Machine learning decision support model for discharge planning in stroke patients

Abstract

Background/aimEfficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission.DesignProspective observational study.MethodsA prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best‐suited predictor for discharge disposition, dichotomized into home and non‐home. To evaluate the accuracy, reliability and interpretability of the best‐performing models, we identified and analysed the features that had the greatest impact on the predictions.ResultsIn total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non‐home discharge. Our model predicting non‐home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia.ConclusionThe ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non‐home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision‐making.Relevance to Clinical PracticeThis study illustrates the characteristics and risk factors of non‐home discharge in patients with stroke, potentially contributing to the improvement of the discharge process.Reporting MethodSTROBE guidelines.

Emerging Post-Hospital Models of Care: A Primer for Stroke Center Leaders

“…While it was a small exploratory study, the intervention was positively perceived by both patients and rehabilitation providers, and improved functional ability defined as meeting rehabilitation goals 17 . Cui and colleagues 18 also reported applying machine learning to predict patients at increased need for support at discharge. Wireless remote monitoring devices are already being used to obtain "athome" physiologic parameters such as blood pressure, blood glucose, oxygen saturation and heart rhythm.…”