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• W3205102007 abstract "Background and Aims: To determine whether a step-down acute neurorehabilitation model targeting medically stable patients during COVID-19 and winter pressures improves patient flow from the Hyperacute stroke unit (HASU) to the community. Methods: Setting: 6 bedded neurorehabilitation unit, St Pancras Hospital, London, UK. Period: January 4th-April 1st 2021. We undertook daily (weekday) HASU review, accepting patients 7 days/week. We recorded therapy interventions as per stroke rehabilitation NICE guidance (2013) and Royal College of Physicians National clinical guidance for stroke (2016), Patient Categorisation Assessment Tool (PCAT) score, Rehabilitation Complexity Score (RCS), pathway diverted from, and HASU length of stay (LOS). Results:We admitted 80/123 patients from 16 different boroughs. Mean age 72.3years (SD 17.3). 41% >80years. 38% transferred Friday-Sunday. 50(63%) with stroke. Average PCAT 26.4 (SD 3.6), RCS admission 10.3 (SD 1.8) improving to 5.6(SD 2.6) on discharge. Average NIHSS 8.9(SD 7.1). Pathway diverted from: acute stroke unit (ASU) 51(64%);local/level 1 neurorehabilitation 26(33%). Average LOS 14.5 days (SD 9.5);30%< 7days. MDT input: physiotherapy/occupational therapy: 76%, SLT: 43%;63%, specialist nursing;64%, behavioural management;45%, medical input 71% mostly risk factor modification, [CG(CLHNFT1] pain and/or tone management. 6 patients (7.5%) required acute medical readmission. Of those who would normally transfer to an ASU;82% were discharged home. HASU LOS was reduced compared with same period last year (3 v 4 days). Conclusions: Nearly two thirds of admitted patients would usually have transferred to an ASU pathway. 82% were discharged home. Acute neurorehabilitation pathways offer a valuable adjunct to acute stroke pathways beyond winter and COVID-19 pressures." @default.
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• W3205102007 date "2021-01-01" @default.
• W3205102007 modified "2023-10-18" @default.
• W3205102007 title "Acute neurorehabilitation stepdown beds. a north central london winter/COVID-19 model to optimise patient flow through the stroke pathway" @default.
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