Objective:To construct a risk prediction model for unplanned readmission of patients undergoing cardiac resynchronization therapy (CRT) and verify the performance of the model.Methods:Using convenience sampling, patients who underwent CRT at the Department of Cardiovascular of the First Affiliated Hospital of Zhengzhou University from July 2017 to July 2020 were selected as the modeling group ( n=279) and the internal validation group ( n=120). CRT patients admitted to the Department of Cardiovascular of the First Affiliated Hospital of Zhengzhou University from August 2021 to August 2022 due to the same or related diseases were selected as the external validation group ( n=86). Multivariate Logistic regression was used to explore the influencing factors of unplanned readmission of CRT patients and establish the prediction model. The fitting effect and discrimination of the model were evaluated through the Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve. The nomogram was established based on R-4.1.2 and Rstudio software. Results:The multivariate Logistic regression analysis showed that creatinine, left atrial diameter, pulmonary artery systolic pressure, New York Heart Association (NYHA) classification, and body mass index (BMI) were risk factors for unplanned readmission in CRT patients, with statistically significant differences ( P<0.05). The prediction model formula was: P=1/{1+exp[- (0.792×creatinine+1.408×left atrial inner diameter+0.887×pulmonary artery systolic pressure+0.769×NYHA classification-0.970×BMI-2.266) ]}. The area under the ROC curve was 0.874, the maximum value of the Jordan index was 0.636, the optimal threshold was 0.256, the sensitivity was 0.826, and the specificity was 0.810. The accuracy of internal validation and external validation was 90.00% and 90.70%, respectively. Conclusions:The constructed prediction model for unplanned readmission of CRT patients has good predictive performance, and the visualized nomogram improves the practical performance of the model. It helps medical and nursing staff identify high-risk groups of unplanned readmission of CRT patients in the early stage and provides a basis for formulating nursing strategies for different risk groups.