Yifang Lu, Wanlin Lei, Maofeng Wang
Department of Medical Oncology, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, 322100, Zhejiang, China.
Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, 322100, Zhejiang, China.
Sci Rep. 2025 Feb 7;15(1):4624. doi: 10.1038/s41598-025-88327-3.
Aspirin is commonly utilized in the management and prevention of various diseases. However, in specific individuals, particularly those with low body mass index (BMI), aspirin can elevate the risk of bleeding. Achieving a delicate equilibrium between the desirable antiplatelet effects and potential bleeding complications is a notable consideration. The objective of this study was to create a novel bleeding risk prediction tool for aspirin users with a low BMI. A total of 2436 aspirin users with a low BMI were included in this study conducted at the Affiliated Dongyang Hospital of Wenzhou Medical University. Patient data, comprising demographics, clinical characteristics, comorbidities, medical history, and laboratory tests, were collected. The patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation purposes. The identification of clinically significant features associated with bleeding was achieved through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression and boruta analysis. Subsequently, these important features underwent multivariate logistic regression analysis. Based on independent bleeding risk factors, a logistic regression model was constructed and presented as a nomogram. Model performance was evaluated using metrics such as the area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC) in both the training and testing sets. LASSO analysis identified two clinical features, while Boruta analysis identified nine clinical features out of a total of 21 features. Subsequent multivariate logistic regression analysis selected significant independent risk factors. The boruta model, which demonstrated the highest AUC, consisted of six clinical variables: hemoglobin, platelet count, previous bleeding, tumor, smoke, and diabetes mellitus. These variables were integrated into a visually represented nomogram. The model exhibited an AUC of 0.832 (95% CI: 0.788-0.875) in the training dataset and 0.775 (95% CI: 0.698-0.853) in the test dataset, indicating excellent discriminatory performance. Calibration curve analysis revealed close alignment with the ideal curve. Furthermore, DCA, CIC, and NRC demonstrated favorable clinical net benefit for the model. This study has successfully created a novel risk prediction tool specifically designed for aspirin users with a low BMI. This tool enables the stratification of low BMI patients based on their anticipated bleeding risk.
阿司匹林常用于多种疾病的管理和预防。然而，在特定个体中，尤其是那些体重指数（BMI）较低的人，阿司匹林会增加出血风险。在理想的抗血小板作用和潜在的出血并发症之间实现微妙的平衡是一个值得关注的问题。本研究的目的是为BMI较低的阿司匹林使用者创建一种新型的出血风险预测工具。温州医科大学附属东阳医院开展的这项研究共纳入了2436名BMI较低的阿司匹林使用者。收集了患者的数据，包括人口统计学、临床特征、合并症、病史和实验室检查结果。为了进行模型开发和内部验证，患者被随机分为两组，比例为7:3。通过使用最小绝对收缩和选择算子（LASSO）回归和博鲁塔分析来识别与出血相关的临床显著特征。随后，对这些重要特征进行多变量逻辑回归分析。基于独立的出血风险因素，构建了一个逻辑回归模型并以列线图的形式呈现。在训练集和测试集中，使用曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）、临床影响曲线（CIC）和净减少曲线（NRC）等指标评估模型性能。LASSO分析确定了两个临床特征，而博鲁塔分析从总共21个特征中确定了9个临床特征。随后的多变量逻辑回归分析选择了显著的独立风险因素。表现出最高AUC的博鲁塔模型由六个临床变量组成：血红蛋白、血小板计数、既往出血史、肿瘤、吸烟和糖尿病。这些变量被整合到一个直观呈现的列线图中。该模型在训练数据集中的AUC为0.832（95%CI：0.788 - 0.875），在测试数据集中的AUC为0.775（95%CI：0.698 - 0.853），表明具有出色的区分性能。校准曲线分析显示与理想曲线紧密吻合。此外，DCA、CIC和NRC表明该模型具有良好的临床净效益。本研究成功创建了一种专门为BMI较低的阿司匹林使用者设计的新型风险预测工具。该工具能够根据低BMI患者预期的出血风险进行分层。
