Dataset Information

Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.

ABSTRACT:

Background

Three-vessel disease (TVD) with a SYNergy between PCI with TAXus and cardiac surgery (SYNTAX) score of ≥ 23 is one of the most severe types of coronary artery disease. We aimed to take advantage of machine learning to help in decision-making and prognostic evaluation in such patients.

Methods

We analyzed 3786 patients who had TVD with a SYNTAX score of ≥ 23, had no history of previous revascularization, and underwent either coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) after enrollment. The patients were randomly assigned to a training group and testing group. The C4.5 decision tree algorithm was applied in the training group, and all-cause death after a median follow-up of 6.6 years was regarded as the class label.

Results

The decision tree algorithm selected age and left ventricular end-diastolic diameter (LVEDD) as splitting features and divided the patients into three subgroups: subgroup 1 (age of ≤ 67 years and LVEDD of ≤ 53 mm), subgroup 2 (age of ≤ 67 years and LVEDD of > 53 mm), and subgroup 3 (age of > 67 years). PCI conferred a patient survival benefit over CABG in subgroup 2. There was no significant difference in the risk of all-cause death between PCI and CABG in subgroup 1 and subgroup 3 in both the training data and testing data. Among the total study population, the multivariable analysis revealed significant differences in the risk of all-cause death among patients in three subgroups.

Conclusions

The combination of age and LVEDD identified by machine learning can contribute to decision-making and risk assessment of death in patients with severe TVD. The present results suggest that PCI is a better choice for young patients with severe TVD characterized by left ventricular dilation.

SUBMITTER: Jie L

PROVIDER: S-EPMC9170909 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.

Jie Liu L Feng Xin-Xing XX Duan Yan-Feng YF Liu Jun-Hao JH Zhang Ce C Jiang Lin L Xu Lian-Jun LJ Tian Jian J Zhao Xue-Yan XY Zhang Yin Y Sun Kai K Xu Bo B Zhao Wei W Hui Ru-Tai RT Gao Run-Lin RL Wang Ji-Zheng JZ Yuan Jin-Qing JQ Huang Xin X Song Lei L

Journal of geriatric cardiology : JGC 20220501 5

<h4>Background</h4>Three-vessel disease (TVD) with a SYNergy between PCI with TAXus and cardiac surgery (SYNTAX) score of ≥ 23 is one of the most severe types of coronary artery disease. We aimed to take advantage of machine learning to help in decision-making and prognostic evaluation in such patients.<h4>Methods</h4>We analyzed 3786 patients who had TVD with a SYNTAX score of ≥ 23, had no history of previous revascularization, and underwent either coronary artery bypass grafting (CABG) or perc ...[more]

PMID: 35722036

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Dataset Information

Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.

Background

Methods

Results

Conclusions

Publications

Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.

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