• Henry de Berker, Archy de Berker, Htin Aung, Pedro Duarte, Salman Mohammed, Hamsaraj Shetty, and Tom Hughes.
• Royal Manchester Children's Hospital, Manchester, UK h.deberker@doctors.org.uk.
• Clin Med (Lond). 2021 Mar 1; 21 (2): e186e191e186-e191.

Background And RationaleReliable prediction of discharge destination in acute stroke informs discharge planning and can determine the expectations of patients and carers. There is no existing model that does this using routinely collected indices of pre-morbid disability and stroke severity.MethodsAge, gender, pre-morbid modified Rankin Scale (mRS) and National Institutes of Health Stroke Scale (NIHSS) were gathered prospectively on an acute stroke unit from 1,142 consecutive patients. A multiclass random forest classifier was used to train and validate a model to predict discharge destination.ResultsUsed alone, the mRS is the strongest predictor of discharge destination. The NIHSS is only predictive when combined with our other variables. The accuracy of the final model was 70.4% overall with a positive predictive value (PPV) and sensitivity of 0.88 and 0.78 for home as the destination, 0.68 and 0.88 for continued inpatient care, 0.7 and 0.53 for community hospital, and 0.5 and 0.18 for death, respectively.ConclusionPre-stroke disability rather than stroke severity is the strongest predictor of discharge destination, but in combination with other routinely collected data, both can be used as an adjunct by the multidisciplinary team to predict discharge destination in patients with acute stroke.© Royal College of Physicians 2021. All rights reserved.

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