ArticleClinical cardiology2019
Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome.
Article in Clinical cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.PloS one · 2021Trial
- Development and validation of a long-term mortality prediction model in acute coronary syndrome survivors: a study of a predominantly male, lower-risk cohort with the capacity to complete cardiopulmonary exercise testing.BMC cardiovascular disorders · 2025Article
- Article
- Machine Learning Models for Predicting in-Hospital Cardiac Arrest: A Comparative Analysis with Logistic Regression.International journal of general medicine · 2025Article
- Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.Scientific reports · 2024Article
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- Lifestyle Influence on Mild Cognitive Impairment Progression: A Decision Tree Prediction Model Study.Neuropsychiatric disease and treatment · 2024Article
- Physical activity, sleep disorders, and type of work in the prevention of cognitive function decline in patients with hypertension.BMC public health · 2023Article
- A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease.BMC pediatrics · 2022Article
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Data mining in clinical big data: the frequently used databases, steps, and methodological models.Military Medical Research · 2021Review
- Analysis of hospitalization costs related to fall injuries in elderly patients.World journal of clinical cases · 2021Article
- Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model.World journal of clinical cases · 2020Article
- Influence of Lifestyles on Mild Cognitive Impairment: A Decision Tree Model Study.Clinical interventions in aging · 2020Article
- Decision tree model for predicting in-hospital cardiac arrest among patients admitted with acute coronary syndrome.Clinical cardiology · 2019Article
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7 authors.
Funding
Abstract
backgroundIn-hospital cardiac arrest (IHCA) may be preventable, with patients often showing signs of physiological deterioration before an event. Our objective was to develop and validate a simple clinical prediction model to identify the IHCA risk among cardiac arrest (CA) patients hospitalized with acute coronary syndrome (ACS). HYPOTHESIS: A predicting model could help to identify the risk of IHCA among patients admitted with ACS.
methodsWe conducted a case-control study and analyzed 21 337 adult ACS patients, of whom 164 had experienced CA. Vital signs, demographic, and laboratory data were extracted from the electronic health record. Decision tree analysis was applied with 10-fold cross-validation to predict the risk of IHCA.
resultsThe decision tree analysis detected seven explanatory variables, and the variables' importance is as follows: VitalPAC Early Warning Score (ViEWS), fatal arrhythmia, Killip class, cardiac troponin I, blood urea nitrogen, age, and diabetes. The development decision tree model demonstrated a sensitivity of 0.762, a specificity of 0.882, and an area under the receiver operating characteristic curve (AUC) of 0.844 (95% CI, 0.805 to 0.849). A 10-fold cross-validated risk estimate was 0.198, while the optimism-corrected AUC was 0.823 (95% CI, 0.786 to 0.860).
conclusionsWe have developed and internally validated a good discrimination decision tree model to predict the risk of IHCA. This simple prediction model may provide healthcare workers with a practical bedside tool and could positively impact decision-making with regard to deteriorating patients with ACS.
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