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Transthoracic Echocardiography-Based Prediction Model of Adverse Event Risk in Patients with COVID-19статья
- Авторы: Golukhova E.Z., Slivneva I.V., Mamalyga M.L., Marapov D.I., Alekhin M.N., Rybka M.M., Volkovskaya I.V.
- Журнал: Pathophysiology
- Том: 29
- Номер: 2
- Год издания: 2022
- Издательство: MDPI Publishing
- Местоположение издательства: Basel, Switzerland, Switzerland
- Первая страница: 157
- Последняя страница: 172
- DOI: 10.3390/pathophysiology29020014
- Аннотация: Cardiopulmonary disorders cause a significant increase in the risk of adverse events in patients with COVID-19. Therefore, the development of new diagnostic and treatment methods for comorbid disorders in COVID-19 patients is one of the main public health challenges. The aim of the study was to analyze patient survival and to develop a predictive model of survival in adults with COVID-19 infection based on transthoracic echocardiography (TTE) parameters. We conducted a prospective, single-center, temporary hospital-based study of 110 patients with moderate to severe COVID-19. All patients underwent TTE evaluation. The predictors of mortality we identified in univariate and multivariable models and the predictive performance of the model were assessed using receiver operating characteristic (ROC) analysis and area under the curve (AUC). The predictive model included three factors: right ventricle (RV)/left ventricle (LV) area (odds ratio (OR) = 1.048 per 1/100 increase, p = 0.03), systolic pulmonary artery pressure (sPAP) (OR = 1.209 per 1 mm Hg increase, p < 0.001), and right ventricle free wall longitudinal strain (RV FW LS) (OR = 0.873 per 1% increase, p = 0.036). The AUC-ROC of the obtained model was 0.925 ± 0.031 (95% confidence interval (95% CI): 0.863–0.986). The sensitivity (Se) and specificity (Sp) measures of the models at the cut-off point of 0.129 were 93.8% and 81.9%, respectively. A binary logistic regression method resulted in the development of a prognostic model of mortality in patients with moderate and severe COVID-19 based on TTE data. It may also have additional implications for early risk stratification and clinical decision making in patients with COVID-19.
- Добавил в систему: Мамалыга Максим Леонидович