Meinarovich Peter, Pautova Alisa, Zuev Evgenii, Sorokina Ekaterina, Chernevskaya Ekaterina, Beloborodova Natalia
Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, 25-2 Petrovka Str., 107031 Moscow, Russia.
J Clin Med. 2024 Aug 26;13(17):5054. doi: 10.3390/jcm13175054.
Early diagnosis of post-operative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. The purpose of this study was to determine whether an integrated approach based on clinical data, along with metabolites and biomarkers, had greater predictive value than the models built on fewer data in the early diagnosis of post-operative complications after cardiac surgery. : The study included patients ( = 62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass) with ( = 26) or without ( = 36) post-operative complications. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers and metabolites. : Multivariate PLS-DA models, predicting the presence or absence of post-operative complications, were built using clinical data, concentrations of metabolites and biomarkers, and the entire data set (ROC-AUC = 0.80, 0.71, and 0.85, respectively). For comparison, we built univariate models using the EuroScore2 and SOFA scales, concentrations of lactate, the dynamic changes of 4-hydroxyphenyllactic acid, and the sum of three sepsis-associated metabolites (ROC-AUC = 0.54, 0.79, 0.62, 0.58, and 0.70, respectively). The proposed complex model using the entire dataset had the best characteristics, which confirms the expediency of searching for new predictive models based on a variety of factors.
术后并发症的早期诊断是一项紧迫任务，这有助于及时开出恰当的治疗方案并降低患者治疗成本。本研究的目的是确定基于临床数据以及代谢物和生物标志物的综合方法，在心脏手术后并发症的早期诊断中是否比基于较少数据构建的模型具有更大的预测价值。该研究纳入了因计划进行心脏手术（体外循环下冠状动脉搭桥术）而入院的患者（n = 62），其中有术后并发症的患者（n = 26）和无术后并发症的患者（n = 36）。分析了术后第一天的临床和实验室数据。此外，在手术前和术后第一天采集患者血样以测定生物标志物和代谢物。使用临床数据、代谢物和生物标志物浓度以及整个数据集构建了预测术后并发症有无的多变量PLS - DA模型（ROC - AUC分别为0.80、0.71和0.85）。为作比较，我们使用欧洲心脏手术风险评估系统2（EuroScore2）和序贯器官衰竭评估（SOFA）量表、乳酸浓度、4 - 羟基苯乳酸的动态变化以及三种脓毒症相关代谢物的总和构建了单变量模型（ROC - AUC分别为0.54、0.79、0.62、0.58和0.70）。所提出的使用整个数据集的复杂模型具有最佳特征，这证实了基于多种因素寻找新的预测模型的合理性。
