A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study

Abstract

Background: A suitable multivariate predictor for predicting mortality following percutaneous coronary intervention (PCI) remains undetermined. We used a nationwide database to construct mortality prediction models to find the appropriate model. Methods: Data were analyzed from the Taiwan National Health Insurance Research Database (NHIRD) covering the period from 2004 to 2013. The study cohort was composed of 3,421 patients with acute myocardial infarction (AMI) diagnosis undergoing PCI. The dataset of enrolled patients was used to construct multivariate prediction models. Of these, 3,079 and 342 patients were included in the training and test groups, respectively. Each patient had 22 input features and 2 output features that represented mortality. This study implemented an artificial neural network model (ANN), a decision tree (DT), a linear discriminant analysis classifier (LDA), a logistic regression model (LR), a naïve Bayes classifier (NB), and a support vector machine (SVM) to predict post-PCI patient mortality.Results: The DT model was found to be the most suitable in terms of performance and real-world applicability. The DT model achieved an area under receiving operating characteristic of 0.895 (95% confidence interval: 0.865-0.925), F1 of 0.969, precision of 0.971, and recall of 0.974. Conclusions:The DT model constructed using data from the NHIRD exhibited effective 30-day mortality prediction for patients with AMI following PCI.

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

“…The other study suggested that when predicting cardiac and sudden death during a one-year follow-up, the AUC in the ML models was improved by 0.08 compared to that in GRACE 17 . Another study reported AUCs of 0.828, 0.895, 0.810, and 0.882 in an artificial neural network (ANN), decision tree (DT), naïve Bayes (NB), and SVM, respectively, for the 30-day mortality, which were slightly higher than or similar to the values (0.83) from the GRACE risk score methods suggested in the validation study 3 , 18 . On the other hand, the previous study did not compare the performance between the conventional models and the ML models in the research data, so that it could only be inferred indirectly 18 .…”

“…Another study reported AUCs of 0.828, 0.895, 0.810, and 0.882 in an artificial neural network (ANN), decision tree (DT), naïve Bayes (NB), and SVM, respectively, for the 30-day mortality, which were slightly higher than or similar to the values (0.83) from the GRACE risk score methods suggested in the validation study 3 , 18 . On the other hand, the previous study did not compare the performance between the conventional models and the ML models in the research data, so that it could only be inferred indirectly 18 . Although the above three studies showed that ML algorithms could enhance discrimination, other researchers proposed that the ML models were not always preferable to the traditional model.…”

“…Two of the three studies showing comparable performance between the traditional and ML models included patients with STEMI only 19 , 20 . In contrast, all three studies showing superior performance of the ML models included all patients with STEMI and NSTEMI 11 , 17 , 18 . Although this could not explain all the inconsistency because subgroup analysis showed that ML also outperformed GRACE in STEMI in a previous study 11 , the different performances of the ML models between the STEMI and NSTEMI groups may have contributed to the inconsistent findings.…”

AI-aided dynamic prediction of bleeding and ischemic risk after coronary stenting and subsequent DAPT

“…Over the past decade, AI techniques, especially ML and deep learning (DL), have been successfully applied in cardiovascular (CV) medicine-bringing tremendous precision to diagnosis and prognostication-and may play a critical role in facilitating evolution of CV medicine to precision CV medicine (12,13). AI algorithms have been increasingly employed in the CAD-related prediction tasks, yielding prime performance compared with the classic statistical models (14)(15)(16)(17). Contemporary studies, however, can provide only a fixed window prediction with static input data.…”

Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries

“…The prediction of cardiovascular adverse events has been traditionally based on logistic regression modelling, itself a machine learning technique, with other, more advanced machine-learning algorithms gaining popularity only recently [ 7 , 8 ]. Several studies comparing conventional risk assessment methods with machine learning models in patients undergoing percutaneous coronary angioplasty reported a significantly improved performance and discrimination of the latter, while others showed only a modest improvement [ 8 , 9 , 10 , 11 ].…”