INR and COVID-19 severity and mortality: A systematic review with meta-analysis and meta-regression

Four years into the pandemic, managing COVID-19 patients with acute coagulopathy: what have we learned?

Coagulopathy alongside micro- and macrovascular thrombotic events were frequent characteristics of patients presenting with acute COVID-19 during the initial stages of the pandemic. However, over the past 4 years, the incidence and manifestations of COVID-19-associated coagulopathy have changed due to immunity from natural infection and vaccination and the appearance of new SARS-CoV-2 variants. Diagnostic criteria and management strategies based on early experience and studies for COVID-19-associated coagulopathy thus require reevaluation. As many other infectious disease states are also associated with hemostatic dysfunction, the coagulopathy associated with COVID-19 may be compounded, especially throughout the winter months, in patients with diverse etiologies of COVID-19 and other infections. This commentary examines what we have learned about COVID-19-associated coagulopathy throughout the pandemic and how we might best prepare to mitigate the hemostatic consequences of emerging infection agents.

Clinical Presentation and Outcomes of Hospitalized Patients with Chronic Kidney Disease and COVID-19 Variant Omicron

Purpose

To investigate the clinical characteristics of hospitalized patients with chronic kidney disease (CKD) and novel coronavirus (SARS-CoV-2) infection and identify potential risk factors that contribute to mortality.

Patients and Methods

This is a retrospective study, conducted on patients with CKD who were admitted to the First Medical Center of the People's Liberation Army General Hospital between December 1, 2022, and February 28, 2023. All patients were also infected with SARS-CoV-2. We analyzed the clinical characteristics of patients, and the patients were categorized into a survival group and a death group whose characteristics were compared. Cox regression analysis was used to identify risk factors that affected patient prognosis.

Results

A total of 406 patients were enrolled in this study, including 298 males (73.4%). The average age was 80.5 (67.0, 88.0) years, and the patients had an average estimated glomerular filtration rate (eGFR) of 50.3 (25.0-79.0) mL/min/1.73m². A total of 158 individuals died during hospitalization, resulting in a mortality rate of 38.9%. Renal function was worse in the death group than in the survival group (P < 0.001). Patients in the death group had more severe COVID-19 disease and higher CKD staging than those in the survival group (all P values < 0.001). Multivariate Cox regression analysis identified several risk factors that affected patient mortality, including being male, a higher resting heart rate (RHR) upon admission, dyspnea, a low lymphocyte count (Lym), a high international standardized ratio (INR), a high Acute Physiology and Chronic Health Evaluation II (APACHE II) score, heart failure, and the need for mechanical ventilation during the disease.

Conclusion

Hospitalized patients with CKD who were infected with SARS-CoV-2 (38.9%) had a relatively high mortality rate (38.9%). Furthermore, a marked correlation was observed between a reduced eGFR and an increased risk of mortality.

SARS-CoV-2 Infection in Pregnant Women With Hypothyroidism

Background Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection has been linked to increased maternal and fetal morbidity and mortality, as evidenced by numerous studies. Given the potential exacerbation of autoimmune diseases during viral infections, maternal and fetal complications such as preterm birth, low birth weight, or preeclampsia, often observed in pregnancies involving autoimmune thyroiditis with hypothyroidism, may be further aggravated. This study seeks to ascertain whether the association between viral infection and hypothyroidism contributes to an increase in adverse pregnancy outcomes. Methods This study included a cohort of 145 pregnant women with SARS-CoV-2 infection, who delivered in the Department of Obstetrics and Gynecology of the University Emergency Hospital in Bucharest, Romania, between January 1, 2020, and December 31, 2022. The participants were divided into two groups depending on the presence of autoimmune thyroiditis with hypothyroidism. We examined the maternal and fetal demographic parameters, paraclinical laboratory parameters, and outcomes, aiming to identify disparities between the two groups. Results Among the 145 SARS-CoV-2-positive pregnant women, the prevalence of hypothyroidism was 8.96%, with 13 cases reported. In the hypothyroidism group, the mean age of coronavirus disease 2019 (COVID-19) patients was higher (34.07 ± 5.18 years vs. 29.25 ± 6.23 years), as was the number of cases of investigated pregnancies, 12 (92.31%) vs. 91 (68.94%). There was no statistically significant correlation observed between fetal weight at birth, one-minute Apgar score, neonatal intensive care unit (NICU) admission, or intrauterine growth restriction between the two groups. Nevertheless, a case of stillbirth was recorded in the hypothyroidism group. The presence of thyroid pathology did not exacerbate the progression of the viral infection, as evidenced by the absence of cases of preeclampsia, ICU admission, or SARS-CoV-2 pneumonia. Conversely, the presence of hypothyroidism in pregnant women with SARS-CoV-2 infection was associated with lower uric acid levels and a slight decrease in international normalised ratio (INR) values. Additionally, there was a significant negative association between uric acid levels and the one-minute Apgar score in the hypothyroidism group, while no such correlations were observed in the other group. Furthermore, there was a statistically significant correlation between intrauterine growth restriction and uric acid values, as well as between the one-minute Apgar score and INR parameters, in both groups. Conclusion The link between SARS-CoV-2 infection and hypothyroidism does not appear to increase the risk of preterm birth, intrauterine growth restriction, or low fetal weight at birth. However, it may be associated with a higher risk of stillbirth. The presence of hypothyroidism in pregnant women with COVID-19 correlates with lower maternal uric acid levels and a slight decrease in INR values. The one-minute Apgar score correlates with the level of uric acid in pregnant women with SARS-CoV-2 infection and hypothyroidism.

Association of coagulation function with the risk of in-hospital mortality in patients with severe acute respiratory distress syndrome

BACKGROUND

To evaluate the association of coagulation disorder score with the risk of in-hospital mortality in acute respiratory distress syndrome (ARDS) patients.

METHODS

In this cohort study, 7,001 adult patients with ARDS were identified from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV). Univariate and multivariate Logistic stepwise regression models were used to explore the associations of coagulation-associated biomarkers with the risk of in-hospital mortality in patients with ADRS. Restricted cubic spline (RCS) was plotted to explore the association between coagulation disorder score and in-hospital mortality of ARDS patients.

RESULTS

The follow-up time for in-hospital death was 7.15 (4.62, 13.88) days. There were 1,187 patients died and 5,814 people survived in hospital. After adjusting for confounding factors, increased risk of in-hospital mortality in ARDS patients was observed in those with median coagulation disorder score [odds ratio (OR) = 1.22, 95% confidence interval (CI): 1.01-1.47) and high coagulation disorder score (OR = 1.38, 95% CI: 1.06-1.80). The results of RCS indicated that when the coagulation disorder score >2, the trend of in-hospital mortality rose gradually, and OR was >1.

CONCLUSIONS

Poor coagulation function was associated with increased risk of in-hospital mortality in ARDS patients. The findings implied that clinicians should regularly detect the levels of coagulation-associated biomarkers for the management of ARDS patients.

Elevated International Normalized Ratio and Mortality in Hospitalized Patients Treated with Direct Oral Anticoagulants

BACKGROUND

Direct oral anticoagulants (DOACs) are associated with a prolongation of the prothrombin time and an increased international normalized ratio (INR). The clinical significance of these changes is unclear. This study aimed to examine the association between an elevated INR on admission and in-hospital death and long-term survival in patients treated with DOACs.

METHODS

Data were retrospectively retrieved from records of hospitalized patients at the Sheba Medical Center between November 2008 and July 2023. Patients were selected based on DOAC treatment, coagulation profile, and INR test done within 48 hours of hospitalization. The outcomes were in-hospital mortality and mortality in the year following hospitalization.

RESULTS

The study included 11,399 hospitalized patients treated with DOACs. Patients with elevated INR had a 180% higher risk of in-hospital mortality (adjusted odds ratio 2.80; 95% confidence interval, 2.30-3.39) and a 57% increased risk of death during the following year (adjusted hazard ratio 1.57; 95% confidence interval, 1.44-1.71). Similar results were observed in subgroup analyses for each DOAC.

CONCLUSIONS

An elevated INR on admission is associated with a higher risk for in-hospital death and increased risk for mortality during the first year following hospitalization in hospitalized patients treated with DOACs. This highlights that elevated INR levels in patients on DOACs should not be dismissed as laboratory variations due to DOAC treatment, as they may serve as a prognostic marker.

Role of biomarkers in prognostication of moderate and severe COVID-19 cases

Background

COVID-19 pandemic demanded upgrading of laboratory medicine to limit morbidity, disability and mortality from moderate and severe SARS-COV-2 infections.

Objective

To assess among moderate and severe COVID-19 patients, C-reactive protein (CRP), procalcitonin (PCT), ferritin, D-dimer, interleukin 6 (IL-6), lactate dehydrogenase (LDH), total and differential leucocyte count (TLC and DLC), neutrophil-to-lymphocyte ratio (NLR), absolute platelet count (APC), prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalized ratio (INR) to find their interdependence and role in prognosis.

Methods

This open label analytical cross-sectional noninterventional study evaluated array of independent biochemical, haematological and coagulopathy markers, viz. CRP, PCT, ferritin, D-dimer, IL-6, LDH, TLC, DLC, NLR, absolute platelet count, PT, APTT and INR on consecutive 100 patients with diagnosis of moderate and severe COVID-19 from July to August 2021.

Results

In our study, on consecutive designated 100 cases (55 cases moderate and 45 cases severe), more severity were reported as the age progressed; gender difference was not noted. Among independent markers, CRP, PCT, ferritin, D-dimer, IL-6 and LDH had statistically significant relation in comparison with severity of the disease as Chi-square calculated value (P < 0.05). TLC, DLC and APC showed no significant relation in comparison with severity of the disease; NLR had highly significant relation. PT showed significant relation in comparison with severity, though APTT and INR did not show significant relation.

Conclusion

Our research group felt that CRP, PCT, ferritin, D-dimer, IL-6, LDH and NLR should be in included in clinical practice guidelines to prognosticate COVID-19 cases. Furthermore, translational researches are needed at all levels of healthcare to improve validity for practices of primary care physicians.

The prothrombin time/international normalized ratio predicts prognosis in cardiogenic shock

OBJECTIVE

The study investigates the prognostic impact of the prothrombin time/international normalized ratio (PT/INR) in patients with cardiogenic shock.

BACKGROUND

Despite ongoing improvements regarding the treatment of cardiogenic shock patients, intensive care unit (ICU)-related mortality in cardiogenic shock patients remains unacceptably high. Limited data regarding the prognostic value of the PT/INR during the course of cardiogenic shock treatment is available.

METHODS

All consecutive patients with cardiogenic shock from 2019 to 2021 were included at one institution. Laboratory values were collected from the day of disease onset (day 1) and days 2, 3, 4 and 8. The prognostic impact of the PT/INR was tested for 30-day all-cause mortality, as well as the prognostic role of PT/INR changes during course of ICU hospitalization. Statistical analyses included univariable t -test, Spearman's correlation, Kaplan-Meier analyses, C-Statistics and Cox proportional regression analyses.

RESULTS

Two hundred twenty-four cardiogenic shock patients were included with a rate of all-cause mortality at 30 days of 52%. The median PT/INR on day 1 was 1.17. The PT/INR on day 1 was able to discriminate 30-day all-cause mortality in cardiogenic shock patients [area under the curve 0.618; 95% confidence interval (CI), 0.544-0.692; P  = 0.002). Patients with PT/INR > 1.17 were associated with an increased risk of 30-day mortality [62% vs. 44%; hazard ratio (HR) = 1.692; 95% CI, 1.174-2.438; P  = 0.005], which was still evident after multivariable adjustment (HR = 1.551; 95% CI, 1.043-2.305; P  = 0.030). Furthermore, especially patients with an increment of the PT/INR by ≥10% from day 1 to day 2 were associated with an increased risk of 30-day all-cause mortality (64% vs. 42%; log-rank P  = 0.014; HR = 1.833; 95% CI, 1.106-3.038; P  = 0.019).

CONCLUSION

Baseline PT/INR and an increase of the PT/INR during the course of ICU treatment were associated with the risk of 30-day all-cause mortality in cardiogenic shock patients.

Association between cyclin-dependent kinase inhibitor 2B antisense RNA 1 and zinc finger homeobox 3 gene polymorphisms and COVID-19 severity

BACKGROUND

There is no doubt about the cardiovascular complications of coronavirus disease 2019 (COVID-19). Several genetic studies have demonstrated an association between genetic variants in a region on chromosome 9p21 and in a region on chromosome 16q22 with myocardial infarction (MI) and atrial fibrillation (AF) accompanied by cerebral infarction (CI), respectively.

OBJECTIVES

MI and CI susceptibility in patients with CDKN2B-AS1 and ZFHX3 polymorphisms, respectively, may have an effect on COVID-19 severity. We aimed to investigate whether there is an association between the cyclin-dependent kinase inhibitor 2B antisense RNA 1 (CDKN2B-AS1) rs1333049 and zinc finger homeobox 3 (ZFHX3) rs2106261 single nucleotide polymorphisms (SNPs) and the degree of COVID-19 severity.

SUBJECTS AND METHODS

This current work was carried out on 360 subjects. They were classified into three groups: 90 severe COVID-19 cases, 90 moderate COVID-19 cases and 180 age- and gender-matched healthy controls. All subjects underwent genotyping of CDKN2B-AS1 (rs1333049) and ZFHX3 (rs2106261) by real-time PCR.

RESULTS

The frequency of G/C in CDKN2B-AS1 (rs1333049) was higher in severe and moderate COVID-19 patients than in controls (71.1% and 53.3% vs. 37.8%). The frequency of the C/C of CDKN2B-AS1 (rs1333049) was higher in moderate COVID-19 patients than in controls (26.7% vs. 13.3%). There were no significant differences regarding genotype frequency and allelic distribution of ZFHX3 (rs2106261) between COVID-19 patients and healthy controls.

CONCLUSION

CDKN2B-AS1 (rs1333049) gene polymorphism may play a role in determining the degree of COVID-19 severity. Further studies on its effect on cyclins and cyclin-dependent kinases (CDKs) [not measured in our study] may shed light on new treatment options for COVID-19.

Systemic inflammation index, disease severity, and mortality in patients with COVID-19: a systematic review and meta-analysis

Introduction

An excessive systemic pro-inflammatory state increases the risk of severe disease and mortality in patients with coronavirus disease 2019 (COVID-19). However, there is uncertainty regarding whether specific biomarkers of inflammation can enhance risk stratification in this group. We conducted a systematic review and meta-analysis to investigate an emerging biomarker of systemic inflammation derived from routine hematological parameters, the systemic inflammation index (SII), in COVID-19 patients with different disease severity and survival status.

Methods

A systematic literature search was conducted in PubMed, Web of Science, and Scopus, between the 1st of December 2019 and the 15th of March 2023. Risk of bias and certainty of evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist and the Grades of Recommendation, Assessment, Development and Evaluation, respectively (PROSPERO registration number: CRD42023420517).

Results

In 39 studies, patients with a severe disease or non-survivor status had significantly higher SII values on admission compared to patients with a non-severe disease or survivor status (standard mean difference (SMD)=0.91, 95% CI 0.75 to 1.06, p<0.001; moderate certainty of evidence). The SII was also significantly associated with the risk of severe disease or death in 10 studies reporting odds ratios (1.007, 95% CI 1.001 to 1.014, p=0.032; very low certainty of evidence) and in six studies reporting hazard ratios (1.99, 95% CI 1.01 to 3.92, p=0.047; very low certainty of evidence). Pooled sensitivity, specificity, and area under the curve for severe disease or mortality were 0.71 (95% CI 0.67 to 0.75), 0.71 (95% CI 0.64 to 0.77), and 0.77 (95% CI 0.73 to 0.80), respectively. In meta-regression, significant correlations were observed between the SMD and albumin, lactate dehydrogenase, creatinine, and D-dimer.

Discussion

Our systematic review and meta-analysis has shown that the SII on admission is significantly associated with severe disease and mortality in patients with COVID-19. Therefore, this inflammatory biomarker derived from routine haematological parameters can be helpful for early risk stratification in this group.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420517.

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

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

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Background and objective

A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

Material and method

This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

Results

Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

Conclusions

This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Remdesivir improves biomarkers associated with disease severity in COVID-19 patients treated in an outpatient setting

BACKGROUND

Remdesivir (RDV) is an intravenous antiviral with activity against SARS-CoV-2 for treatment of hospitalized COVID-19 patients with moderate-to-severe disease. Biomarkers associated with clinical outcomes have been identified for COVID-19, but few evaluated in context of antiviral treatment. Here, we assessed baseline (day 1, prior to first RDV dose) biomarkers and the impact of RDV treatment on longitudinal biomarker readouts.

METHODS

Recently, RDV was evaluated in high-risk, non-hospitalized patients with confirmed SARS-CoV-2 infection and was highly effective at preventing disease progression. The randomized, double-blind, placebo-controlled Phase 3 study included 562 participants who received at least 1 dose of study drug, of which 312 consented for longitudinal biomarker assessments at baseline, day 3, and day 14. We assessed sixteen baseline biomarkers and the impact of RDV treatment on longitudinal biomarker readouts.

RESULTS

Six well-known, inflammation-associated biomarkers are elevated at baseline in participants meeting the primary endpoint of hospitalization or death by day 28. Moreover, in comparison to placebo, biomarkers in RDV-treated participants show accelerated improvement, including reduction of soluble angiopoietin-2, D-dimer, and neutrophil-to-lymphocyte ratio, as well as an increase in lymphocyte counts.

CONCLUSIONS

Overall, the findings in this study suggest that RDV treatment may accelerate the improvement of multiple biomarkers of COVID-19 severity, which are associated with better clinical outcomes during infection. These findings have implications for better understanding the activity of antiviral treatments in COVID-19.

Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients

Rationale

COVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants.

Objective

This study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2.

Methods

COVID-19 patients were randomly divided into the training and the validation cohorts. Both Lasso and Logistic regression analyses were performed to identify prediction factors, which were then selected to build a deterioration model in the training cohort. This model was validated in the validation cohort.

Measurements and main results

The deterioration model of COVID-19 was constructed with five indices, including C-reactive protein, neutrophil count/lymphocyte count (NLR), albumin/globulin ratio (A/G), international normalized ratio (INR), and blood urea nitrogen (BUN). The area under the ROC curve (AUC) showed that this model displayed a high accuracy in predicting deterioration, which was 0.85 in the training cohort and 0.85 in the validation cohort. The nomogram provided an easy way to calculate the possibility of deterioration, and the decision curve analysis (DCA) and clinical impact curve analysis (CICA)showed good clinical net profit using this model.

Conclusion

The model we constructed can identify and predict the risk of deterioration (requirement for ventilatory support or death) in elderly patients and it is clinically practical, which will facilitate medical decision making and allocating medical resources to those with critical conditions.

Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity

Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.

[Significant Prolongation of the International Normalized Ratio Associated with COVID-19 Treatment: Possible Drug Interaction with Remdesivir]

A 55-year-old man with hypertrophic cardiomyopathy and a pacemaker was admitted with coronavirus disease 2019 (COVID-19). Before admission, the patient's medications included amiodarone, diltiazem, bisoprolol, atorvastatin, etizolam, and warfarin (WF). After admission, dexamethasone (DXM) and remdesivir (RDV) were initiated for treating COVID-19. The international normalized ratio (INR) on admission was 1.8, which increased to 3.4 on day 5 and to 6.9 on day 10 after admission. Although there have been reports that RDV may occasionally prolong prothrombin time and that the degree of prolongation is often less severe, the mechanism of action has not been elucidated till date. There are reports of prolonged INR when WF is co-administered with RDV and DXM, suggesting that drug interactions may be a potential cause for the prolongation. A similar drug interaction may have potentially occurred in the case reported here. In addition, this case used amiodarone (AMD), and it has been reported that the RDV concentration increases when used in combination with AMD. Further investigations are needed to elucidate the cause of INR prolongation. Thus, close monitoring of the patient is recommended when RDV is co-administered with high-risk agents to avoid unnecessary side effects.

Good practice statements for antithrombotic therapy in the management of COVID-19: Guidance from the SSC of the ISTH

Despite the emergence of high quality randomized trial data with the use of antithrombotic agents to reduce the risk of thromboembolism, end-organ failure, and possibly mortality in patients with coronavirus disease 2019 (COVID-19), questions still remain as to optimal patient selection for these strategies, the use of antithrombotics in outpatient settings and in-hospital settings (including critical care units), thromboprophylaxis in special patient populations, and the management of acute thrombosis in hospitalized COVID-19 patients. In October 2021, the International Society on Thrombosis and Haemostasis (ISTH) formed a multidisciplinary and international panel of content experts, two patient representatives, and a methodologist to develop recommendations on treatment with anticoagulants and antiplatelet agents for COVID-19 patients. The ISTH Guideline panel discussed additional topics to be well suited to a non-Grading of Recommendations Assessment, Development, and Evaluation (GRADE) for Good Practice Statements (GPS) to support good clinical care in the antithrombotic management of COVID-19 patients in various clinical settings. The GPS panel agreed on 17 GPS: 3 in the outpatient (pre-hospital) setting, 12 in the hospital setting both in non-critical care (ward) as well as intensive care unit settings, and 2 in the immediate post-hospital discharge setting based on limited evidence or expert opinion that supports net clinical benefit in enacting the statements provided. The antithrombotic therapies discussed in these GPS should be available in low- and middle-income countries.

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.

Inflammatory Biomarkers Associated with In-Hospital Mortality in Critical COVID-19 Patients

The COVID-19 pandemic poses global healthcare challenges due to its unpredictable clinical course. The aim of this study is to identify inflammatory biomarkers and other routine laboratory parameters associated with in-hospital mortality in critical COVID-19 patients. We performed a retrospective observational study on 117 critical COVID-19 patients. Following descriptive statistical analysis of the survivor and non-survivor groups, optimal cut-off levels for the statistically significant parameters were determined using the ROC method, and the corresponding Kaplan-Meier survival curves were calculated. The inflammatory parameters that present statistically significant differences between survivors and non-survivors are IL-6 (p = 0.0004, cut-off = 27.68 pg/mL), CRP (p = 0.027, cut-off = 68.15 mg/L) and IL-6/Ly ratio (p = 0.0003, cut-off = 50.39). Additionally, other statistically significant markers are creatinine (p = 0.031, cut-off = 0.83 mg/dL), urea (p = 0.0002, cut-off = 55.85 mg/dL), AST (p = 0.0209, cut-off = 44.15 U/L), INR (p = 0.0055, cut-off = 1.075), WBC (p = 0.0223, cut-off = 11.68 × 10⁹/L) and pH (p = 0.0055, cut-off = 7.455). A survival analysis demonstrated significantly higher in-hospital mortality rates of patients with values of IL-6, IL-6/Ly, AST, INR, and pH exceeding previously mentioned thresholds. In our study, IL-6 and IL-6/Ly have a predictive value for the mortality of critically-ill patients diagnosed with COVID-19. The integration of these parameters with AST, INR and pH could contribute to a prognostic score for the risk stratification of critical patients, reducing healthcare costs and facilitating clinical decision-making.

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

BACKGROUND AND AIMS

Severe manifestations of coronavirus disease 2019 (COVID-19) are associated with alterations in blood cells that regulate immunity, inflammation, and hemostasis. We conducted an updated systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), and COVID-19 progression and mortality.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published between January 2020 and June 2022.

RESULTS

In 71 studies reporting the investigated parameters within 48 hours of admission, higher NLR (HR 1.21, 95% CI 1.16 to 1.27, p < 0.0001), relative neutrophilia (HR 1.62, 95% CI 1.46 to 1.80, p < 0.0001), relative lymphopenia (HR 1.62, 95% CI 1.27 to 2.08, p < 0.001), and relative thrombocytopenia (HR 1.74, 95% CI 1.36 to 2.22, p < 0.001), but not PLR (p = 0.11), were significantly associated with disease progression and mortality. Between-study heterogeneity was large-to-extreme. The magnitude and direction of the effect size were not modified in sensitivity analysis.

CONCLUSIONS

NLR and neutrophil, lymphocyte, and platelet count significantly discriminate COVID-19 patients with different progression and survival outcomes. (PROSPERO registration number: CRD42021267875).

A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction

The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku.

Predictive Value of Blood Coagulation Parameters in Poor Outcomes in COVID-19 Patients: A Retrospective Observational Study in Romania

SARS-CoV-2 infection produces alterations in blood clotting, especially in severe cases of COVID-19. Abnormal coagulation parameters in patients with COVID-19 are important prognostic factors of disease severity. The objective of this study was to evaluate the predictive value of aPTT, D-dimer, INR and PT in the mortality of patients with COVID-19. A retrospective, single-center, observational study was conducted on COVID-19 patients admitted to the Municipal Emergency Clinical Hospital in Timisoara, Romania, between August and October 2021. Patients were confirmed as COVID-19 positive by reverse transcription-polymerase chain reaction (RT-PCR) assay. After applying the inclusion/exclusion criteria, a total of 82 patients were included in the analysis. Receiver operating characteristic (ROC) curves of D-Dimer, INR, PT and aPTT were generated to assess whether the baseline of each of these biomarkers was accurately predictive for mortality in patients with COVID-19. Mortality among patients enrolled in this study was 20.7%, associated with older age and presence of heart disease. The areas under the ROC curve (AUC-ROC) of D-Dimer, INR, PT, and aPTT were 0.751, 0.724, 0.706 and 0.753. Differences in survival for patients with coagulation biomarker levels above cut-off values compared to patients below these values were statistically significant. All evaluated parameters had significant differences and good performance in predicting mortality of COVID-19 patients, except fibrinogen, which had no significant difference. Moreover, aPTT and D-dimer were the best performing parameters in predicting mortality in patients with SARS-CoV-2 infection.

ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.

Point-of-Care Device for Assessment of Blood Coagulation Status in COVID-19 Patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent driving the current COVID-19 pandemic. The acute respiratory distress that occurs in some severe COVID-19 cases has been linked with hypercoagulation or thrombotic events as well as a worse prognosis and increased risk of death. Thus, point-of-care devices that can be used for early detection of coagulation abnormalities would assist in COVID-19 management. This chapter describes the use of the Roche Diagnostics CoaguChek^® XS test kit for potential use in COVID-19 personalized medicine approaches.

Diagnostic and Prognostic Significance of the Prothrombin Time/International Normalized Ratio in Sepsis and Septic Shock

OBJECTIVE

The study investigates the diagnostic and prognostic significance of the prothrombin time/international normalized ratio (PT/INR) in patients with sepsis and septic shock.

BACKGROUND

Sepsis may be complicated by disseminated intravascular coagulation (DIC). While the status of coagulopathy of septic patients is represented within the sepsis-3 definition by assessing the platelet count, less data regarding the prognostic impact of the PT/INR in patients admitted with sepsis and septic shock is available.

METHODS

Consecutive patients with sepsis and septic shock from 2019 to 2021 were included. Blood samples were retrieved from day of disease onset (ie, day 0), as well as on day 1, 2, 4, 6 and 9 thereafter. Firstly, the diagnostic value of the PT/INR in comparison to the activated partial thromboplastin time (aPTT) was tested for septic shock compared to sepsis without shock. Secondly, the prognostic value of the PT/INR for 30-day all-cause mortality was tested. Statistical analyses included univariable t-tests, Spearman's correlations, C-statistics, Kaplan-Meier analyses and Cox proportional regression analyses.

RESULTS

338 patients were included (56% sepsis without shock, 44% septic shock). The overall rate of all-cause mortality at 30 days was 52%. With an area under the curve (AUC) of 0.682 (p= .001) on day 0, the PT/INR revealed moderate discrimination of septic shock and sepsis without shock. Furthermore, PT/ INR was able to discriminate non-survivors and survivors at 30 days (AUC = 0.612; p = .001). Patients with a PT/INR >1.5 had higher rates of 30-day all-cause mortality than patients with lower values (mortality rate 73% vs 48%; log rank p = .001; HR = 2.129; 95% CI 1.494-3.033; p = .001), even after multivariable adjustment (HR = 1.793; 95% CI 1.343-2.392; p = .001). Increased risk of 30-day all-cause mortality was observed irrespective of concomitant thrombocytopenia.

CONCLUSION

The PT/INR revealed moderate diagnostic accuracy for septic shock but was associated with reliable prognostic accuracy with regard to 30-day all-cause mortality in patients admitted with sepsis and septic shock.