Key result
Machine learning algorithms, specifically Artificial Neural Networks and Random Forests, predicted periprocedural myocardial infarction with an accuracy of 0.72 and an AUC of 0.77.
Why the study?
Current approaches to predict periprocedural myocardial infarction after PCI fail to identify many patients who could benefit from preventive treatment.
Do machine learning algorithms improve the prediction of periprocedural myocardial infarction in inpatients with single coronary stenosis undergoing stent implantation?
Observational (n=10,886)
No
Do machine learning algorithms improve the prediction of periprocedural myocardial infarction in inpatients with single coronary stenosis undergoing stent implantation?
Machine learning models, particularly Artificial Neural Networks and Random Forest, can predict periprocedural myocardial infarction with moderate accuracy in patients undergoing PCI.
ML models achieved moderate accuracy for PMI prediction after PCI; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: Periprocedural myocardial infarction (PMI) after percutaneous coronary intervention (PCI) is associated with the bad prognosis in patients. Current approaches to predict PMI fail to identify many people who would benefit from preventive treatment, and machine learning (ML) offers opportunity to improve the performance of ML models for PMI based on the big routine data. METHODS: By using electronic medical records, we retrospectively extracted all records of patients from 2007 to 2019 in our cardiovascular center. The main enrollment criterion was that inpatients with one single coronary stenosis with stents implantation this time. The primary outcome was PMI [PMI3: cTnI >3-fold upper reference limit (URL); PMI5: cTnI >5-fold URL]. Four different ML algorithms [Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Networks (ANN)] were evaluated and their diagnostic accuracy measures were compared. RESULTS: A total of (10,886) patients who were admitted in our hospital. PMI3 and PMI5 results were analyzed respectively. The incidence of PMI3 and PMI5 was 20.9% and 13.7%. In PMI3 Drop group, ANN (accuracy: 0.72; AUC: 0.77) showed the best power to predict the presence of PMI; In PMI3 Mean Group, RF (accuracy: 0.72; AUC: 0.77) showed the best power; In PMI5 Drop group, RF (accuracy: 0.67; AUC: 0.67) showed the best power; In PMI5 Mean group, RF (accuracy: 0.61; AUC: 0.67) showed the best power. CONCLUSIONS: ML methods may provide accurate prediction of PMI in CAD patients, and could be used as a precise model in the preventive treatment of PMI.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2020) conducted an observational in Periprocedural myocardial infarction after percutaneous coronary intervention (n=10,886). Machine learning algorithms (SVM, LR, RF, ANN) was evaluated on Periprocedural myocardial infarction (PMI3: cTnI >3-fold URL; PMI5: cTnI >5-fold URL). Machine learning algorithms, specifically Artificial Neural Networks and Random Forests, predicted periprocedural myocardial infarction with an accuracy of 0.72 and an AUC of 0.77.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: