ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Clinical and Translational Cardiovascular Medicine
Risk Factors for Hematuria during Indwelling Urinary Catheterization in Acute Myocardial Infarction: A Comparative Analysis Using Logistic Regression and Decision Tree
Provisionally accepted
- Heyuan People's Hospital, Heyuan, China
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Objective : This study aimed to systematically identify risk factors for urinary catheter-related hematuria in patients with acute myocardial infarction (AMI). By integrating logistic regression and decision tree models, we sought to develop actionable strategies for risk stratification and complication prevention. Methods:A retrospective analysis of 209 AMI patients was conducted to evaluate predictors of hematuria, including demographics, coagulation indices (INR, platelets), and procedural variables. Logistic regression and decision tree (CART algorithm) models were employed to identify risk factors and their interactions. Model performance was assessed using ROC-AUC, sensitivity, and specificity. Results :The incidence of catheter-related hematuria was 32.5%. Both models identified persistent agitation during catheter indwelling and PLT≤246 as common predictors. The logistic regression model specifically identified Gender (OR=0.202), patient awareness of catheter purpose and precautions (OR=0.470), and emergency catheter placement (OR=2.257) as significant factors. The decision tree model uniquely identified INR > 0.955 and repeated complaints of urethral pain as predictors.. Conclusion :Hematuria in AMI patients results from coagulation dysfunction, procedural trauma, and behavioral factors. The combined use of logistic regression and decision trees enhances risk stratification. Clinical strategies should prioritize gentle catheterization, dynamic coagulation monitoring, and patient education to reduce complications.
Keywords: acute myocardial infarction, Hematuria, Logistic regression, DecisionTree, machine learning
Received: 21 Mar 2025; Accepted: 04 Nov 2025.
Copyright: © 2025 Zhang, Huang, Peng, Huang, Chen and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jia Zhang, 562232317@qq.com
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