Key result
The ANFIS-PSO model achieved higher accuracy (89%) in predicting long-term major adverse cardiovascular events following percutaneous coronary intervention compared to ANFIS (81%) and logistic regression (72%).
Why the study?
The study aimed to evaluate a hybrid model combining an adaptive neuro-fuzzy inference system and particle swarm optimization to predict long-term MACCE after PCI with stent implantation.
Does an ANFIS-PSO machine learning model improve the prediction of long-term MACCE in patients undergoing PCI compared to standard ANFIS and traditional logistic regression?
Cohort (n=220)
No
Does an ANFIS-PSO machine learning model improve the prediction of long-term MACCE in patients undergoing PCI compared to standard ANFIS and traditional logistic regression?
Absolute Event Rate: 89% vs 72%
A hybrid machine learning model (ANFIS-PSO) significantly outperformed traditional logistic regression in predicting 10-year MACCE following PCI, suggesting potential utility for long-term risk stratification.
May aid post-PCI MACCE stratification; leaves open prospective validation before clinical use.
BACKGROUND: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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.
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Karsidani et al. (2022) conducted a cohort in Coronary artery disease requiring percutaneous coronary intervention (n=220). ANFIS-PSO model vs. Logistic regression was evaluated on Accuracy in predicting major adverse cardiac and cerebrovascular events (MACCE). The ANFIS-PSO model achieved higher accuracy (89%) in predicting long-term major adverse cardiovascular events following percutaneous coronary intervention compared to ANFIS (81%) and logistic regression (72%).
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