ArticleCardiovascular diabetology2023
Machine learning-based models to predict one-year mortality among Chinese older patients with coronary artery disease combined with impaired glucose tolerance or diabetes mellitus.
Article in Cardiovascular diabetology, 2023. 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.
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16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Risk prediction models for acute kidney injury in patients with acute pancreatitis: a systematic review.BMC gastroenterology · 2026Pooled it
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Leveraging interpretable machine learning to identify sarcopenia in middle-aged and older adults with intrinsic capacity decline: an analysis of CHARLS data under AWGS 2025.BMC medical informatics and decision making · 2026Article
- Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.The international journal of cardiovascular imaging · 2026Article
- Prognostic stratification of cardiovascular risk and cardiac remodeling in prediabetes: a multimodal analysis comparing ADA and WHO/IEC diagnostic criteria.Cardiovascular diabetology · 2026Observational
- Machine learning predicts severe adverse events and salvage success of CT-guided lung biopsy after nondiagnostic transbronchial lung biopsy.European radiology · 2026Article
- Serum peptidomics by MALDI-TOF MS coupled with machine learning approaches for diagnosis of primary liver cancer.Analytical and bioanalytical chemistry · 2026Article
- Development and validation of the C-reactive protein-triglyceride-glucose index for predicting short- and long-term mortality in critically ill patients with coronary artery disease: a multicenter cohort study.Frontiers in cardiovascular medicine · 2026Article
- Machine learning-based model to predict liposuction outcomes in unilateral breast cancer-related lymphedema.Frontiers in oncology · 2026Article
- Multimodal Data-Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, Cross-Regional Cohort Study.Journal of medical Internet research · 2025Article
- An ensemble machine learning-based risk stratification tool for 30-day mortality prediction in critically ill cardiovascular patients.Cardiovascular diabetology · 2025Article
- Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models.Journal of cardiovascular development and disease · 2025Review
- Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.BMC geriatrics · 2025Article
- Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms.Scientific reports · 2025Article
- Exosomal non-coding RNAs: key molecules in the diagnosis and treatment of coronary artery disease.PeerJ · 2025Review
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Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAn accurate prediction of survival prognosis is beneficial to guide clinical decision-making. This prospective study aimed to develop a model to predict one-year mortality among older patients with coronary artery disease (CAD) combined with impaired glucose tolerance (IGT) or diabetes mellitus (DM) using machine learning techniques.
methodsA total of 451 patients with CAD combined with IGT and DM were finally enrolled, and those patients randomly split 70:30 into training cohort (n = 308) and validation cohort (n = 143).
resultsThe one-year mortality was 26.83%. The least absolute shrinkage and selection operator (LASSO) method and ten-fold cross-validation identified that seven characteristics were significantly associated with one-year mortality with creatine, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and chronic heart failure being risk factors and hemoglobin, high density lipoprotein cholesterol, albumin, and statins being protective factors. The gradient boosting machine model outperformed other models in terms of Brier score (0.114) and area under the curve (0.836). The gradient boosting machine model also showed favorable calibration and clinical usefulness based on calibration curve and clinical decision curve. The Shapley Additive exPlanations (SHAP) found that the top three features associated with one-year mortality were NT-proBNP, albumin, and statins. The web-based application could be available at https://starxueshu-online-application1-year-mortality-main-49cye8.streamlitapp.com/ .
conclusionsThis study proposes an accurate model to stratify patients with a high risk of one-year mortality. The gradient boosting machine model demonstrates promising prediction performance. Some interventions to affect NT-proBNP and albumin levels, and statins, are beneficial to improve survival outcome among patients with CAD combined with IGT or DM.
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