Early coagulation tests predict risk stratification and prognosis of COVID-19

Clinical Value of Coagulation Function Indicators in Children with Severe Pneumonia

Objective

This study aimed to probe the changes in coagulation function-related indicators (prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (D-D), and fibrinogen degradation product (FDP)) in severe pneumonia and their clinical significance.

Methods

The levels of coagulation function indicators of all the children were measured within 24 hours of admission. Pearson correlation analysis was utilized to analyze the correlation between PT, APTT, FIB, D-D, FDP and PCIS in children with severe pneumonia. The ROC curve was drawn to assess the power of PT, APTT, FIB, D-D and FDP in diagnosing severe pneumonia and predicting the prognosis of severe pneumonia. A logistic regression analysis was implemented to analyze the factors influencing the prognosis of children with severe pneumonia.

Results

PT, APTT, FIB, FDP, and D-D in the critically severe pneumonia and the extremely severe pneumonia groups were higher versus the common pneumonia group (P < 0.05). FDP and D-D levels in children with severe pneumonia were negatively correlated with PCIS. PT, APTT, FIB, FDP, and D-D of children in the poor prognosis group were higher compared with those in the good prognosis group (P < 0.05). Further logistic regression analysis unveiled that FDP and APTT were influential factors impacting the prognosis of severe pneumonia.

Conclusion

The levels of D-D, FDP, FIB, APTT, and PT in severe pneumonia are increased. Detecting the contents of coagulation function indicators can help clinical judgment of the changes in the condition of severe pneumonia and evaluate prognosis.

Genome wide-scale CRISPR-Cas9 knockout screens identify a fitness score for optimized risk stratification in colorectal cancer

BACKGROUND

The molecular complexity of colorectal cancer poses a significant challenge to the clinical implementation of accurate risk stratification. There is still an urgent need to find better biomarkers to enhance established risk stratification and guide risk-adapted treatment decisions.

METHODS

we systematically analyzed cancer dependencies of 17 colorectal cancer cells and 513 other cancer cells based on genome-scale CRISPR-Cas9 knockout screens to identify colorectal cancer-specific fitness genes. A regression model was built using colorectal cancer-specific fitness genes, which was validated in other three independent cohorts. 30 published gene expression signatures were also retrieved.

FINDINGS

We defined a total of 1828 genes that were colorectal cancer-specific fitness genes and identified a 22 colorectal cancer-specific fitness gene (CFG22) score. A high CFG22 score represented unfavorable recurrence and mortality rates, which was validated in three independent cohorts. Combined with age, and TNM stage, the CFG22 model can provide guidance for the prognosis of colorectal cancer patients. Analysis of genomic abnormalities and infiltrating immune cells in the CFG22 risk stratification revealed molecular pathological difference between the subgroups. Besides, drug analysis found that CFG22 high patients were more sensitive to clofibrate.

INTERPRETATION

The CFG22 model provided a powerful auxiliary prediction tool for identifying colorectal cancer patients with high recurrence risk and poor prognosis, optimizing precise treatment and improving clinical efficacy.

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.

Diagnosis and Treatment of Coagulopathy Caused by the New Coronavirus: A Systematic Review and Meta-Analysis Protocol

Background

The new coronavirus is an agent of respiratory infections associated with thrombosis in vital organs. This study aimed to propose a better diagnosis and treatment of coagulation disorders caused by the new coronavirus (Covid-19).

Materials and Methods

Search in Cochrane central, Web of Science, PubMed, Scopus, and Ovid will be done. Also, according to the inclusion criteria, cross-sectional studies, cohort, clinical trial, and case-control will be included without gender and language restriction. Participants will also be Covid-19 patients with coagulation disorders. Any disagreement in the stages of screening, selection, and extraction of data between the two reviewers will be resolved by discussion, then if not resolved, the opinion of expert reviewers will be used. The risk of bias will be assessed using the NOS (Newcastle-Ottawa scale) tool for cross-sectional study, cohort and case-control, and the Cochrane checklist for clinical trials study. Metaanalysis of included studies that are similar based on the methodology will be done. Also, a fixed or random-effect model will be used for this it. Heterogeneity indices (I2), odds ratio (OR), risk ratio (RR), mean difference, and %95 confidence interval will also be calculated by Stata V.13.0 (Corporation, College Station TX).

Results

Treatment with anticoagulants will reduce the severity of thrombosis and lung disease in patients. D-dimer measurement will also be a diagnosis indicator of thrombosis.

Conclusions

Simultaneous study of coagulation disorders and thrombosis in patients and development of a Godliness based on it will play a treatment role in the follow-up of the coronavirus disease.

The Role of Biomarkers in Hospitalized COVID-19 Patients With Systemic Manifestations

The following article aims to review COVID-19 biomarkers used in hospital practice. It is apparent that COVID-19 is not simply a pulmonary disease but has systemic manifestations. For this reason, biomarkers must be used in the management of diagnosed patients to provide holistic care. Patients with COVID-19 have been shown to have pulmonary, hepatobiliary, cardiovascular, neurologic, and renal injury, along with coagulopathy and a distinct cytokine storm. Biomarkers can effectively inform clinicians of systemic organ injury due to COVID-19. Furthermore, biomarkers can be used in predictive models for severe COVID-19 in admitted patients. The utility of doing so is to allow for risk stratification and utilization of proper treatment protocols. In addition, COVID-19 biomarkers in the pediatric population are discussed, specifically in predicting Multisystem Inflammatory Syndrome. Ultimately, biomarkers can be used as predictive tools to allow clinicians to identify and adequately manage patients at increased risk for worse outcomes from COVID-19. Both literature review and anecdotal evidence has shown that severe COVID-19 is a systemic disease, and understanding associated biomarkers are crucial for hospitalized patients’ proper clinical decision-making. For example, the cytokine storm releases inflammatory markers in different organ systems such as the pulmonary, hepatobiliary, hematological, cardiac, neurological, and renal systems. This review summarizes the latest research of COVID-19 that can help inform healthcare professionals how to better mitigate morbidity and mortality associated with this disease and provides information about certain systemic biomarkers that can be incorporated into hospital practice to provide more comprehensive care for hospitalized COIVD-19 patients.

Meta-Analysis and Systematic Review of Coagulation Disbalances in COVID-19: 41 Studies and 17,601 Patients

Introduction

Coagulation parameters are important determinants for COVID-19 infection. We conducted meta-analysis to assess the association between early hemostatic parameters and infection severity.

Methods

Electronic search was made for papers that addressed clinical characteristics of COVID-19 patients and disease severity. Results were filtered using exclusion and inclusion criteria and then pooled into a meta-analysis to estimate the standardized mean difference (SMD) with 95% confidence interval (CI) for D-dimers, fibrinogen, prothrombin time, platelet count (PLT), activated partial thromboplastin time. To explore the heterogeneity and robustness of our fundings, sensitivity and subgroup analyses were conducted. Publication bias was assessed with contour-enhanced funnel plots and Egger's test by linear regression. Coagulation parameters data from retrospective cohort study of 451 patients with COVID-19 at National Research Center for Cardiac Surgery were included in meta-analysis of published studies.

Results

Overall, 41 original studies (17,601 patients) on SARS-CoV-2 were included. For the two groups of patients, stratified by severity, we identified that D-dimers, fibrinogen, activated partial thromboplastin time, and prothrombin time were significantly higher in the severe group [SMD 0.6985 with 95%CI (0.5155; 0.8815); SMD 0.661 with 95%CI (0.3387; 0.9833); SMD 0.2683 with 95%CI (0.1357; 0.4009); SMD 0.284 with 95%CI (0.1472; 0.4208)]. In contrast, PLT was significantly lower in patients with more severe cases of COVID-19 [SMD -0.1684 with 95%CI (-0.2826; -0.0542)]. Neither the analysis by the leave-one-out method nor the influence diagnostic have identified studies that solely cause significant change in the effect size estimates. Subgroup analysis showed no significant difference between articles originated from different countries but revealed that severity assessment criteria might have influence over estimated effect sizes for platelets and D-dimers. Contour-enhanced funnel plots and the Egger's test for D-dimers and fibrinogen revealed significant asymmetry that might be a sign of publication bias.

Conclusions

The hemostatic laboratory parameters, with exception of platelets, are significantly elevated in patients with severe COVID-19. The two variables with strongest association to disease severity were D-dimers and fibrinogen levels. Future research should aim outside conventional coagulation tests and include analysis of clotting formation and platelet/platelet progenitors characteristics.

The ATRIA score is superior to the m-CHA2DS2-Vasc score in predicting in-hospital mortality in COVID-19

OBJECTIVE

Coronavirus disease 2019 (COVID-19) has become a health and social problem all over the world. Most of the deaths occur from embolism and thrombus formation. We aimed to compare the predictive value of the anticoagulation and risk factors in atrial fibrillation (ATRIA) and m-CHA2DS2-Vasc scores in in-hospital mortality in COVID-19.

METHODS

Three-hundred and ninety-four patients who were hospitalized due to COVID-19 between 10 June 2020 and 10 September 2020 were included. Three-hundred and sixty patients who survived were defined as the non-mortality group and the remaining 34 whose hospitalizations resulted in death were defined as the mortality group. The anticoagulation and risk factors in atrial fibrillation and m-CHA2DS2-Vasc scores of the patients were calculated.

RESULTS

A total of 394 patients, mean age 66.2±9.7 (221 male [56.1%]) were included in this retrospective study. The median values of the anticoagulation and risk factors in atrial fibrillation and m-CHA2DS2-Vasc scores were different between the groups (p<0.000 for both). The multivariate logistic regression analysis showed that both the m-CHA2DS2-Vasc and anticoagulation and risk factors in atrial fibrillation scores were independent predictors of in-hospital mortality (p=0.024, 95%CI 1.039-1.704 for anticoagulation and risk factors in atrial fibrillation and p=0.043, 95%CI 1.012-2.088 for m-CHA2DS2-Vasc). In the receiver operating characteristic curve analysis, the anticoagulation and risk factors in atrial fibrillation score was superior to the m-CHA2DS2-Vasc score with an AUC 0.774 and SE:0.037, and p<0.001.

CONCLUSIONS

In our study, we showed that the anticoagulation and risk factors in atrial fibrillation and m-CHA2DS2-Vasc scores can be used as predictors of thrombosis and mortality in COVID-19 patients. In addition, the predictive value of the anticoagulation and risk factors in atrial fibrillation score was higher than that of m-CHA2DS2-Vasc. The use of the anticoagulation and risk factors in atrial fibrillation score to assess high-risk patients in COVID-19 may be recommended.

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study

Background:

In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time.

Methods:

We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using

An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps.

Results:

We observed that

Conclusions:

Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.

Dynamic changes in coagulation parameters and correlation with disease severity and mortality in patients with COVID-19

Objective: This study aimed to describe the dynamic changes of coagulation parameters and evaluate the relationship between longitudinal coagulation parameters abnormalities and prognosis of COVID-19 patients.

Methods: We performed a retrospective study of 1131 COVID-19 patients. Longitudinal coagulation parameters and clinical outcomes were analyzed.

Results: Abnormal coagulation parameters were observed in patients with COVID-19, both at hospital admission (INR 2.3%, PT 7.9%, APTT 15.4%, TT 0.9%, FDP 2.3%, D-dimer 19.7%) and peak hospitalization (INR 4.8%, PT 13.4%, APTT 25.6%, TT 2.7%, FDP 10.4%, D-dimer 31.5%). Compared with non-severe patients with COVID-19, severe patients had a slightly higher INR, PT, APTT, whereas remarkably higher FDP and D-dimer (p < 0.05). On multivariate analysis, age > 60 years, male, obesity, comorbidity, abnormal D-dimer on hospital admission, and abnormal peak hospitalization PT, APTT, FDP and D-dimer were associated with COVID-19 severity. The extreme coagulation parameters abnormalities (PT > 16s, FDP > 50 ug/ml, and D-dimer > 5 ug/ml) were associated with a significantly higher mortality.

Conclusion: Longitudinal coagulation parameters abnormalities are common in patients with COVID-19, and associated with disease severity and mortality. Monitoring coagulation parameters is advisable to improve the management of patients with COVID-19.

Identification of parameters in routine blood and coagulation tests related to the severity of COVID-19

Objective: This study aimed to identify the predictive value of simple markers in routine blood and coagulation tests for the severity of coronavirus disease 2019 (COVID-19). Methods: A total of 311 consecutive COVID-19 patients, including 281 patients with mild/moderate COVID-19 and 30 patients with severe/life-threatening COVID-19, were retrospectively enrolled. Logistic modeling and ROC curve analyses were used to assess the indexes for identifying disease severity. Results: Lymphocyte and eosinophil counts of COVID-19 patients in the severe/life-threatening group were significantly lower than those of patients in the mild/moderate group (P < 0.001). Coagulation parameters, high-sensitivity C-reactive protein (hsCRP) levels and procalcitonin levels were higher in the severe/life-threatening group compared with the mild/moderate group (all P < 0.05). Univariate and multivariate logistic models revealed that hsCRP and fibrinogen degradation products (FDPs) were predictors of severe COVID-19 (OR = 1.072, P = 0.036; and OR = 1.831, P = 0.036, respectively). The AUROCs of hsCRP and FDP for predicting severe/life-threatening COVID-19 were 0.850 and 0.766, respectively. The optimal cutoffs of hsCRP and FDP for the severe/life-threatening type of COVID-19 were 22.41 mg/L and 0.95 µg/ml, respectively. Conclusion: Serum CRP and FDP levels are positively related to the severity of COVID-19. This finding indicates that CRP and FDP levels may potentially be used as early predictors for severe illness and help physicians triage numerous patients in a short time.

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

Background

COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments.

Objective

To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission.

Methods

We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions.

Results

In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results.

Conclusions

Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.