Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

Abstract

Objective: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.

Design: We used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.

Setting: Bristol Royal Infirmary general intensive care unit (GICU).

Patients: Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.

Results: In both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, 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 discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.

Conclusions: Our findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

Keywords: cinical audit; health informatics; information technology.

Figure 1

Performance of the nurse-led discharge criteria and random forest with extended feature set (RF_ext) evaluated on held-out data for a single train-test split. Left: receiver-operator-characteristic curves with associated area-under-curve scores. Right: precision-recall curves. AUC, area-under-curve; GICU, general intensive care unit; NLD, nurse-led discharge; RF, random forest.