The scope for improvement in hyper-acute stroke care in Scotland

Abstract Thrombolysis is associated with reduced disability for selected patients who have suffered ischemic stroke. However only a fraction of all patients who have suffered this type of stroke receive thrombolysis. The short time window of 4.5 h in which treatment is licensed means that rapid care and well-organized pathways are essential. We studied measures to increase the uptake of thrombolysis through a better understanding of the hospital delays which lead to a lack of timely brain scanning and diagnosis. We examine the factors influencing the number of thrombolysed patients, the time between arrival at hospital and the administration of thrombolysis (door to needle time). Our analysis is based on the Scottish Stroke Care Audit (SSCA) data covering all stroke patients admitted to hospitals in Scotland in 2010, as well as on interviews with stroke care staff in Scotland. The data show significant variation in the speed of scanning, thrombolysis treatment and number of patients receiving treatment among hospitals. In the best performing hospital, 68% of patients arriving within 4 h of stroke onset are scanned in time for thrombolysis compared with 40% on average and 5% in the worst performing hospital. We model the system as a discrete-event simulation following the patient journey, starting when patients have a stroke and ending at thrombolysis for those who qualify. The simulation results show that just improving the performance of all hospitals to the level of the best performing hospital would (even without improvements in onset to arrival times) increase the thrombolysis rate from 6% (in 2010) to 11% of all admitted stroke patients in Scotland. By 2013 9% of patients were receiving thrombolysis, suggesting there is still room for improvement.

[1]  Leonid Churilov,et al.  Decision support in pre-hospital stroke care operations: A case of using simulation to improve eligibility of acute stroke patients for thrombolysis treatment , 2013, Comput. Oper. Res..

[2]  M. Krause,et al.  Endovascular therapy for ischemic stroke with perfusion-imaging selection. , 2015, The New England journal of medicine.

[3]  D. Leys,et al.  Door-to-Needle Time and the Proportion of Patients Receiving Intravenous Thrombolysis in Acute Ischemic Stroke: Uniform Interpretation and Reporting , 2013, Stroke.

[4]  Hester F. Lingsma,et al.  A randomized trial of intraarterial treatment for acute ischemic stroke. , 2015, The New England journal of medicine.

[5]  David B Matchar,et al.  Strategic Planning to Reduce the Burden of Stroke Among Veterans: Using Simulation Modeling to Inform Decision Making , 2014, Stroke.

[6]  P. Sandercock,et al.  Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials | NOVA. The University of Newcastle's Digital Repository , 2014 .

[7]  A. Bowen,et al.  National Clinical Guideline for Stroke , 2008 .

[8]  J. Saver.  Time Is Brain—Quantified , 2006, Stroke.

[9]  Mark Elder,et al.  Simulating the impact of change: implementing best practice in stroke care , 2011, London journal of primary care.

[10]  G Scott Gazelle,et al.  Stroke: Effect of implementing an evaluation and treatment protocol compliant with NINDS recommendations. , 2003, Radiology.

[11]  Christos Vasilakis,et al.  Modelling nationwide hospital length of stay: opening the black box , 2005, J. Oper. Res. Soc..

[12]  J. Wardlaw,et al.  Thrombolysis for acute ischaemic stroke. , 2014, The Cochrane database of systematic reviews.

[13]  Martin Pitt,et al.  Will Delays in Treatment Jeopardize the Population Benefit From Extending the Time Window for Stroke Thrombolysis? , 2012, Stroke.

[14]  Sally I. McClean,et al.  A modeling framework that combines markov models and discrete-event simulation for stroke patient care , 2011, TOMC.

[15]  Benita Cox,et al.  Facilitating stroke care planning through simulation modelling , 2010, Health Informatics J..

[16]  Javier Mar,et al.  Budget impact analysis of thrombolysis for stroke in Spain: a discrete event simulation model. , 2010, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[17]  Martin Pitt,et al.  Maximizing the Population Benefit From Thrombolysis in Acute Ischemic Stroke: A Modeling Study of In-Hospital Delays , 2012, Stroke.

[18]  L. Garg,et al.  Is it beneficial to increase the provision of thrombolysis?-- a discrete-event simulation model. , 2012, QJM : monthly journal of the Association of Physicians.

[19]  A. Rabinstein,et al.  In-hospital delays to stroke thrombolysis: paradoxical effect of early arrival , 2007, Neurological research.

[20]  M. Wintermark,et al.  Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association , 2013, Stroke.

[21]  Eric E. Smith,et al.  Randomized assessment of rapid endovascular treatment of ischemic stroke. , 2015, The New England journal of medicine.

[22]  Maarten Schrooten,et al.  Treatment Time-Specific Number Needed to Treat Estimates for Tissue Plasminogen Activator Therapy in Acute Stroke Based on Shifts Over the Entire Range of the Modified Rankin Scale , 2009, Stroke.

[23]  S. Kiechl,et al.  Stroke Thrombolysis: Having More Time Translates Into Delayed Therapy: Data From the Austrian Stroke Unit Registry , 2010, Stroke.