Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Department of Biostatistics, Research Center for Health Sciences, School of Public Health, Hamadan University of Medical Sciences, P.O. Box 4171-65175, Hamadan, Iran.
BMC Cardiovasc Disord. 2022 Aug 30;22(1):389. doi: 10.1186/s12872-022-02825-0.
This study aimed to use the hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict the long term occurrence of major adverse cardiac and cerebrovascular events (MACCE) of patients underwent percutaneous coronary intervention (PCI) with stent implantation.
This retrospective cohort study included a total of 220 patients (69 women and 151 men) who underwent PCI in Ekbatan medical center in Hamadan city, Iran, from March 2009 to March 2012. The occurrence and non-occurrence of MACCE, (including death, CABG, stroke, repeat revascularization) were considered as a binary outcome. The predictive performance of ANFIS model for predicting MACCE was compared with ANFIS-PSO and logistic regression.
During ten years of follow-up, ninety-six patients (43.6%) experienced the MACCE event. By applying multivariate logistic regression, the traditional predictors such as age (OR = 1.05, 95%CI: 1.02-1.09), smoking (OR = 3.53, 95%CI: 1.61-7.75), diabetes (OR = 2.17, 95%CI: 2.05-16.20) and stent length (OR = 3.12, 95%CI: 1.48-6.57) was significantly predicable to MACCE. The ANFIS-PSO model had higher accuracy (89%) compared to the ANFIS (81%) and logistic regression (72%) in the prediction of MACCE.
The predictive performance of ANFIS-PSO is more efficient than the other models in the prediction of MACCE. It is recommended to use this model for intelligent monitoring, classification of high-risk patients and allocation of necessary medical and health resources based on the needs of these patients. However, the clinical value of these findings should be tested in a larger dataset.
本研究旨在采用基于自适应神经模糊推理系统（ANFIS）和粒子群优化（PSO）的混合方法，预测接受经皮冠状动脉介入治疗（PCI）和支架植入术的患者发生主要不良心脑血管事件（MACCE）的长期预后。
本回顾性队列研究共纳入 220 名（69 名女性和 151 名男性）于 2009 年 3 月至 2012 年 3 月在伊朗哈马丹市埃克巴坦医疗中心接受 PCI 的患者。MACCE 的发生和未发生（包括死亡、CABG、卒中和再次血运重建）被视为二项结局。比较了 ANFIS 模型、ANFIS-PSO 模型和逻辑回归模型预测 MACCE 的预测性能。
在十年的随访期间，96 名患者（43.6%）发生了 MACCE 事件。通过多变量逻辑回归，年龄（OR=1.05，95%CI：1.02-1.09）、吸烟（OR=3.53，95%CI：1.61-7.75）、糖尿病（OR=2.17，95%CI：2.05-16.20）和支架长度（OR=3.12，95%CI：1.48-6.57）等传统预测因素对 MACCE 有显著预测价值。与 ANFIS（81%）和逻辑回归（72%）相比，ANFIS-PSO 模型在预测 MACCE 方面具有更高的准确性（89%）。
与其他模型相比，ANFIS-PSO 模型在预测 MACCE 方面的预测性能更优。建议根据患者的需求，使用该模型进行智能监测、高危患者分类和必要医疗资源的分配。然而，这些发现的临床价值需要在更大的数据集上进行检验。
