.05).

Conclusions

In patients with COVID-19 and surgery-associated VTE, the risk of recurrence was low, with no differences between both groups.

Analysis of Microvasculature in Children Recovered from COVID-19 Using Swept-Source OCT/OCTA Technology

PURPOSE

To analyze structural and vascular changes of the retina and choroid in pediatric patients with coronavirus disease 2019 (COVID-19) using optical coherence tomography (OCT)/OCT angiography (OCTA).

METHODS

This comparative cross-sectional study consists of the COVID-19 group including pediatric COVID-19 patients and the control group including healthy children. Vessel density (VD), central macular thickness,, and choroidal thickness (ChT) measurements were performed using swept-source OCT/OCTA 12 weeks after the recovery from COVID-19.

RESULTS

The mean VD measurements in the central fovea and nasal quadrants of all three retinal layers and choriocapillaris showed insignificantly lower values in the COVID-19 group when compared to the control group (0:002 < p < 0:05 for all). Similar to VD measurements, insignificant lower ChT measurements were obtained in the central fovea and nasal points in the COVID-19 group.

CONCLUSION

OCTA can be used as a non-invasive and valid biomarker in the assessment of early microvascular dysfunction associated with COVID-19.

Common Prognostic Biomarkers and Outcomes in Patients with COVID-19 Infection in Saudi Arabia

Background: COVID-19 is a respiratory disease that eventually became a pandemic, with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently been reported to help in early prediction and managing severe cases, which might improve outcomes. Our study aimed to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in COVID-19 patients and its effect on the outcome. Methods: We have collected retrospective data on socio-demographics, medical history, biomarkers, and disease outcomes from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID-19 in our cohort. The presence of abnormal inflammatory biomarkers (D-dimer, CRP, troponin, LDH, ferritin, and t white blood cells) was significantly associated with unstable COVID-19 disease. In addition, patients with evidence of severe respiratory disease, particularly those who required mechanical ventilation, had higher biomarkers when compared to those with stable respiratory conditions (p < 0.001). Conclusion: Identifying biomarkers predicts outcomes for COVID-19 patients and may significantly help in their management.

In the SARS-CoV-2 Pandora Pandemic: Can the Stance of Premorbid Intestinal Innate Immune System as Measured by Fecal Adnab-9 Binding of p87:Blood Ferritin, Yielding the FERAD Ratio, Predict COVID-19 Susceptibility and Survival in a Prospective Population Database?

SARS-CoV-2 severity predictions are feasible, though individual susceptibility is not. The latter prediction allows for planning vaccination strategies and the quarantine of vulnerable targets. Ironically, the innate immune response (InImS) is both an antiviral defense and the potential cause of adverse immune outcomes. The competition for iron has been recognized between both the immune system and invading pathogens and expressed in a ratio of ferritin divided by p87 (as defined by the Adnab-9 ELISA stool-binding optical density, minus the background), known as the FERAD ratio. Associations with the FERAD ratio may allow predictive modeling for the susceptibility and severity of disease. We evaluated other potential COVID-19 biomarkers prospectively. Patients with PCR+ COVID-19 tests (Group 1; n = 28) were compared to three other groups. In Group 2 (n = 36), and 13 patients displayed COVID-19-like symptoms but had negative PCR or negative antibody tests. Group 3 (n = 90) had no symptoms and were negative when routinely PCR-tested before medical procedures. Group 4 (n = 2129) comprised a pool of patients who had stool tests and symptoms, but their COVID-19 diagnoses were unknown; therefore, they were chosen to represent the general population. Twenty percent of the Group 4 patients (n = 432) had sufficient data to calculate their FERAD ratios, which were inversely correlated with the risk of COVID-19 in the future. In a case report of a neonate, we studied three biomarkers implicated in COVID-19, including p87, Src (cellular-p60-sarcoma antigen), and Abl (ABL-proto-oncogene 2). The InImS of the first two were positively correlated. An inverse correlation was found between ferritin and lysozyme in serum (p < 0.05), suggesting that iron could have impaired an important innate immune system anti-viral effector and could partially explain future COVID-19 susceptibility.

Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.

Evaluation of the Hematological Patterns from Up to 985 Days of Long COVID: A Cross-Sectional Study

Long COVID affects many individuals following acute coronavirus disease 2019 (COVID-19), and hematological changes can persist after the acute COVID-19 phase. This study aimed to evaluate these hematological laboratory markers, linking them to clinical findings and long-term outcomes in patients with long COVID. This cross-sectional study selected participants from a ‘long COVID’ clinical care program in the Amazon region. Clinical data and baseline demographics were obtained, and blood samples were collected to quantify erythrogram-, leukogram-, and plateletgram-related markers. Long COVID was reported for up to 985 days. Patients hospitalized in the acute phase had higher mean red/white blood cell, platelet, and plateletcrit levels and red blood cell distribution width. Furthermore, hematimetric parameters were higher in shorter periods of long COVID than in longer periods. Patients with more than six concomitant long COVID symptoms had a higher white blood cell count, a shorter prothrombin time (PT), and increased PT activity. Our results indicate there may be a compensatory mechanism for erythrogram-related markers within 985 days of long COVID. Increased levels of leukogram-related markers and coagulation activity were observed in the worst long COVID groups, indicating an exacerbated response after the acute disturbance, which is uncertain and requires further investigation.

Risk of thrombosis recurrence among patients with COVID-19- and surgery-associated venous thromboembolism

Introduction

Recent surgery is a well-known major transient risk factor for venous thromboembolism (VTE) due to the low risk of VTE recurrence after anticoagulation is discontinued. On the other hand, the risk of VTE recurrence among patients with COVID-19-associated VTE is unknown. This study aimed to compare the risk of VTE recurrence between patients with COVID-19- and surgery-associated VTE.

Methods

A prospective observational single-center study was performed including consecutive patients diagnosed with VTE in a tertiary hospital from January 2020 to May 2022 and followed up for at least 90 days. Baseline characteristics, clinical presentation, and outcomes were assessed. The incidence of VTE recurrence, bleeding, and death was compared between both groups.

Results

A total of 344 patients were included in the study: 111 patients with surgery-associated VTE and 233 patients with COVID-19-associated VTE. Patients with COVID-19-associated VTE were more frequently men (65.7% vs 48.6%, p =  0.003). VTE recurrence was 3% among COVID-19 patients and 5.4% among surgical patients, with no significant differences (p =  0.364). The incidence rate of recurrent VTE was 1.25 per 1000 person-months in COVID-19 patients and 2.29 person-months in surgical patients, without significant differences (p =  0.29). In the multivariate analysis, COVID-19 was associated with higher mortality (HR 2.34; 95% CI 1.19–4.58), but not with a higher risk of recurrence (HR 0.52; 95% CI 0.17–1.61). No differences were found in recurrence in the multivariate competing risk analysis (SHR 0.82; 95% CI 0.40–2.05).

Conclusions

In patients with COVID-19 and surgery-associated VTE, the risk of recurrence was low, with no differences between both groups.

Impact of inflammatory markers, dread diseases and cycle threshold (Ct) Values in COVID-19 progression

The link between the inflammatory marker and SARS-CoV-2 cycle threshold (Ct) with disease progression remains undefined, mainly in coronavirus disease-2019 (COVID-19). Therefore, this study aimed to identify several inflammatory markers (Ferritin, LDH, and D-dimer), and Ct values to predict outcomes in hospitalized COVID-19 Iraqi patients. A case study was performed on 426 patients to guess cutoff values of inflammatory markers that were detected by a real-time polymerase chain reaction (RT-PCR) and specific auto-analyzer instrument. Significantly increased levels of inflammatory markers in critical and severe patients compared with mild-moderate (p < 0.001). Compared with aging and disease severity, inflammatory markers and Ct values are significantly related to the aging and severity in critical and severe COVID-19 patients (p < 0.001). Finding the Ct value was negatively associated with Ferritin, LDH, and D-dimer (p < 0.001); moreover, inflammatory markers concentrations and Ct values were significantly higher during the first ten days. The Ct values correlate with some relevant clinical parameters of inflammation. Higher levels of D dimer, S. Ferritin and LDH were associated with older age and the severity of COVID-19. The area under the ROC curve indicates that serum ferritin was the highest and excellent predictor for disease severity. Keywords: Coronavirus disease 2019; Inflammation; D-dimer; Ferritin; Lactate dehydrogenase; Cycle threshold (Ct).

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.