Abstract
Objective
The primary objective is to work towards a clinical decision support tool that can improve discharge practice on the intensive care unit.
Design
We used two datasets of routinely collected patient data to test and improve upon a set of previously proposed discharge criteria.
Setting
Bristol Royal Infirmary general intensive care unit (GICU).
Patients
Two cohorts derived from historical datasets: 1933 intensive care patients from GICU in Bristol, and 10658 from MIMIC-III (a publicly available intensive care dataset).
Interventions
None.
Primary outcome measure
None
Results In both cohorts few successfully discharged patients met the of all the discharge criteria. Both a random forest and a logistic classifier, trained on MIMIC and cross validated on GICU, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the NLD criteria according to feature importance from the logistic model we showed improved performance over the original NLD criteria, while retaining good interpretability.
Conclusions Our findings constitute a proof of concept for a decision support tool to run alongside a clinical information system, and streamline the process of discharge from the ICU.
Strengths and Limitations of this study
This study applies machine learning techniques to the problem of classifying patients that are ready for discharge from intensive care.
Two cohorts of historical data are used, allowing cross-validation and a comparison of results between healthcare contexts.
Our approach represents the first step towards a decision support tool that would help clinicians identify dischargeable patients as early as possible.