POINT: Should Therapeutic Heparin Be Administered to Acutely Ill Hospitalized Patients With COVID-19? Yes

Role of Therapeutic Anticoagulation in COVID-19: The Current Situation

Thrombotic complications from COVID-19 are now well known and contribute to significant morbidity and mortality. Different variants confer varying risks of thrombotic complications. Heparin has anti-inflammatory and antiviral effects. Due to its non-anticoagulant effects, escalated-dose anticoagulation, especially therapeutic-dose heparin, has been studied for thromboprophylaxis in hospitalized patients with COVID-19. Few randomized, controlled trials have examined the role of therapeutic anticoagulation in moderately to severely ill patients with COVID-19. Most of these patients had elevated D-dimers and low bleeding risks. Some trials used an innovative adaptive multiplatform with Bayesian analysis to answer this critical question promptly. All the trials were open-label and had several limitations. Most trials showed improvements in the meaningful clinical outcomes of organ-support-free days and reductions in thrombotic events, mainly in non-critically-ill COVID-19 patients. However, the mortality benefit needed to be more consistent. A recent meta-analysis confirmed the results. Multiple centers initially adopted intermediate-dose thromboprophylaxis, but the studies failed to show meaningful benefits. Given the new evidence, significant societies have suggested therapeutic anticoagulation in carefully selected patients who are moderately ill and do not require an intensive-care-unit level of care. There are multiple ongoing trials globally to further our understanding of therapeutic-dose thromboprophylaxis in hospitalized patients with COVID-19. In this review, we aim to summarize the current evidence regarding the use of anticoagulation in patients with COVID-19 infection.

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach

BACKGROUND

The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19.

METHODS

Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.

RESULTS

Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes.

CONCLUSIONS

This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model's components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies.

Critically Ill COVID-19 Patients Show Reduced Point of Care-Measured Butyrylcholinesterase Activity—A Prospective, Monocentric Observational Study

A biomarker for risk stratification and disease severity assessment in SARS-CoV-2 infections has not yet been established. Point of care testing (POCT) of butyrylcholinesterase (BChE) enables early detection of systemic inflammatory responses and correlates with disease severity in sepsis and burns. In acute care or resource-limited settings, POCT facilitates rapid clinical decision making, a particularly beneficial aspect in the management of pandemic situations. In this prospective observational study, POCT-measured BChE activity was assessed in 52 critically ill COVID-19 patients within 24 h of ICU admission and on the third and seventh day after ICU admission. Forty (77%) of these patients required venovenous extracorporeal membrane oxygenation (vvECMO). In critically ill COVID-19 patients, BChE activity is significantly decreased compared with healthy subjects, but also compared with other inflammatory conditions such as sepsis, burns, or trauma. POCT BChE activity reflects the severity of organ dysfunction and allows prediction of 28-day mortality in critically ill COVID-19 patients. Implementing early POCT BChE measurement could facilitate risk stratification and support admission and transfer decisions in resource-limited settings.