Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study
BackgroundGastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce...
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JMIR Publications
2025-01-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e67346 |
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| author | Yanqi Kou Shicai Ye Yuan Tian Ke Yang Ling Qin Zhe Huang Botao Luo Yanping Ha Liping Zhan Ruyin Ye Yujie Huang Qing Zhang Kun He Mouji Liang Jieming Zheng Haoyuan Huang Chunyi Wu Lei Ge Yuping Yang |
| author_facet | Yanqi Kou Shicai Ye Yuan Tian Ke Yang Ling Qin Zhe Huang Botao Luo Yanping Ha Liping Zhan Ruyin Ye Yujie Huang Qing Zhang Kun He Mouji Liang Jieming Zheng Haoyuan Huang Chunyi Wu Lei Ge Yuping Yang |
| author_sort | Yanqi Kou |
| collection | DOAJ |
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BackgroundGastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
ObjectiveThis study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
MethodsA multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables.
ResultsThe RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups.
ConclusionsThe ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies. |
| format | Article |
| id | doaj-art-1ee51e5c607f4414963088f6bd2fe745 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-1ee51e5c607f4414963088f6bd2fe7452025-01-30T20:31:41ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e6734610.2196/67346Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort StudyYanqi Kouhttps://orcid.org/0000-0001-8456-7474Shicai Yehttps://orcid.org/0009-0005-7663-7379Yuan Tianhttps://orcid.org/0009-0007-5053-5512Ke Yanghttps://orcid.org/0009-0004-9029-5996Ling Qinhttps://orcid.org/0009-0008-9175-8728Zhe Huanghttps://orcid.org/0009-0007-6634-0170Botao Luohttps://orcid.org/0009-0008-8060-2381Yanping Hahttps://orcid.org/0000-0002-8884-494XLiping Zhanhttps://orcid.org/0009-0001-0440-9095Ruyin Yehttps://orcid.org/0009-0001-0081-6232Yujie Huanghttps://orcid.org/0009-0000-9932-7473Qing Zhanghttps://orcid.org/0009-0006-5556-8543Kun Hehttps://orcid.org/0000-0003-2079-244XMouji Lianghttps://orcid.org/0009-0007-4370-6838Jieming Zhenghttps://orcid.org/0009-0007-0451-1608Haoyuan Huanghttps://orcid.org/0009-0001-5219-3620Chunyi Wuhttps://orcid.org/0009-0005-0518-8166Lei Gehttps://orcid.org/0000-0001-6867-8780Yuping Yanghttps://orcid.org/0000-0001-5597-1881 BackgroundGastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making. ObjectiveThis study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. MethodsA multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables. ResultsThe RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups. ConclusionsThe ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies.https://www.jmir.org/2025/1/e67346 |
| spellingShingle | Yanqi Kou Shicai Ye Yuan Tian Ke Yang Ling Qin Zhe Huang Botao Luo Yanping Ha Liping Zhan Ruyin Ye Yujie Huang Qing Zhang Kun He Mouji Liang Jieming Zheng Haoyuan Huang Chunyi Wu Lei Ge Yuping Yang Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study Journal of Medical Internet Research |
| title | Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study |
| title_full | Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study |
| title_fullStr | Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study |
| title_full_unstemmed | Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study |
| title_short | Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study |
| title_sort | risk factors for gastrointestinal bleeding in patients with acute myocardial infarction multicenter retrospective cohort study |
| url | https://www.jmir.org/2025/1/e67346 |
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