ArticleClinical cardiology2021
Machine learning for early prediction of in-hospital cardiac arrest in patients with acute coronary syndromes.
Article in Clinical cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Applications of the Early Warning Score as an outcome measure: a systematic review of methodologies.BMJ health & care informatics · 2026Pooled it
- Prediction of in-hospital cardiac arrest on general wards using calibrated machine learning.BMJ health & care informatics · 2026Article
- Explainable machine learning for estimation of elevated left ventricular filling pressure: a multicenter validation.Journal of echocardiography · 2026Article
- Early Prediction of Cardiac Arrest Based on Time-Series Vital Signs Using Deep Learning: Retrospective Study.JMIR formative research · 2026Article
- Establishment and validation of a machine learning-based predictive model for in-hospital mortality risk in acute myocardial infarction patients complicated with diabetes mellitus.Frontiers in cardiovascular medicine · 2026Article
- Association between dietary branched-chain amino acid intake and anaemia risk in children and adolescents: a matched case-control study.Frontiers in nutrition · 2026Article
- Operational characteristics of a hospital rapid response team for public-area emergencies: a retrospective study.Frontiers in medicine · 2026Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Evaluation of machine learning models for personalized prediction of benefit from temporary mechanical circulatory support after out-of-hospital cardiac arrest.European heart journal. Digital health · 2025Article
- Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.Scientific reports · 2025Article
- Enhancing Clinical Cardiac Care: Predicting In-Hospital Cardiac Arrest With Machine Learning.Annals of laboratory medicine · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma.Scientific reports · 2025Article
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters.Biomedical engineering online · 2023Article
- Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review.Journal of clinical medicine · 2023Review
- Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.NPJ digital medicine · 2023Article
- Article
- Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department.Scientific reports · 2023Article
- Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.Scientific reports · 2023Observational
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPrevious studies have used machine leaning to predict clinical deterioration to improve outcome prediction. However, no study has used machine learning to predict cardiac arrest in patients with acute coronary syndrome (ACS). Algorithms are required to generate high-performance models for predicting cardiac arrest in ACS patients with multivariate features. HYPOTHESIS: Machine learning algorithms will significantly improve outcome prediction of cardiac arrest in ACS patients.
methodsThis retrospective cohort study reviewed 166 ACS patients who had in-hospital cardiac arrest. Eight machine learning algorithms were trained using multivariate clinical features obtained 24 h prior to the onset of cardiac arrest. All machine learning models were compared to each other and to existing risk prediction scores (Global Registry of Acute Coronary Events, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve (AUROC).
resultsThe XGBoost model provided the best performance with regard to AUC (0.958 [95%CI: 0.938-0.978]), accuracy (88.9%), sensitivity (73%), negative predictive value (89%), and F1 score (80%) compared with other machine learning models. The K-nearest neighbor model generated the best specificity (99.3%) and positive predictive value (93.8%) metrics, but had low and unacceptable values for sensitivity and AUC. Most, but not all, machine learning models outperformed the existing risk prediction scores.
conclusionsThe XGBoost model, which was generated based on a machine learning algorithm, has high potential to be used to predict cardiac arrest in ACS patients. This proposed model significantly improves outcome prediction compared to existing risk prediction scores.
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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.