癌症患者的心血管疾病（CVD）可能会影响非预期的再入院风险，据报道，这些再入院费用高昂，与更糟糕的死亡率和预后相关。我们旨在展示使用机器学习技术在2017-2018年全国再入院数据库中预测因CVD而导致住院癌症患者180天内非预期再入院风险的可行性。我们包括了住院的癌症患者，结果是出院后180天内由于任何CVD而发生的非预期再入院。CVD包括房颤、冠心病、心力衰竭、中风、周围动脉疾病、心脏肥大和心肌病。我们实施了决策树（DT）、随机森林、极端梯度提升（XGBoost）和AdaBoost。准确率、精确率、召回率、F2分数和接收者操作特性曲线（AUC）用于评估模型的性能。在358,629名住院癌症患者中，5.86% （n = 21,021）经历了由任何CVD引起的非预期再入院。三种集成算法的性能优于决策树，其中XGBoost的性能最佳。我们发现住院时间、年龄和癌症手术是癌症患者心血管疾病相关的非预期住院的重要预测因素。机器学习模型可以预测住院癌症患者因CVD而引起的非预期再入院的风险。© 2023年 Springer Nature Limited.

Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017-2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model's performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.© 2023. Springer Nature Limited.