Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach

Abstract

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.

Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review

“…Collecting and utilizing such data may involve higher costs but hold the potential to improve model performance. Artificial intelligence (AI) and machine learning algorithms, including techniques such as Extreme Gradient Boosting, the Gaussian mixture model, Decision Tree, and Random Forest ( 50 – 53 ) have become increasingly prevalent in addressing such challenges. Machine learning with its remarkable capability to analyze extensive volumes of intricate data ( 54 ) holds tremendous potential for enhancing predictive performance and should see wider adoption in the medical field.…”