Prevalence and clinical implication of thrombocytopenia and heparin-induced thrombocytopenia in patients who are critically ill with COVID-19.

As the COVID-19 pandemic continues to evolve, different clinical manifestations are better understood and studied. These include various haematologic disorders that have been shown to be associated with increased morbidity and mortality. We studied the prevalence of one unusual manifestation, heparin-induced thrombocytopenia (HIT) and its clinical implications in patients who are severely ill with COVID-19 in a single tertiary centre in Israel. The presence of thrombocytopenia, disseminated intravascular coagulation (DIC) and HIT, and their association with clinical course and outcomes were studied. One-hundred and seven patients with COVID-19 were included. Fifty-seven (53.2%) patients developed thrombocytopenia, which was associated with the worst outcomes (ventilation, DIC and increased mortality). Sixteen (28.0%) patients with thrombocytopenia were positive for HIT, all of which were supported by extracorporeal devices. HIT was independently associated with ventilation days, blood product transfusions, longer hospitalisation and mortality.Platelet abnormalities and HIT are common in patients who are critically ill with COVID-19 and are associated with the worst clinical outcomes. The mechanisms underlying HIT in COVID-19 are yet to be studied; HIT may contribute to the dysregulated immunologic response associated with COVID-19 critical illness and may play a significant part in the coagulopathy seen in these patients. As many patients with COVID-19 require aggressive thromboprophylaxis, further understanding of HIT and the implementation of appropriate protocols are important.

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.