Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With COVID-19.

Background: COVID-19 infection carries significant morbidity and mortality. Current risk prediction for complications in COVID-19 is limited, and existing approaches fail to account for the dynamic course of the disease. Objectives: The purpose of this study was to develop and validate the COVID-HEART predictor, a novel continuously updating risk-prediction technology to forecast adverse events in hospitalized patients with COVID-19. Methods: Retrospective registry data from patients with severe acute respiratory syndrome coronavirus 2 infection admitted to 5 hospitals were used to train COVID-HEART to predict all-cause mortality/cardiac arrest (AM/CA) and imaging-confirmed thromboembolic events (TEs) (n = 2,550 and n = 1,854, respectively). To assess COVID-HEART's performance in the face of rapidly changing clinical treatment guidelines, an additional 1,100 and 796 patients, admitted after the completion of development data collection, were used for testing. Leave-hospital-out validation was performed. Results: Over 20 iterations of temporally divided testing, the mean area under the receiver operating characteristic curve were 0.917 (95% confidence interval [CI]: 0.916-0.919) and 0.757 (95% CI: 0.751-0.763) for prediction of AM/CA and TE, respectively. The interquartile ranges of median early warning times were 14 to 21 hours for AM/CA and 12 to 60 hours for TE. The mean area under the receiver operating characteristic curve for the left-out hospitals were 0.956 (95% CI: 0.936-0.976) and 0.781 (95% CI: 0.642-0.919) for prediction of AM/CA and TE, respectively. Conclusions: The continuously updating, fully interpretable COVID-HEART predictor accurately predicts AM/CA and TE within multiple time windows in hospitalized COVID-19 patients. In its current implementation, the predictor can facilitate practical, meaningful changes in patient triage and resource allocation by providing real-time risk scores for these outcomes. The potential utility of the predictor extends to COVID-19 patients after hospitalization and beyond COVID-19.

Post-Acute COVID-19 Syndrome and the cardiovascular system: What is known?

Post-Acute COVID-19 Syndrome (PACS) is defined by persistent symptoms >3-4 weeks after onset of COVID-19. The mechanism of these persistent symptoms is distinct from acute COVID-19 although not completely understood despite the high incidence of PACS. Cardiovascular symptoms such as chest pain and palpitations commonly occur in PACS, but the underlying cause of symptoms is infrequently known. While autopsy studies have shown that the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) rarely causes direct myocardial injury, several syndromes such as myocarditis, pericarditis, and Postural Orthostatic Tachycardia Syndrome have been implicated in PACS. Additionally, patients hospitalized with acute COVID-19 who display biomarker evidence of myocardial injury may have underlying coronary artery disease revealed by the physiological stress of SARS-CoV-2 infection and may benefit from medical optimization. We review what is known about PACS and the cardiovascular system and propose a framework for evaluation and management of related symptoms.