Academic Journal
Development and Validation of an Interpretable Machine Learning Model for Predicting 1-Year Cardiac Death After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: A Multicenter Study.
| Τίτλος: | Development and Validation of an Interpretable Machine Learning Model for Predicting 1-Year Cardiac Death After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: A Multicenter Study. |
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| Συγγραφείς: | Liu H; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China.; Department of Cardiology Heart Center, Inner Mongolia People's Hospital Hohhot China., Hu S; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Zhang Y; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Gu T; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Zhang J; Cardiovascular Research Institute University of California San Francisco CA USA., Wu X; Institute for Global Health Sciences University of California San Francisco CA USA., Liu X; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Shao X; NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology Tianjin Medical University Tianjin China., Wang L; Cardiology Department, Chest Hospital Tianjin University Tianjin China., Tse G; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China.; School of Nursing and Health Studies Hong Kong Metropolitan University Hong Kong China., Liu T; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Chen K; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China. |
| Πηγή: | Journal of the American Heart Association [J Am Heart Assoc] 2026 Jul 07; Vol. 15 (13), pp. e044796. Date of Electronic Publication: 2026 Jun 23. |
| Τύπος έκδοσης: | Journal Article; Multicenter Study; Validation Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101580524 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2047-9980 (Electronic) Linking ISSN: 20479980 NLM ISO Abbreviation: J Am Heart Assoc Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford : Wiley-Blackwell |
| Ιατρικοί όροι (MeSH): | Myocardial Infarction*/mortality , Myocardial Infarction*/surgery , Percutaneous Coronary Intervention*/adverse effects , Percutaneous Coronary Intervention*/mortality , Boosting Machine Learning Algorithms*, China/epidemiology ; Risk Assessment/methods ; Aged ; Female ; Humans ; Male ; Middle Aged ; Predictive Learning Models ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies ; Risk Factors ; Time Factors |
| Περίληψη: | Background: Risk prediction of cardiac death following percutaneous coronary intervention remains suboptimal in acute myocardial infarction. This study aimed to develop and externally validate an interpretable machine learning model using only routine laboratory and demographic variables to predict 1-year cardiac death in this population. Methods: We retrospectively enrolled 19 284 patients with acute myocardial infarction who underwent percutaneous coronary intervention across 82 hospitals in Tianjin, China between January 2010 and March 2024. The cohort was randomly split into training (70%, n=13 499) and internal validation (30%, n=5785) sets. An external cohort of 2048 patients from an independent center was used for validation. A Light Gradient-Boosting Machine model based solely on routinely available laboratory and demographic variables, with no imaging inputs, was developed and compared with GRACE (Global Registry of Acute Coronary Events) scores. Shapley Additive Explanations were used to assess model interpretability. Results: In the original data set, 1984 patients experienced 1-year cardiac death. The model achieved strong discrimination in internal validation (area under the curve 0.921, precision-recall area under the curve 0.711, sensitivity 79.7%). In the external validation cohort, LightGBM achieved a precision-recall area under the curve of 0.162 and significantly outperformed the GRACE score in discrimination (area under the curve, 0.811 versus 0.728; P=0.001). Complementary assessments of calibration and decision-curve analysis supported the overall findings. Conclusions: This interpretable machine learning model based exclusively on routine laboratory and demographic variables outperformed the GRACE score in predicting 1-year cardiac death after percutaneous coronary intervention in patients with acute myocardial infarction. Its strong discrimination and external validity support its potential for real-world risk stratification and individualized management. |
| Contributed Indexing: | Keywords: acute myocardial infarction; cardiac death; machine learning; percutaneous coronary intervention; routine laboratory tests |
| Entry Date(s): | Date Created: 20260623 Date Completed: 20260708 Latest Revision: 20260729 |
| Update Code: | 20260729 |
| DOI: | 10.1161/JAHA.125.044796 |
| PMID: | 42333686 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2047-9980 |
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| DOI: | 10.1161/JAHA.125.044796 |