Stroke prognostication for discharge planning with machine learning: A derivation study. (September 2020)

• Record Type: Journal Article
• Authors: Bacchi, Stephen; Oakden-Rayner, Luke; Menon, David K.; Jannes, Jim; Kleinig, Timothy; Koblar, Simon
• Is Part Of: Journal of clinical neuroscience. Volume 79(2020)
• Page Start: 100
• Page End: 103
• Publication Date: 2020-09
• Subjects: Machine learning -- Artificial intelligence -- Predictive analytics -- Neural network -- Logistic regression; Brain -- Surgery -- Periodicals; Neurosciences -- Periodicals; Nervous system -- Surgery -- Periodicals; Brain -- surgery -- Periodicals; Neurosurgical Procedures -- Periodicals; Neurosciences -- Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.jocn.2020.07.046
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: Machine learning may aid in the prediction of post-stroke outcomes relevant to discharge planning. Logistic regression and artificial neural networks may be effective in this task. Validation studies with data from multiple sites are required. Future studies may aim to assess for benefits in patient-oriented outcomes with model deployment. Abstract: Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data to predict the likely length of stay (LOS) for patients admitted with stroke. Secondary aims included the prediction of discharge modified Rankin Scale (mRS), in-hospital mortality, and discharge destination. In this study a retrospective dataset was used to develop and test a variety of machine learning models. The patients included in the study were all stroke admissions (both ischaemic stroke and intracerebral haemorrhage) at a single tertiary hospital between December 2016 and September 2019. The machine learning models developed and tested (75%/25% train/test split) included logistic regression, random forests, decision trees and artificial neural networks. The study included 2840 patients. In LOS prediction the highest area under the receiver operator curve (AUC) was achieved on the unseen test dataset by an artificial neural network at 0.67. Higher AUC were achieved using logistic regression models in the prediction of discharge functional independence (mRS ≤2) (AUC 0.90) and in the prediction of in-hospital mortality (AUC 0.90). Logistic regression was also the best performing model for predicting home vs non-home discharge destination (AUC 0.81). This study indicates that machine learning may aid in the prognostication of factors relevant to post-stroke discharge planning. Further prospective and external validation is required, as well as assessment of the impact of subsequent implementation.