Impaired cardiac function has been described as a frequent complication of COVID-19-related pneumonia. To investigate possible underlying mechanisms, we represented the cardiovascular system by means of a lumped-parameter 0D mathematical model. The model was calibrated using clinical data, recorded in 58 patients hospitalized for COVID-19-related pneumonia, to make it patient-specific and to compute model outputs of clinical interest related to the cardiocirculatory system. We assessed, for each patient with a successful calibration, the statistical reliability of model outputs estimating the uncertainty intervals. Then, we performed a statistical analysis to compare healthy ranges and mean values (over patients) of reliable model outputs to determine which were significantly altered in COVID-19-related pneumonia. Our results showed significant increases in right ventricular systolic pressure, diastolic and mean pulmonary arterial pressure, and capillary wedge pressure. Instead, physical quantities related to the systemic circulation were not significantly altered. Remarkably, statistical analyses made on raw clinical data, without the support of a mathematical model, were unable to detect the effects of COVID-19-related pneumonia, thus suggesting that the use of a calibrated 0D mathematical model to describe the cardiocirculatory system is an effective tool to investigate the impairments of the cardiocirculatory system associated with COVID-19.

翻译：心脏功能受损已被描述为COVID-19相关肺炎的常见并发症。为探究潜在机制，我们通过集总参数0D数学模型对心血管系统进行了建模。该模型利用58例因COVID-19相关肺炎住院患者的临床数据进行校准，以实现患者特异性，并计算与心循环系统相关的临床关注模型输出指标。对于校准成功的每位患者，我们评估了模型输出的统计可靠性，估计其不确定性区间。随后，我们开展统计分析，比较可靠模型输出的健康范围与患者均值，以确定COVID-19相关肺炎中显著改变的关键指标。结果显示，右心室收缩压、舒张期及平均肺动脉压、毛细血管楔压显著升高，而体循环相关物理量未发生显著改变。值得注意的是，基于原始临床数据（无数学模型支持）的统计分析无法检测到COVID-19相关肺炎的影响，这表明使用经校准的0D数学模型描述心循环系统，是研究COVID-19相关心循环系统损伤的有效工具。