ArticleBMC medical informatics and decision making2022
Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- Interpretable machine learning analysis of immune-inflammatory biomarkers for predicting no-reflow in patients with acute myocardial infarction after percutaneous coronary intervention.Annals of medicine · 2026Article
- Artificial intelligence in the cath lab: Bridging predictive models and real-time procedural decision support.EXCLI journal · 2026Review
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance.Reviews in cardiovascular medicine · 2025Article
- STEMI-OP in-hospital mortality prediction algorithms: Frailty-integrated machine learning in older patients undergoing primary PCI.npj aging · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2024Article
- Machine learning prediction of no reflow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.Cardiovascular diagnosis and therapy · 2024Article
- The Predictive Value of the Inflammatory Prognostic Index for Detecting No-Reflow in ST-Elevation Myocardial Infarction Patients.Arquivos brasileiros de cardiologia · 2024Article
- Machine learning models to predict 30-day mortality for critical patients with myocardial infarction: a retrospective analysis from MIMIC-IV database.Frontiers in cardiovascular medicine · 2024Article
- Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.Frontiers in cardiovascular medicine · 2024Article
- Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.Cardiovascular digital health journal · 2023Article
- Utility of electrocardiogram to predict the occurrence of the no-reflow phenomenon in patients undergoing primary percutaneous coronary intervention (PPCI): a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2023Review
- [Clinical variables associated with no-reflow after percutaneous coronary intervention in ST-segment elevation myocardial infarction: Secondary analysis of the PERSTEMI I and II study].Archivos peruanos de cardiologia y cirugia cardiovascularArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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).
methodsThe 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.
resultsA 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).
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.