Abstract: Objective: To analyze the current situation and risk factors of unplanned readmission of comprehensive ICU, and to develop and verify the risk predict model of unplanned readmission to ICU. Methods: The data of patients discharged from comprehensive ICU of a tertiary grade A hospital in Zhenjiang from January 2015 to April 2019 was collected. The data was divided into modeling set and validation set. Binary Logistics regression was used to analysis the data, and the model was examined by ROC curve. The grouped data was used to verify the sensitivity and specificity of the model. Results: A total of 805 patients were included, 42 patients readmitted to the ICU after the initial transfer within 30 days. The readmission rate was 5.2%. The main cause of readmission was respiratory failure. The results of binary Logistics regression showed that age (OR=1.029), admission diagnosis of respiratory diseases (OR=5.625), secondary intubation (OR=12.290), and SOFA score (OR=1.368) are independent risk factors for unplanned readmission to ICU (P<0.05), AUC=0.884. The maximum value of the Youden index was 0.658, and the corresponding sensitivity was 91.9%, the specificity was 73.9%. The cutoff value was 0.033. The independent data verification result showed that AUC=0.806, the sensitivity was 80.0%, and the specificity was 62.3%. Conclusion: The risk prediction model had a good predictive effect on the risk of unplanned readmission to comprehensive ICU, which can evaluate the risk before transferring from the ICU, and provide reference for preventive treatment and nursing intervention.

Key words: comprehensive ICU; unplanned readmission to ICU; risk predict model