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Stroke prognostication for discharge planning with machine learning: A derivation study.

Stephen Bacchi¹, Luke Oakden-Rayner², David K Menon³

¹Royal Adelaide Hospital, Adelaide, SA 5000, Australia; University of Adelaide, Adelaide, SA 5005, Australia; South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia.

Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia

|October 19, 2020

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This summary is machine-generated.

Machine learning models can predict stroke outcomes like length of stay and functional independence. These tools show promise for improving post-stroke discharge planning and patient care.

Area of Science:

Neurology
Medical Informatics
Artificial Intelligence

Background:

Accurate early prognostication is crucial for effective post-stroke discharge planning.
Machine learning (ML) offers potential for improving predictive accuracy in stroke care.

Purpose of the Study:

To evaluate ML models for predicting length of stay (LOS) in stroke patients.
To assess ML model performance for predicting discharge modified Rankin Scale (mRS), in-hospital mortality, and discharge destination.

Main Methods:

Retrospective analysis of 2840 stroke admissions (ischaemic and intracerebral haemorrhage).
Development and testing of logistic regression, random forests, decision trees, and artificial neural networks.
75%/25% train/test split on admission data.

Keywords:

Artificial intelligence Logistic regression Machine learning Neural network Predictive analytics

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Main Results:

Artificial neural networks achieved an AUC of 0.67 for LOS prediction.
Logistic regression models demonstrated high AUCs (0.90) for predicting functional independence (mRS ≤2) and in-hospital mortality.
Logistic regression also performed well (AUC 0.81) in predicting discharge destination (home vs. non-home).

Conclusions:

Machine learning models show potential to aid in prognostication for post-stroke discharge planning.
Further prospective validation and implementation studies are necessary to confirm clinical utility.