目的:准确预测生存预后有助于指导临床决策。本研究的目的是开发一种使用机器学习技术来预测老年心房颤动(AF)患者复合血栓栓塞事件(CTE)发生的模型。这些事件包括新诊断的脑缺血事件,心血管事件,肺栓塞,下肢动脉栓塞.
方法:本回顾性研究纳入2010年1月至2022年6月解放军总医院收治的6,079例老年房颤住院患者(≥75岁)。随机森林插补用于处理丢失的数据。在描述性统计部分,根据CTE的发生将患者分为两组,并对两组之间的差异进行了分类变量的卡方检验和连续变量的秩和检验。在机器学习部分,以7:3的比例将患者随机分为训练数据集(n=4,225)和验证数据集(n=1,824).四种机器学习模型(逻辑回归,决策树,随机森林,XGBoost)在训练数据集上进行了训练,并在验证数据集上进行了验证。
结果:复合血栓栓塞事件的发生率为19.53%。最小绝对收缩和选择算子(LASSO)方法,使用5倍交叉验证,将其应用于训练数据集,并确定了总共18个与CTE发生显著相关的特征。随机森林模型在曲线下面积方面优于其他模型(ACC:0.9144,SEN:0.7725,SPE:0.9489,AUC:0.927,95%CI:0.9105-0.9443)。基于临床决策曲线的随机森林模型也显示出良好的临床有效性。Shapley加法移植(SHAP)显示,与模型相关的前五个特征是缺血性卒中病史,高甘油三酯(TG),高总胆固醇(TC),血浆D-二聚体高,年龄。
结论:本研究提出了一个准确的模型来对CTE高危患者进行分层。随机森林模型具有良好的性能。缺血性卒中病史,年龄,高TG,高TC和高血浆D-二聚体可能与CTE相关。
OBJECTIVE: Accurate prediction of survival prognosis is helpful to guide clinical decision-making. The aim of this study was to develop a model using machine learning techniques to predict the occurrence of composite thromboembolic events (CTEs) in elderly patients with atrial fibrillation(AF). These events encompass newly diagnosed cerebral ischemia events, cardiovascular events, pulmonary embolism, and lower extremity arterial embolism.
METHODS: This retrospective study included 6,079 elderly hospitalized patients (≥ 75 years old) with AF admitted to the People\'s Liberation Army General Hospital in China from January 2010 to June 2022. Random forest imputation was used for handling missing data. In the descriptive statistics section, patients were divided into two groups based on the occurrence of CTEs, and differences between the two groups were analyzed using chi-square tests for categorical variables and rank-sum tests for continuous variables. In the machine learning section, the patients were randomly divided into a training dataset (n = 4,225) and a validation dataset (n = 1,824) in a 7:3 ratio. Four machine learning models (logistic regression, decision tree, random forest, XGBoost) were trained on the training dataset and validated on the validation dataset.
RESULTS: The incidence of composite thromboembolic events was 19.53%. The Least Absolute Shrinkage and Selection Operator (LASSO) method, using 5-fold cross-validation, was applied to the training dataset and identified a total of 18 features that exhibited a significant association with the occurrence of CTEs. The random forest model outperformed other models in terms of area under the curve (ACC: 0.9144, SEN: 0.7725, SPE: 0.9489, AUC: 0.927, 95% CI: 0.9105-0.9443). The random forest model also showed good clinical validity based on the clinical decision curve. The Shapley Additive exPlanations (SHAP) showed that the top five features associated with the model were history of ischemic stroke, high triglyceride (TG), high total cholesterol (TC), high plasma D-dimer, age.
CONCLUSIONS: This study proposes an accurate model to stratify patients with a high risk of CTEs. The random forest model has good performance. History of ischemic stroke, age, high TG, high TC and high plasma D-Dimer may be correlated with CTEs.