A Deep Learning Model for Predicting ICU Discharge Readiness and Estimating Excess ICU Stay Duration

Abstract

Objective: In the complex landscape of ICU operations, accurate discharge decisions are crucial yet challenging, as premature discharge risks readmission and mortality while prolonged stays consume resources and heighten infection risk. The objective of this work is to develop a deep learning-based Discharge Readiness Score (DRS) model using minimal clinical features to predict ICU discharge readiness, and to highlight its application in estimating excess ICU stays for resource optimization. Methods and procedures: We utilized nearly 1.8 million ICU patient-stays from 2007–2023 across 300 US hospitals in the Philips eICU database. Six readily available features (age, mean arterial pressure, systolic pressure, heart rate, respiratory rate, and Glasgow Coma Scale) were used as inputs. A 5-layer neural network predicted patient mortality within 48 hours post-ICU discharge as a proxy for discharge readiness. The model was trained on 80% of data, validated on 10%, and tested on 10% (approximately 180,000 patients). We applied the model hourly to estimate excess ICU stays, defining excess stay as the time patients remained at low risk but continued in ICU. Results: The model achieved an AUC of 0.93 on the test set. Performance was consistent across years, ethnicities, ICU types, and admission groups. Using the model, we found that about 22% of patients had excess ICU time, with a median of 16 hours. The analysis highlighted trends over time and across ICU types, providing insights into resource utilization. Conclusion: The DRS model effectively predicts ICU discharge readiness using minimal features and can estimate excess ICU stays, aiding resource optimization. Clinical Impact— The model offers a practical tool for ICU discharge planning and resource utilization analysis, potentially improving patient outcomes and ICU operations