Department of General Surgery, 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. 2024 May 31;14(1):12507. doi: 10.1038/s41598-024-63437-6.
Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857-0.874) in the training dataset and 0.861 (95% CI 0.848-0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.
阿司匹林广泛用于预防和治疗多种血管疾病，如中风和冠心病。但目前尚不清楚降低血管疾病风险和潜在出血风险增加之间的最佳平衡。本研究旨在开发一种专门用于评估使用阿司匹林的个体出血风险的预测模型。
这项研究共纳入了 58415 名使用阿司匹林治疗的患者。从温州医科大学附属东阳医院收集了患者的人口统计学数据、临床特征、合并症、病史和实验室检查结果等详细数据。患者以 7:3 的比例随机分为两组。较大的组用于模型开发，较小的组用于内部验证。
为了开发预测模型，我们采用最小绝对收缩和选择算子（LASSO）回归，然后进行多变量逻辑回归。通过评估受试者工作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）等指标来评估模型的性能。
该研究采用的 LASSO 衍生模型纳入了 6 个变量，包括性别、手术、既往出血、血红蛋白、血小板计数和脑梗死。该模型在预测阿司匹林使用者出血风险方面表现出色，在训练数据集中的 AUC 为 0.866（95%CI 0.857-0.874），在测试数据集中的 AUC 为 0.861（95%CI 0.848-0.875）。在截断值为 0.047 时，该模型具有中等的敏感性（83.0%）和特异性（73.9%）。校准曲线分析表明，该列线图与理想曲线非常接近，提示校准良好。DCA 曲线显示了与列线图模型相关的良好临床净获益。
我们开发的 LASSO 衍生预测模型具有成为临床环境中预测出血的替代工具的潜力。
