Kiziltas Faruk Sanberk, Ozkan Ozhan, Toptan Fatih, Kara Ibrahim, Dogan Zuhal, Gokmen Kadir, Eryilmaz Esra Gundogdu, Erdem Ali Fuat
Department of Electrical and Electronics Engineering, Sakarya University, 54050, Sakarya, Turkey.
Department of Anesthesiology and Reanimation, Sakarya Training and Research Hospital, 54100, Sakarya, Turkey.
Sci Rep. 2025 Nov 13;15(1):39841. doi: 10.1038/s41598-025-23437-6.
This study investigated the effect of hemodynamic data during cardiopulmonary bypass (CPB) on neurocognitive impairment in patients undergoing coronary artery bypass graft (CABG) surgery using machine learning algorithms. Twenty-eight CABG patients were included in the study. Systolic and diastolic blood pressure, pulse (PLS), and SpO were recorded at 2.4-second intervals, yielding 730,870 data points (450,548 after artefact removal). Data were analyzed using Random Forest classification and Partial Dependence Plots (PDPs) to evaluate the collective and individual effects of parameters on neurocognitive outcomes. Separate analyses were conducted for the surgery period, bypass, and cross-clamp periods. Cognitive impairment was assessed using the Montreal Cognitive Assessment (MoCA), with a decrease of ≥ 2 points considered indicative of impairment. The highest classification accuracy (86%) was achieved during the bypass period when all parameters were analyzed together. Parameter importance varied by surgical phase: PLS was most significant during the surgery period and bypass periods. PDPs revealed specific optimal parameter ranges for each surgical phase. This study highlights the complex and dynamic role of hemodynamic parameters in preserving neurocognitive function during CABG surgery. Findings suggest that monitoring specific combinations of parameters during different surgical phases could help reduce neurocognitive risks. The significant importance of PLS data and its relationship with mean arterial pressure (MAP) values indicates that pulsatile flow may play a crucial role in neurocognitive protection. Future research should validate these findings in larger cohorts and develop phase-specific monitoring strategies for clinical implementation.
本研究使用机器学习算法，调查了冠状动脉旁路移植术（CABG）患者体外循环（CPB）期间的血流动力学数据对神经认知障碍的影响。28例CABG患者纳入本研究。以2.4秒的间隔记录收缩压、舒张压、脉搏（PLS）和脉搏血氧饱和度（SpO），共产生730,870个数据点（去除伪影后为450,548个）。使用随机森林分类和偏倚依赖图（PDP）分析数据，以评估参数对神经认知结果的综合和个体影响。分别对手术期、体外循环期和主动脉阻断期进行分析。使用蒙特利尔认知评估量表（MoCA）评估认知障碍，下降≥2分被认为提示有障碍。当所有参数一起分析时，体外循环期的分类准确率最高（86%）。参数重要性因手术阶段而异：PLS在手术期和体外循环期最为显著。PDP揭示了每个手术阶段的特定最佳参数范围。本研究强调了血流动力学参数在CABG手术中保护神经认知功能方面的复杂和动态作用。研究结果表明，在不同手术阶段监测特定的参数组合可能有助于降低神经认知风险。PLS数据的显著重要性及其与平均动脉压（MAP）值的关系表明，搏动血流可能在神经认知保护中起关键作用。未来的研究应在更大的队列中验证这些发现，并制定针对各阶段的监测策略以用于临床实践。
