ArticleBMC cardiovascular disorders2025
Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Prognostic value of the triglyceride-glucose index combined with atherogenic index of plasma for all-cause mortality in critically ill patients with chronic heart failure: a machine learning-driven retrospective cohort study.Lipids in health and disease · 2026Article
- An explainable machine learning framework integrating clinical and lipidomic signatures for early severity stratification of hypertriglyceridemic pancreatitis.Frontiers in medicine · 2026Article
- Development and internal validation of a nomogram and machine-learning models for postoperative recurrence in adult patients with chronic rhinosinusitis with nasal polyps.Frontiers in medicine · 2026Article
- LASSO-based nomogram and machine learning models for predicting 30-day nasogastric tube dependence after acute ischemic stroke.Frontiers in neurology · 2026Article
- AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights.Journal of clinical medicine · 2025Article
- Dynamic Predictive Models of Cardiogenic Shock in STEMI: Focus on Interventional and Critical Care Phases.Journal of clinical medicine · 2025Article
- A nomogram model based on HALP score and sST2 for predicting 1-year MACE risk after PCI in acute myocardial infarction patients.Frontiers in cardiovascular medicine · 2025Article
- Fluid balance trajectories and prognosis in patients with acute myocardial infarction complicated by cardiogenic shock: a group-based trajectory model approach.Frontiers in cardiovascular medicine · 2025Article
- Development and internal-external validation of a risk prediction nomogram for secondary myocardial injury in traumatic brain injury.Frontiers in neurologyArticle
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Abstract
backgroundTo evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to analyze the impact of these predictors on the patients' prognosis.
methodsA retrospective analysis was conducted on 332 adult patients who were diagnosed with AMI-CS and admitted to the ICU for the first time within the eICU Collaborative Research Database (eICU-CRD). AdaBoost, XGBoost, LightGBM, Random Forest and logistic regression nomogram were developed utilizing the random forest recursive elimination (RF-RFE) and least absolute shrinkage and selection operator (LASSO) algorithms for feature selection.
resultsCompared to the machine learning models, the nomogram demonstrated superior predictive accuracy for mortality in patients with AMI-CS, with an AUC value of 0.869 (95% CI: 0.803, 0.883) and an F1 score of 0.897 for the internal test set of nomogram, and an AUC of 0.770 (95% CI: 0.702, 0.801) and an F1 score of 0.832 for the external validation set.
conclusionsNomogram enhance the interpretability and transparency of the models, leading to more reliable prognostic predictions for AMI-CS patients. This facilitates clinicians in making precise decisions, thereby enhancing patient prognosis.
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