Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study

Background: Intensive care units (ICUs) harbor the sickest patients with the utmost needs of medical care. Discharge from ICU needs to consider the reason for admission and stability after ICU care. Organ dysfunction or instability after ICU discharge constitute potentially life-threatening situations for patients. Methods: This is a single center, observational, retrospective cohort study conducted at ICUs at the Kepler University Hospital in Linz, Austria. Patients aged 18 years and above admitted to the study center's ICUs between 2010 and 01-01 and 2019-10-31 were included in the study. Patients transferred to another ICU, discharged to a different hospital or home, or that died during their ICU stay were excluded. We used machine learning (ML) models to predict unplanned ICU readmission or death using an internal dataset or MIMIC-IV as training data and compared the models with the Stability and Workload Index for Transfer (SWIFT) score. Further, we evaluated the influence of features on the models using Shapley Additive Explanations. Results: The best ML models achieved an area under the curve of the receiver operating characteristic (AUC-ROC) of 0.721 +/- 0.029 and a high negative predictive value (NPV) of 0.990 +/- 0.002. The most important features were heart rate, peripheral oxygen saturation and arterial blood pressure. Performance of the SWIFT score was worse than the ML models (best AUC-ROC 0.618 +/- 0.011). Conclusions: ML models were able to identify patients that will not need unplanned ICU readmission and will not die within 48 h after discharge.