Recurrent upper extremity arterial thrombosis preceding a diagnosis of COVID-19.

Arterial thrombosis occurs when there is endothelial damage in the setting of hypercoagulability and arterial blood stasis. COVID-19 has been theorized to cause both endothelial damage and promote hypercoagulability by causing an imbalance of clotting factors. In many studies, there have been a large proportion of COVID-19 patients that suffered a thromboembolic event, in both the venous and arterial systems. Our patient, who did not have a significant past medical history, presented with a recurrent brachial artery occlusion despite medical and surgical management, and subsequently tested positive for COVID-19 late in his admission. In conclusion, there is high suspicion that there is a relationship between COVID-19 infection and recurrent arterial thrombosis.

COVID-19 associated Multisystem Inflammatory Syndrome in Children (MIS-C) guidelines; revisiting the Western New York approach as the pandemic evolves.

Multisystem inflammatory syndrome of children (MIS-C) continues to be a highly concerning diagnosis in those recently infected with SARS-CoV-2. The diagnosis of MIS-C cases will likely become even more challenging as vaccine uptake and natural immunity in previously infected persons leads to lower circulating rates of SARS-CoV-2 infection and will make cases sporadic. Febrile children presenting with cardiac dysfunction, symptoms overlapping Kawasaki disease or significant gastrointestinal complaints warrant a thorough screen in emergency departments, urgent care centers, and outpatient pediatric or family medicine practices. An increased index of suspicion and discussion regarding higher level of care (transferring to pediatric tertiary care centers or to intensive care) continues to be recommended. Herein we outline a broad approach with a multidisciplinary team for those meeting the case definition and believe such an approach is crucial for successful outcomes.

Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

OBJECTIVE: To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND METHODS: We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system. RESULTS: The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. CONCLUSION: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.