Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention

• Main Authors: Deng, L. (Author), Huang, D. (Author), Su, X. (Author), Zeng, X. (Author), Zhao, X. (Author), Zhou, M. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2022
• Subjects: coronary angiography; Coronary Angiography; hospital mortality; Hospital Mortality; human; Humans; In-hospital mortality; machine learning; Machine learning; Machine Learning; no reflow phenomenon; No-reflow; No-Reflow Phenomenon; percutaneous coronary intervention; Percutaneous Coronary Intervention; Primary percutaneous coronary intervention; procedures; Retrospective Studies; retrospective study; ST Elevation Myocardial Infarction; ST segment elevation myocardial infarction; ST-elevation myocardial infarction
• Online Access: View Fulltext in Publisher

 Background: The machine learning algorithm (MLA) was implemented to establish an optimal model to predict the no reflow (NR) process and in-hospital death that occurred in ST-elevation myocardial infarction (STEMI) patients who underwent primary percutaneous coronary intervention (pPCI). Methods: The data were obtained retrospectively from 854 STEMI patients who underwent pPCI. MLA was applied to predict the potential NR phenomenon and confirm the in-hospital mortality. A random sampling method was used to split the data into the training (66.7%) and testing (33.3%) sets. The final results were an average of 10 repeated procedures. The area under the curve (AUC) and the associated 95% confidence intervals (CIs) of the receiver operator characteristic were measured. Results: A random forest algorithm (RAN) had optimal discrimination for the NR phenomenon with an AUC of 0.7891 (95% CI: 0.7093–0.8688) compared with 0.6437 (95% CI: 0.5506–0.7368) for the decision tree (CTREE), 0.7488 (95% CI: 0.6613–0.8363) for the support vector machine (SVM), and 0.681 (95% CI: 0.5767–0.7854) for the neural network algorithm (NNET). The optimal RAN AUC for in-hospital mortality was 0.9273 (95% CI: 0.8819–0.9728), for SVM, 0.8935 (95% CI: 0.826–0.9611); NNET, 0.7756 (95% CI: 0.6559–0.8952); and CTREE, 0.7885 (95% CI: 0.6738–0.9033). Conclusions: The MLA had a relatively higher performance when evaluating the NR risk and in-hospital mortality in patients with STEMI who underwent pPCI and could be utilized in clinical decision making. © 2022, The Author(s).