Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke.

BACKGROUND: Expert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use. METHODS: Anonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals. RESULTS: Of the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods. CONCLUSIONS: Machine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.

U.K. Foot and Mouth Disease: A Systemic Risk Assessment of Existing Controls.

This article details a systemic analysis of the controls in place and possible interventions available to further reduce the risk of a foot and mouth disease (FMD) outbreak in the United Kingdom. Using a research-based network analysis tool, we identify vulnerabilities within the multibarrier control system and their corresponding critical control points (CCPs). CCPs represent opportunities for active intervention that produce the greatest improvement to United Kingdom's resilience to future FMD outbreaks. Using an adapted 'features, events, and processes' (FEPs) methodology and network analysis, our results suggest that movements of animals and goods associated with legal activities significantly influence the system's behavior due to their higher frequency and ability to combine and create scenarios of exposure similar in origin to the U.K. FMD outbreaks of 1967/8 and 2001. The systemic risk assessment highlights areas outside of disease control that are relevant to disease spread. Further, it proves to be a powerful tool for demonstrating the need for implementing disease controls that have not previously been part of the system.

What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice.

INTRODUCTION: The aim of this work was to understand between-hospital variation in thrombolysis use among emergency stroke admissions in England and Wales. PATIENTS: A total of 88,928 patients who arrived at all 132 emergency stroke hospitals in England Wales within 4 h of stroke onset, from 2016 to 2018. METHODS: Machine learning was applied to the Sentinel Stroke National Audit Programme (SSNAP) data set, to learn which patients in each hospital would likely receive thrombolysis. We used XGBoost machine learning models, coupled with a SHAP model for explainability; Shapley (SHAP) values, providing estimates of how patient features, and hospital identity, influence the odds of receiving thrombolysis. RESULTS: Thrombolysis use in patients arriving within 4 h of known or estimated stroke onset ranged 7% -49% between hospitals. The odds of receiving thrombolysis reduced 9-fold over the first 120 min of arrival-to-scan time, varied 30-fold with stroke severity, reduced 3-fold with estimated rather than precise stroke onset time, fell 6-fold with increasing pre-stroke disability, fell 4-fold with onset during sleep, fell 5-fold with use of anticoagulants, fell 2-fold between 80 and 110 years of age, reduced 3-fold between 120 and 240 min of onset-to-arrival time and varied 13-fold between hospitals. The majority of between-hospital variance was explained by the hospital, rather than the differences in local patient populations. CONCLUSIONS: Using explainable machine learning, we identified that the majority of the between-hospital variation in thrombolysis use in England and Wales may be explained by differences in in-hospital processes and differences in attitudes to judging suitability for thrombolysis.

National implementation of reperfusion for acute ischaemic stroke in England: How should services be configured? A modelling study.

Objectives: To guide policy when planning thrombolysis (IVT) and thrombectomy (MT) services for acute stroke in England, focussing on the choice between 'mothership' (direct conveyance to an MT centre) and 'drip-and-ship' (secondary transfer) provision and the impact of bypassing local acute stroke centres. Design: Outcome-based modelling study. Setting: 107 acute stroke centres in England, 24 of which provide IVT and MT (IVT/MT centres) and 83 provide only IVT (IVT-only units). Participants: 242,874 emergency admissions with acute stroke over 3 years (2015-2017). Intervention: Reperfusion delivered by drip-and-ship, mothership or 'hybrid' models; impact of additional travel time to directly access an IVT/MT centre by bypassing a more local IVT-only unit; effect of pre-hospital selection for large artery occlusion (LAO). Main outcome measures: Population benefit from reperfusion, time to IVT and MT, admission numbers to IVT-only units and IVT/MT centres. Results: Without pre-hospital selection for LAO, 94% of the population of England live in areas where the greatest clinical benefit, assuming unknown patient status, accrues from direct conveyance to an IVT/MT centre. However, this policy produces unsustainable admission numbers at these centres, with 78 out of 83 IVT-only units receiving fewer than 300 admissions per year (compared to 3 with drip-and-ship). Implementing a maximum permitted additional travel time to bypass an IVT-only unit, using a pre-hospital test for LAO, and selecting patients based on stroke onset time, all help to mitigate the destabilising effect but there is still some significant disruption to admission numbers, and improved selection of patients suitable for MT selectively reduces the number of patients who would receive IVT at IVT-only centres, challenging the sustainability of IVT expertise in IVT-only centres. Conclusions: Implementation of reperfusion for acute stroke based solely on achieving the maximum population benefit potentially leads to destabilisation of the emergency stroke care system. Careful planning is required to create a sustainable system, and modelling may be used to help planners maximise benefit from reperfusion while creating a sustainable emergency stroke care system.

Planning and Providing Acute Stroke Care in England: The Effect of Planning Footprint Size.

Background: Guidelines in England recommend that hyperacute stroke units (HASUs) should have a minimum of 600 confirmed stroke admissions per year in order to sustain expert consultant-led services, and that travel time for patients should ideally be 30 min or less. Currently, 61% of stroke patients attend a unit with at least 600 admissions per year and 56% attend such a unit and have a travel time of no more than 30 min. Objective: We have sought to understand how varying the planning and provision footprint in England affects access to care whilst achieving the recommended admission numbers for hyper-acute stroke care. We have compared two different planning footprints to national-level planning: planning using five NHS Regions in England, and planning using 44 Sustainability and Transformation Partnerships (STPs) in England. Methods: Computer modeling and optimization using a multi-objective genetic algorithm. Results: The number of stroke admissions between STPs varies by seven-fold, while the number of stroke admissions between NHS Regions varies by 2.5-fold. In order to meet stroke admission guidelines (600/year) for all units the maximum possible proportion of patients within 30 min would be 82, 78, and 72% with no boundaries to planning/provision, NHS Region boundaries, and STP boundaries (in these scenarios patients cannot move outside of their own STP or NHS Region). If STP or NHS Region boundaries are removed for provision of service (after planning is performed at these local levels), travel time is improved, but number of admissions to individual hospitals become significantly changed, especially at STP planning level where admission numbers per unit changed by an average of 204 (19%), and not all units maintained 600 admissions after removal of boundaries. Conclusion: Planning and providing services at STP level could lead to sub-optimal service provision compared with using larger and more consistently populated planning areas.

Maximising access to thrombectomy services for stroke in England: A modelling study.

PURPOSE: Both intravenous thrombolysis (IVT) and intra-arterial endovascular thrombectomy (ET) improve the outcome of patients with acute ischaemic stroke, with endovascular thrombectomy being an option for those patients with large vessel occlusions. We sought to understand how organisation of services affects time to treatment for both intravenous thrombolysis and endovascular thrombectomy. METHOD: A multi-objective optimisation approach was used to explore the relationship between the number of intravenous thrombolysis and endovascular thrombectomy centres and times to treatment. The analysis is based on 238,887 emergency stroke admissions in England over 3 years (2013-2015). RESULTS: Providing hyper-acute care only in comprehensive stroke centres (CSC, providing both intravenous thrombolysis and endovascular thrombectomy, and performing >150 endovascular thrombectomy per year, maximum 40 centres) in England would lead to 15% of patients being more than 45 min away from care, and would create centres with up to 4300 stroke admissions/year. Mixing hyper-acute stroke units (providing intravenous thrombolysis only) with comprehensive stroke centres speeds time to intravenous thrombolysis and mitigates admission numbers to comprehensive stroke centres, but at the expense of increasing time to endovascular thrombectomy. With 24 comprehensive stroke centres and all remaining current acute stroke units as hyper-acute stroke units, redirecting patients directly to attend a comprehensive stroke centre by accepting a small delay (15-min maximum) in intravenous thrombolysis reduces time to endovascular thrombectomy: 25% of all patients would be redirected from hyper-acute stroke units to a comprehensive stroke centre, with an average delay in intravenous thrombolysis of 8 min, and an average improvement in time to endovascular thrombectomy of 80 min. The balance of comprehensive stroke centre:hyper-acute stroke unit admissions would change from 24:76 to 49:51. CONCLUSION: Planning of hyper-acute stroke services is best achieved when considering all forms of acute care and ambulance protocol together. Times to treatment need to be considered alongside manageable and sustainable admission numbers.

Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway.

OBJECTIVE: To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals. DESIGN: Computer simulation modelling and machine learning. SETTING: Seven acute stroke units. PARTICIPANTS: Anonymised clinical audit data for 7864 patients. RESULTS: Three factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%-73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machine learning model could predict the benefit of 'exporting' clinical decision making from one hospital to another, while allowing for differences in patient population between hospitals. By applying pathway simulation and machine learning together, we found a realistic ceiling of 15%-25% use of thrombolysis across different hospitals and, in the seven hospitals studied, a realistic opportunity to double the number of patients with no significant disability that may be attributed to thrombolysis. CONCLUSIONS: National clinical audit may be enhanced by a combination of pathway simulation and machine learning, which best allows for an understanding of key levers for improvement in hyperacute stroke pathways, allowing for differences between local patient populations. These models, based on standard clinical audit data, may be applied at scale while providing results at individual hospital level. The models facilitate understanding of variation and levers for improvement in stroke pathways, and help set realistic targets tailored to local populations.

Feasibility of a hyper-acute stroke unit model of care across England: a modelling analysis.

OBJECTIVES: The policy of centralising hyperacute stroke units (HASUs) in England aims to provide stroke care in units that are both large enough to sustain expertise (>600 admissions/year) and dispersed enough to rapidly deliver time-critical treatments (<30 min maximum travel time). Currently, just over half (56%) of patients with stroke access care in such a unit. We sought to model national configurations of HASUs that would optimise both institutional size and geographical access to stroke care, to maximise the population benefit from the centralisation of stroke care. DESIGN: Modelling of the effect of the national reconfiguration of stroke services. Optimal solutions were identified using a heuristic genetic algorithm. SETTING: 127 acute stroke services in England, serving a population of 54 million people. PARTICIPANTS: 238 887 emergency admissions with acute stroke over a 3-year period (2013-2015). INTERVENTION: Modelled reconfigurations of HASUs optimised for institutional size and geographical access. MAIN OUTCOME MEASURE: Travel distances and times to HASUs, proportion of patients attending a HASU with at least 600 admissions per year, and minimum and maximum HASU admissions. RESULTS: Solutions were identified with 75-85 HASUs with annual stroke admissions in the range of 600-2000, which achieve up to 82% of patients attending a stroke unit within 30 min estimated travel time (with at least 95% and 98% of the patients being within 45 and 60 min travel time, respectively). CONCLUSIONS: The reconfiguration of hyperacute stroke services in England could lead to all patients being treated in a HASU with between 600 and 2000 admissions per year. However, the proportion of patients within 30 min of a HASU would fall from over 90% to 80%-82%.