Seemub Zaman Chowdhury1,2, Prithvi Santana Baskar1,2, Sonu Bhaskar1,2,3,4,5. 1. Neurovascular Imaging Laboratory, Ingham Institute for Applied Medical Research, Clinical Sciences Stream, Sydney, New South Wales, Australia. 2. University of New South Wales (UNSW, South Western Sydney Clinical School, UNSW Medicine, Sydney, New South Wales, Australia. 3. Department of Neurology & Neurophysiology, Liverpool Hospital & South West Sydney Local Health District (SWSLHD, Sydney, New South Wales, Australia. 4. Stroke & Neurology Research Group, Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia. 5. NSW Brain Clot Bank, NSW Health Statewide Biobank and NSW Health Pathology, Sydney, New South Wales, Australia.
Abstract
BACKGROUND: The prehospital phase is critical in ensuring that stroke treatment is delivered quickly and is a major source of time delay. This study sought to identify and examine prehospital stroke workflow optimizations (PSWOs) and their impact on improving health systems, reperfusion rates, treatment delays, and clinical outcomes. METHODS: The authors conducted a systematic literature review and meta-analysis by extracting data from several research databases (PubMed, Cochrane, Medline, and Embase) published since 2005. We used appropriate key search terms to identify clinical studies concerning prehospital workflow optimization, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: The authors identified 27 articles that looked at the impact of prehospital workflow optimizations on time and treatment parameters; 26 were included in the meta-analysis. The PSWO were subgrouped into three categories: improved intravenous thrombolysis (IVT) triage, large-vessel occlusion (LVO) bypass, and mobile stroke unit (MSU). The salient findings are as follows: improved IVT triage led to significantly improved rates of IVT (relative risk [RR] = 1.80, 95% confidence interval [CI] = 1.18 to 2.75); however, MSU did not (RR = 1.22, 95% CI = 0.98 to 1.52). Improved IVT triage (standard mean difference [SMD] = -0.82, 95% CI = -1.32 to -0.32), LVO bypass (SMD = -0.80, 95% CI = -1.13 to -0.47), and MSU (SMD = -0.87, 95% CI = -1.57 to -0.17) were found to significantly reduce door-to-needle time for IVT. MSU was found to significantly reduce call-to-needle (SMD = -1.41, 95% CI = -1.94 to -0.88) and onset-to-needle (SMD = -1.15, 95% CI = -1.74 to -0.56) times for IVT. MSU additionally demonstrated significant reduction in door-to-perfusion (SMD = -0.72, 95% CI = -1.32 to -0.12) as well as call-to-perfusion (SMD = -0.73, 95% CI = -1.08 to -0.38) times for EVT. Finally, PSWO did not demonstrate significant improvements in rates of good functional outcome (RR = 1.04, 95% CI = 0.97 to 1.12) or mortality at 90 days (RR = 1.00, 95% CI = 0.76 to 1.31). CONCLUSIONS: This systematic review and meta-analysis found that PSWO significantly improves several time metrics related to stroke treatment leading to improvement in IVT reperfusion rates. Thus, the implementation of these measures in stroke networks is a promising avenue to improve an often-neglected aspect of the stroke response. However, the limited available data suggest functional outcomes and mortality are not significantly improved by PSWO; hence, further studies and improvement strategies vis-à-vis PSWOs are warranted.
BACKGROUND: The prehospital phase is critical in ensuring that stroke treatment is delivered quickly and is a major source of time delay. This study sought to identify and examine prehospital stroke workflow optimizations (PSWOs) and their impact on improving health systems, reperfusion rates, treatment delays, and clinical outcomes. METHODS: The authors conducted a systematic literature review and meta-analysis by extracting data from several research databases (PubMed, Cochrane, Medline, and Embase) published since 2005. We used appropriate key search terms to identify clinical studies concerning prehospital workflow optimization, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: The authors identified 27 articles that looked at the impact of prehospital workflow optimizations on time and treatment parameters; 26 were included in the meta-analysis. The PSWO were subgrouped into three categories: improved intravenous thrombolysis (IVT) triage, large-vessel occlusion (LVO) bypass, and mobile stroke unit (MSU). The salient findings are as follows: improved IVT triage led to significantly improved rates of IVT (relative risk [RR] = 1.80, 95% confidence interval [CI] = 1.18 to 2.75); however, MSU did not (RR = 1.22, 95% CI = 0.98 to 1.52). Improved IVT triage (standard mean difference [SMD] = -0.82, 95% CI = -1.32 to -0.32), LVO bypass (SMD = -0.80, 95% CI = -1.13 to -0.47), and MSU (SMD = -0.87, 95% CI = -1.57 to -0.17) were found to significantly reduce door-to-needle time for IVT. MSU was found to significantly reduce call-to-needle (SMD = -1.41, 95% CI = -1.94 to -0.88) and onset-to-needle (SMD = -1.15, 95% CI = -1.74 to -0.56) times for IVT. MSU additionally demonstrated significant reduction in door-to-perfusion (SMD = -0.72, 95% CI = -1.32 to -0.12) as well as call-to-perfusion (SMD = -0.73, 95% CI = -1.08 to -0.38) times for EVT. Finally, PSWO did not demonstrate significant improvements in rates of good functional outcome (RR = 1.04, 95% CI = 0.97 to 1.12) or mortality at 90 days (RR = 1.00, 95% CI = 0.76 to 1.31). CONCLUSIONS: This systematic review and meta-analysis found that PSWO significantly improves several time metrics related to stroke treatment leading to improvement in IVT reperfusion rates. Thus, the implementation of these measures in stroke networks is a promising avenue to improve an often-neglected aspect of the stroke response. However, the limited available data suggest functional outcomes and mortality are not significantly improved by PSWO; hence, further studies and improvement strategies vis-à-vis PSWOs are warranted.
Authors: Verónica V Olavarría; Lorena Hoffmeister; Carolina Vidal; Alejandro M Brunser; Arnold Hoppe; Pablo M Lavados Journal: Front Neurol Date: 2022-04-08 Impact factor: 4.086