Yuan Xu1, Xinlei Yang1, Hui Huang2, Chen Peng3, Yanqiu Ge3, Honghu Wu4, Jiajing Wang3, Gang Xiong1, Yingping Yi5. 1. Medical Big-Data Center, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi China. 2. Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi China. 3. School of Public Health, Medical School, Nanchang University, Nanchang, Jiangxi China. 4. Biobank Center , The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi China. 5. Medical Big-Data Center, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi China. Electronic address: yyp66@126.com.
Abstract
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to identify the ischemic stroke readmission risk factors and establish a 90-day readmission prediction model for first-time ischemic stroke patients. METHODS: The readmission prediction model was developed using the extreme gradient boosting (XGboost) model, which can generate an ensemble of classification trees and assign a predictive risk score to each feature. The patient data were split into a training set (5159) and a validation set (911). The prediction results were evaluated with the receiver operating characteristic (ROC) curve and time-dependent ROC curve, which were compared with the outputs from the logistic regression (LR) model. RESULTS: A total of 6070 adult patients (39.6% female, median age 67 years) without any ischemic attack (IS) history were included, and 520 (8.6%) were readmitted within 90 days. The XGboost-based prediction model achieved a standard area under the curve (AUC) value of .782 (.729-.834), and the best time-dependent AUC value was .808 in 54 days for the validation set. In contrast, the LR model yielded a standard AUC value of .771 (.714-.828) and best time-dependent AUC value of .797. CONCLUSIONS: The XGboost model obtained a better risk prediction for 90-day readmission for first-time ischemic stroke patients than the LR model. This model can also reveal the high risk factors for stroke readmission in first-time ischemic stroke patients.
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic strokepatients are usually higher than those of patients with other chronic diseases. Our aim was to identify the ischemic stroke readmission risk factors and establish a 90-day readmission prediction model for first-time ischemic strokepatients. METHODS: The readmission prediction model was developed using the extreme gradient boosting (XGboost) model, which can generate an ensemble of classification trees and assign a predictive risk score to each feature. The patient data were split into a training set (5159) and a validation set (911). The prediction results were evaluated with the receiver operating characteristic (ROC) curve and time-dependent ROC curve, which were compared with the outputs from the logistic regression (LR) model. RESULTS: A total of 6070 adult patients (39.6% female, median age 67 years) without any ischemic attack (IS) history were included, and 520 (8.6%) were readmitted within 90 days. The XGboost-based prediction model achieved a standard area under the curve (AUC) value of .782 (.729-.834), and the best time-dependent AUC value was .808 in 54 days for the validation set. In contrast, the LR model yielded a standard AUC value of .771 (.714-.828) and best time-dependent AUC value of .797. CONCLUSIONS: The XGboost model obtained a better risk prediction for 90-day readmission for first-time ischemic strokepatients than the LR model. This model can also reveal the high risk factors for stroke readmission in first-time ischemic strokepatients.