Multicenter validation of a machine learning model to predict intensive care unit readmission within 48 hours after discharge

Background: Intensive care unit (ICU) readmission is a crucial indicator of patient safety. However, discharge decisions often rely on subjective assessment due to a lack of standardized guidelines. We aimed to develop a machine-learning model to predict ICU readmission within 48 h and compare its performance to traditional scoring systems. Methods: We developed an ensemble model, iREAD, that generates a probability score at ICU discharge, representing the likelihood of the patient being readmitted to the ICU within 48 h, using data from Seoul National University Hospital (SNUH) and validated it using the MIMIC-III and eICU-CRD datasets. From September 2007 to August 2021, a total of 70,842 patients were included from SNUH. The MIMIC-III datasets comprised 43,237 patients admitted to ICUs between 2001 and 2012 at Beth Israel Deaconess Medical Center, and the eICU-CRD datasets included 90,271 ICU admissions across 208 hospitals between 2014 and 2015. Patients younger than 18, those who died in ICUs, or who refused life-sustaining treatment were excluded from the final analysis. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared to the traditional scores and conventional machine learning models. Kaplan-Meier analysis was performed to compare the outcome between the high-risk and low-risk groups. Findings: We developed the iREAD, that utilized 30 input features, encompassing demographics, length of stay, vital signs, GCS, and laboratory values. iREAD demonstrated superior performance compared with other models across all cohorts (all P < 0.001). In the internal validation, iREAD achieved AUROCs of 0.771 (95% CI 0.743-0.798), 0.834 (0.821-0.846), and 0.820 (0.808-0.832) for early (≤48 h), late (>48 h), and overall ICU readmissions, respectively. External validations with MIMIC-III and eICU-CRD also showed modest performance with AUROCs of 0.768 (0.748-0.787) and 0.725 (0.712-0.739) for overall readmission in MIMIC-III and eICU-CRD respectively, demonstrating superior performance compared to other models (All P < 0.001; higher than other models). Kaplan-Meier analysis revealed that over 40% of high-risk patients predicted by iREAD were readmitted within 48 h, representing a more than four-fold increase in predictive performance compared to the traditional scores. Interpretation: iREAD demonstrates superior performance in predicting ICU readmission within 48 h after discharge compared to traditional scoring systems or conventional machine learning models in both internal and external validations. While the performance degradation observed in the external validations suggests the need for further prospective validation on diverse patient populations, the robust performance and ability to identify high-risk patients have the potential to guide clinical decision-making. Funding: This work was supported by the Korea Health Technology Research & Development Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant number RS-2021-KH114109).