AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study

TL;DR

This study shows that an AI model called GRU-D++ can better predict ICU readmission or death after discharge than traditional tools, helping improve patient care decisions.

Contribution

The study introduces GRU-D++, a deep learning model that outperforms the SWIFT score in predicting ICU readmission and mortality after discharge.

Findings

GRU-D++ achieved an AUROC of 0.802 in internal validation and 0.756 in external validation.

The model outperformed the traditional SWIFT score in predicting ICU readmission or death within seven days of discharge.

Abstract

Background/Objectives: The transition from the intensive care unit (ICU) to the hospital ward is a critical high-risk period for patients. Early ICU discharge reduces costs and frees up ICU resources but can lead to readmission or unexpected death if patients are discharged prematurely. Despite the availability of risk stratification tools such as the Stability and Workload Index for Transfer (SWIFT) score, predicting ICU readmission remains challenging and inconsistent. However, artificial intelligence (AI) and machine learning (ML) techniques have recently shown promise in improving clinical decision support systems, particularly in the ICU. This study aimed to identify the risk factors and assess the performance of AI models in predicting readmission or death within seven days of ICU discharge using the MIMIC-IV (between 2008 and 2019) and Kangwon National University Hospital (KNUH, between 1 January 2016 and 28 February 2023) databases. Methods: This retrospective cohort study utilized the MIMIC-IV database for model training and internal validation and the KNUH database for external validation. Various machine learning and deep learning models have been developed to predict ICU readmission or death within seven days of discharge. The performance of the primary model, GRU-D++, was compared to the SWIFT score. Statistical analysis focused on the area under the receiver operating characteristic curve (AUROC) data to evaluate model accuracy. Results: The GRU-D++ model outperformed the SWIFT score, achieving AUROC of 0.802 and 0.756 for internal and external validations, respectively. Both datasets demonstrated that the GRU-D++ model provided better predictive performance for ICU readmission or death within seven days than the traditional SWIFT score. Conclusions: Our findings suggest that the GRU-D++ deep learning model is a valuable tool for the early detection of patient deterioration after ICU discharge, potentially aiding the prevention of ICU readmission. This study highlights the potential of AI to improve clinical decision-making in intensive care settings.

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