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Machine learning prediction of no reflow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.
| Content Provider | Europe PMC |
|---|---|
| Author | Wang, Lin Bao, Pei Wang, Xiaochen Xu, Banglong Liu, Zeyan Hu, Guangquan |
| Copyright Year | 2024 |
| Abstract | BackgroundNo-reflow (NRF) phenomenon is a significant challenge in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI). Accurate prediction of NRF may help improve clinical outcomes of patients. This retrospective study aimed at creating an optimal model based on machine learning (ML) to predict NRF in these patients, with the additional objective of guiding pre- and intra-operative decision-making to reduce NRF incidence.MethodsData were collected from 321 STEMI patients undergoing pPCI between January 2022 and May 2023, with the dataset being randomly divided into training and internal validation sets in a 7:3 ratio. Selected features included pre- and intra-operative demographic data, laboratory parameters, electrocardiogram, comorbidities, patients’ clinical status, coronary angiographic data, and intraoperative interventions. Post comprehensive feature cleaning and engineering, three logistic regression (LR) models [LR-classic, LR-random forest (LR-RF), and LR-eXtreme Gradient Boosting (LR-XGB)], a RF model and an eXtreme Gradient Boosting (XGBoost) model were developed within the training set, followed by performance evaluation on the internal validation sets.ResultsAmong the 261 patients who met the inclusion criteria, 212 were allocated to the normal flow group and 49 to the NRF group. The training group consisted of 183 patients, while the internal validation group included 78 patients. The LR-XGB model, with an area under the curve (AUC) of 0.829 [95% confidence interval (CI): 0.779–0.880], was selected as the representative model for logistic regression analyses. The LR model had an AUC slightly lower than XGBoost model (AUC 0.835, 95% CI: 0.781–0.889) but significantly higher than RF model (AUC 0.731, 95% CI: 0.660–0.802). Internal validation underscored the unique advantages of each model, with the LR model demonstrating the highest clinical net benefit at relevant thresholds, as determined by decision curve analysis. The LR model encompassed seven meaningful features, and notably, thrombolysis in myocardial infarction flow after initial balloon dilation (TFAID) was the most impactful predictor in all models. A web-based application based on the LR model, hosting these predictive models, is available at https://l7173o-wang-lyn.shinyapps.io/shiny-1/.ConclusionsA LR model was successfully developed through ML to forecast NRF phenomena in STEMI patients undergoing pPCI. A web-based application derived from the LR model facilitates clinical implementation. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC11384450&blobtype=pdf |
| ISSN | 22233652 |
| Volume Number | 14 |
| DOI | 10.21037/cdt-24-83 |
| PubMed Central reference number | PMC11384450 |
| Issue Number | 4 |
| PubMed reference number | 39263488 |
| Journal | Cardiovascular Diagnosis and Therapy [Cardiovasc Diagn Ther] |
| e-ISSN | 22233660 |
| Language | English |
| Publisher | AME Publishing Company |
| Publisher Date | 2024-08-08 |
| Access Restriction | Open |
| Rights License | Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0. 2024 Cardiovascular Diagnosis and Therapy. All rights reserved. |
| Subject Keyword | Machine learning (ML) ST-segment elevation myocardial infarction (STEMI) primary percutaneous coronary intervention (pPCI) no-reflow (NRF) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cardiology and Cardiovascular Medicine |
