Analysis of long-term cardiovascular outcomes of COVID-19. Artificial Intelligence model for risk evaluation

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. Background The COVID-19 pandemic has caused a serious worldwide health crisis with wide-reaching consequences. According to current studies, COVID-19 may contribute to the emergence of various problems, including cardiovascular disorders [1-3]. However, there are currently no strategies for predicting the cardiovascular post-acute COVID-19 consequences, nor is there any scientific examination of these effects [3]. This knowledge gap must be filled in order to improve post-acute COVID-19 care [4]. Purpose To conduct a 12-month follow-up longitudinal observation study to explore potential long-term cardiovascular outcomes of COVID-19 and develop an AI-based model to predict them. Methods We included 328 COVID-19 patients who were admitted to the hospital between February and April 2021. The average age of the studied population was 56.1, while 51% of the population was male. Patients with severe comorbidities, major adverse cardiovascular events (MACE) in anamnesis, and intra-hospital mortality were not included. After discharge, patients were monitored for 12 months. The data were randomly divided into derivation (n=201), validation (n=81), and external cohort (n=47). The obtained data include clinical data, results of laboratory and instrumental investigations, and medical records during the 1-year period. Our AI model is a convolutional neural network (CNN) consisting of sigmoid-activation-function neurons, trained by using outcome results, received in follow-ups, and evaluated on the external cohort. Results Due to the analysis of clinical data, higher levels of C-reactive protein, D-dimer, neutrophil/lymphocyte ratio, and lower levels of thyrotropin were associated with a higher 12-month risk of cardiovascular consequences. The longitudinal study showed that 16.4% of patients experienced MACE (8.5% - myocardial infarction, 5.2% - cerebrovascular disorders, 2.7% - pulmonary embolism) leading to cardiovascular death in 2.4% of the studied population. The total count of cardiovascular complications was 25.9% including first-detected hypertension, arrhythmias, heart failure etc. The derivation and validation datasets were used for the training of the CNN. The ROC analysis indicated that our AI model had a high predictive value for cardiovascular events during follow-up (AUC=0.891, 95% CI=0.869–0.913, p<0.001, sensitivity 92.4%, specificity 90.1%). The external cohort data analysis showed accurate results in 41/47 patients (87.2% accuracy), which significantly outperformed any other known methods of cardiovascular outcomes prediction during 12 months after COVID-19. Conclusions Cardiovascular health in COVID-19 survivors needs special attention since COVID-19 is linked to an elevated risk of post-acute cardiovascular consequences. Long-term outcomes for COVID-19 can be predicted using AI-driven models.