Collegium Medicum, Jan Kochanowski University in Kielce, al. IX Wieków Kielc 19A, 25-369 Kielce, Poland.
European Institute of Post-Graduate Education in Kielce, Duża 21, 25-305 Kielce, Poland.
Int J Environ Res Public Health. 2022 Dec 11;19(24):16654. doi: 10.3390/ijerph192416654.
Air pollution and COVID-19 infection affect the pathogenesis of cardiovascular disease. The impact of these factors on the course of ACS treatment is not well defined. The purpose of this study was to evaluate the effects of air pollution, COVID-19 infection, and selected clinical factors on the occurrence of perioperative death in patients with acute coronary syndrome (ACS) by developing a neural network model. This retrospective study included 53,076 patients with ACS from the ORPKI registry (National Registry of Invasive Cardiology Procedures) including 2395 COVID-19 (+) patients and 34,547 COVID-19 (-) patients. The neural network model developed included 57 variables, had high performance in predicting perioperative patient death, and had an error risk of 0.03%. Based on the analysis of the effect of permutation on the variable, the variables with the greatest impact on the prediction of perioperative death were identified to be vascular access, critical stenosis of the left main coronary artery (LMCA) or left anterior descending coronary artery (LAD). Air pollutants and COVID-19 had weaker effects on end-point prediction. The neural network model developed has high performance in predicting the occurrence of perioperative death. Although COVID-19 and air pollutants affect the prediction of perioperative death, the key predictors remain vascular access and critical LMCA or LAD stenosis.
空气污染和 COVID-19 感染影响心血管疾病的发病机制。这些因素对 ACS 治疗过程的影响尚未明确。本研究的目的是通过开发神经网络模型来评估空气污染、COVID-19 感染和选定的临床因素对急性冠状动脉综合征 (ACS) 患者围手术期死亡的发生的影响。这项回顾性研究纳入了来自 ORPKI 注册中心（全国经皮冠状动脉介入治疗登记处）的 53076 例 ACS 患者，其中包括 2395 例 COVID-19(+)患者和 34547 例 COVID-19(-)患者。开发的神经网络模型包含 57 个变量，在预测围手术期患者死亡方面具有较高的性能，且错误风险为 0.03%。通过对变量的排列分析，确定对围手术期死亡预测影响最大的变量是血管入路、左主干冠状动脉 (LMCA) 或左前降支冠状动脉 (LAD) 的临界狭窄。空气污染物和 COVID-19 对终点预测的影响较弱。所开发的神经网络模型在预测围手术期死亡的发生方面具有较高的性能。尽管 COVID-19 和空气污染物会影响围手术期死亡的预测，但关键预测因素仍然是血管入路和临界 LMCA 或 LAD 狭窄。
