Prospective and external validation of stroke discharge planning machine learning models. (February 2022)

• Record Type: Journal Article
• Authors: Bacchi, Stephen; Oakden-Rayner, Luke; Menon, David K; Moey, Andrew; Jannes, Jim; Kleinig, Timothy; Koblar, Simon
• Is Part Of: Journal of clinical neuroscience. Volume 96(2022)
• Page Start: 80
• Page End: 84
• Publication Date: 2022-02
• Subjects: Logistic regression -- Artificial neural network -- Predictive analytics -- Artificial intelligence -- Length of stay -- Functional independence; 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.2021.12.031
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: Validation of models for post-stroke outcome prediction for discharge planning. Similar performance was achieved on prospective and external datasets. Improved outcomes with model implementation have not yet been demonstrated. Implementation studies are required. Abstract: Machine learning may be able to help with predicting factors that aid in discharge planning for stroke patients. This study aims to validate previously derived models, on external and prospective datasets, for the prediction of discharge modified Rankin scale (mRS), discharge destination, survival to discharge and length of stay. Data were collected from consecutive patients admitted with ischaemic or haemorrhagic stroke at the Royal Adelaide Hospital from September 2019 to January 2020, and at the Lyell McEwin Hospital from January 2017 to January 2020. The previously derived models were then applied to these datasets with three pre-defined cut-off scores (high-sensitivity, Youden's index, and high-specificity) to return indicators of performance including area under the receiver operator curve (AUC), sensitivity and specificity. The number of individuals included in the prospective and external datasets were 334 and 824 respectively. The models performed well on both the prospective and external datasets in the prediction of discharge mRS ≤ 2 (AUC 0.85 and 0.87), discharge destination to home (AUC 0.76 and 0.78) and survival to discharge (AUC 0.91 and 0.92). Accurate prediction of length of stay with only admission data remains difficult (AUC 0.62 and 0.66). This study demonstrates successful prospective and external validation of machine learning models using six variables to predict information relevant to discharge planning for stroke patients. Further research is required to demonstrate patient or system benefits following implementation of these models.