Golukhova Elena Zelikovna, Slivneva Inessa Viktorovna, Mamalyga Maxim Leonidovich, Marapov Damir Ildarovich, Alekhin Mikhail Nikolaevich, Rybka Mikhail Mikhailovich, Volkovskaya Irina Vasilevna
A.N. Bakulev National Medical Scientific Center for Cardiovascular Surgery, Ministry of Health of the Russian Federation, 121552 Moscow, Russia.
Department of Emergency Ultrasound and Functional Diagnostics, A.N. Bakulev National Medical Scientific Center for Cardiovascular Surgery, Ministry of Health of the Russian Federation, 121552 Moscow, Russia.
Pathophysiology. 2022 Apr 26;29(2):157-172. doi: 10.3390/pathophysiology29020014.
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.
心肺疾病会显著增加新冠肺炎患者发生不良事件的风险。因此，开发针对新冠肺炎患者合并症的新诊断和治疗方法是主要的公共卫生挑战之一。本研究的目的是分析患者的生存率，并基于经胸超声心动图（TTE）参数建立新冠肺炎成年患者生存的预测模型。我们对110例中重度新冠肺炎患者进行了一项前瞻性、单中心、基于临时医院的研究。所有患者均接受了TTE评估。我们在单变量和多变量模型中确定了死亡率的预测因素，并使用受试者工作特征（ROC）分析和曲线下面积（AUC）评估了模型的预测性能。预测模型包括三个因素：右心室（RV）/左心室（LV）面积（每增加1/100，比值比（OR）=1.048，p = 0.03）、收缩期肺动脉压（sPAP）（每增加1 mmHg，OR = 1.209，p < 0.001）和右心室游离壁纵向应变（RV FW LS）（每增加1%，OR = 0.873，p = 0.036）。所获模型的AUC-ROC为0.925±0.031（95%置信区间（95%CI）：0.863−0.986）。在截断点为0.129时，模型的敏感性（Se）和特异性（Sp）分别为93.8%和81.9%。二元逻辑回归方法基于TTE数据建立了中重度新冠肺炎患者死亡率预后模型。它可能对新冠肺炎患者的早期风险分层和临床决策也有额外的意义。
