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Original research
Effect of routing paradigm on patient centered outcomes in acute ischemic stroke
  1. Minerva H Zhou1,
  2. Akash P Kansagra2,3,4
  1. 1 Washington University School of Medicine, St Louis, Missouri, USA
  2. 2 Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri, USA
  3. 3 Department of Neurological Surgery, Washington University School of Medicine, St Louis, Missouri, USA
  4. 4 Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA
  1. Correspondence to Dr Akash P Kansagra, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO 63110, USA; apkansagra{at}gmail.com

Abstract

Background To compare performance of routing paradigms for patients with acute ischemic stroke using clinical outcomes.

Methods We simulated different routing paradigms in a system comprising one primary stroke center (PSC) and one comprehensive stroke center (CSC), separated by distances representative of urban, suburban, and rural environments. In the nearest center paradigm, patients are initially sent to the nearest center, while in CSC first, patients are sent to the CSC. In the Rhode Island and distributive paradigms, patients with a FAST-ED (Facial palsy, Arm weakness, Speech changes, Time, Eye deviation, and Denial/neglect) score ≥4 are sent to the CSC, while others are sent to the nearest center or PSC, respectively. Performance and efficiency were compared using rates of good clinical outcome, determined by type and timing of treatment using clinical trial data, and number needed to bypass (NNB).

Results Good clinical outcome was achieved in 43.76% of patients in nearest center, 44.48% in CSC first, and 44.44% in Rhode Island and distributive in an urban setting; 43.38% in nearest center, 44.19% in CSC first, and 44.17% in Rhode Island in a suburban setting; and 41.10% in nearest center, 43.20% in CSC first, and 42.73% in Rhode Island in a rural setting. In all settings, NNB was generally higher for CSC first compared with Rhode Island or distributive.

Conclusion Routing paradigms that allow bypass of nearer hospitals for thrombectomy capable centers improve population level patient outcomes. Differences are more pronounced with increasing distance between hospitals; therefore, paradigm choice may be most impactful in rural settings. Selective bypass, as implemented in the Rhode Island and distributive paradigms, improves system efficiency with minimal impact on outcomes.

  • stroke
  • thrombectomy

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Introduction

Earlier treatment of patients with acute ischemic stroke (AIS) is known to correlate with improved clinical outcomes,1–3 underscoring the importance of rapid delivery of these patients to hospitals capable of implementing appropriate care. When intravenous tissue plasminogen activator (IV tPA) was the only approved treatment for AIS, minimizing time to treatment generally implied routing the patient to the nearest capable center. However, with recent clinical trials demonstrating dramatic efficacy of endovascular thrombectomy (EVT) in patients with AIS due to large vessel occlusion (LVO),2 3 routing of patients has become more complex. In particular, not all hospitals capable of administering IV tPA are capable of performing EVT. Optimal routing of patients by emergency medical services (EMS) must now account for these hospital to hospital differences in treatment capability.

Different routing paradigms in use throughout the USA may prioritize initiation of either IV tPA or EVT based on initial hospital destination. For simplicity, we refer to hospitals capable of providing IV tPA but not EVT as primary stroke centers (PSC) and hospitals capable of providing both as comprehensive stroke centers (CSC). So called ‘drip and ship’ models prioritize early initiation of IV tPA at the closest center but delay EVT for patients that must be transferred to a CSC. In contrast, so called ‘mothership’ models prioritize direct transport to a CSC for early initiation of EVT, potentially delaying administration of IV tPA on account of longer transport time.4 While there are strong opinions on the optimal model for patient routing, there is little objective, outcome based data to guide the choice between these and related routing paradigms.

In this work, we develop a population level simulation based on parameters derived from clinical trial data to model the effect of different EMS routing strategies on patient centered clinical outcomes and underlying performance metrics, including the rate of functional independence at 90 days, time between symptom onset and important care waypoints (eg, initial hospital arrival, IV tPA, and EVT), and percentage of patients that receive IV tPA and EVT. We further compare the performance of these routing strategies under different geographical conditions corresponding to urban, suburban, and rural settings. We hypothesize that paradigms that include a mechanism for hospital bypass will outperform those that do not, and that the choice of routing paradigm will have the greatest impact in rural settings, where separation between hospitals is large.

Methods

Model design

Using MATLAB R2017a Simulink (Mathworks, Natick, Massachusetts, USA), we created a hospital network model consisting of one PSC and one CSC. Both centers can administer IV tPA and perform non-invasive vascular imaging, but only the CSC can perform EVT. These two centers are separated by 10 miles in a simulated urban setting, 30 miles in a suburban setting, and 100 miles in a rural setting, with corresponding service radii of 15 miles, 45 miles, and 150 miles, respectively, around the PSC.

Patients with AIS are generated in a random location within the designated service area. National Institutes of Health Stroke Scale (NIHSS), used as a measure of stroke severity, is generated probabilistically from an established distribution.5 The presence of underlying LVO6 and corresponding FAST-ED (Facial palsy, Arm weakness, Speech changes, Time, Eye deviation, and Denial/neglect) score7—representative of prehospital stroke severity scales used by EMS as a surrogate for NIHSS—is also generated probabilistically based on NIHSS. Symptom onset to time of EMS discovery is also generated from a previously reported distribution.8 Travel time to and between hospitals is determined by straight line distance. Additional details are contained within the online supplement.

EMS routing paradigms

Each patient generated as above is replicated and assigned to each EMS routing paradigm being tested. The initial hospital destination is chosen according to one of four models for EMS routing:

  1. Nearest center: Patients are sent to the nearest center (PSC or CSC), regardless of stroke severity.

  2. CSC first: Patients are sent to the CSC, regardless of stroke severity.

  3. Rhode Island: Patients with FAST-ED ≥4 are sent to the CSC, while patients with FAST-ED <4 are sent to the nearest center (PSC or CSC). This approach is similar to the paradigm used in Rhode Island9 and is a hybrid of ‘nearest center’ and ‘CSC first’ models, with the latter being used in patients with greater likelihood of underlying LVO.

  4. Distributive: Patients with FAST-ED ≥4 are sent to the CSC, while patients with FAST-ED <4 are sent to the PSC. This approach is intended to ensure EVT access to patients with LVO by diverting low severity strokes to a PSC, and is therefore feasible only in urban settings where bypass of a nearby CSC in favor of a more distant PSC does not incur a large time penalty.

While these routing models determine the initial hospital destination, EVT eligible patients at the PSC may be subsequently transferred to the CSC as needed in all models.

Deterministic simulation

Additional factors that influence choice of treatment or treatment related outcomes, such as presence of contraindications to IV tPA and/or EVT, rate of LVO recanalization with IV tPA and/or EVT, and development of intracranial hemorrhage (ICH), are based on best estimates derived from clinical trial data (table 1).3 10–13

Table 1

Model parameters

Good clinical outcome is defined as a 90 day modified Rankin Score (mRS) score of 0–2 and determined by type of treatment received and time to treatment. Patients without LVO may receive IV tPA or no treatment, with corresponding time dependent mRS distributions from the treatment and placebo arms of a pooled analysis of IV tPA related clinical trial data.14 Similarly, patients with LVO may receive no treatment, IV tPA only, or EVT with or without IV tPA. The associated mRS distributions correspond to the control arms of groups ineligible and eligible for IV tPA2 and the time dependent pooled intervention group achieving substantial reperfusion,3 respectively, in a meta-analysis of EVT related clinical trial data. Substantial reperfusion is defined as Thrombolysis in Cerebral Ischemia 2b or greater; patients who do not achieve substantial reperfusion post-intervention are assigned clinical outcomes as if they did not receive EVT, using the distribution for either ‘no treatment’ or ‘IV tPA only,’ as appropriate. This simplifying assumption is necessary due to the lack of pertinent clinical trial data. The ‘IV tPA only’ distributions used for the LVO and non-LVO groups both included patients with symptomatic ICH. A schematic depicting this outcome assignment is shown in figure 1.

Figure 1

Flow diagram depicting clinical care pathways for patients with and without large vessel occlusion (LVO). The final mode of care (no treatment, intravenous tissue plasminogen activator (IV tPA) only, or endovascular thrombectomy (EVT)±IV tPA) determines the distribution used to generate a modified Rankin Scale score. Distributions based on control populations in: a=alteplase ineligible; b=alteplase received subgroup in Goyal et al 2; c=EVT subgroup with substantial reperfusion in Saver et al 3; d=placebo; and e=alteplase subgroups in Lees et al.14 PSC, primary stroke center; TICI, Thrombolysis in Cerebral Ischemia.

Stochastic simulation

To account for uncertainty and variability associated with deterministic model parameters,15 we also created a stochastic model that is identical to the deterministic model except that fixed parameter estimates are replaced with probability distributions encompassing a realistic range of parameter values (table 1). For each such parameter, a triangular probability distribution is used, with the peak of the distribution corresponding to the fixed parameter estimate used in the deterministic model, and the bounds of the probability distribution corresponding to 2 SD or reasonable estimates based on local institutional experience. SD are determined using the binomial distribution for counting data and reported SD for continuous data when available.

Simulation output and analysis

A deterministic simulation was performed to model the care of 100 000 patients. Measures of system performance (eg, time to treatment) and clinical outcome (eg, percentage of patients with good clinical outcome) were assessed for all routing paradigms. Number needed to bypass (NNB)—defined as the percentage of patients initially taken to a non-nearest hospital divided by the percentage difference in rates of good clinical outcome between the route of interest and nearest center—was calculated as a measure of bypass efficiency. Subsequently, a stochastic simulation was performed to model the care of 10 000 patients in each of 1000 different sets of probabilistically generated model parameters. Rates of good clinical outcome and NNB for each trial were aggregated into distributions, which were used in turn to generate stochastic estimates of system performance and efficiency. Overall supremacy of routing paradigms was also quantified by determining the percentage of simulations in which each model achieved the best performance of all paradigms.

Approval of the institutional review board and informed consent were not required as no patients were involved in this study.

Results

Deterministic model

Metrics of system performance in the deterministic model are described in table 2. As expected, nearest center had the shortest median time to first hospital arrival and highest rates of IV tPA administration, while CSC first had the highest rates of EVT.

Table 2

Performance of deterministic simulation with 100 000 patients

Good clinical outcome was achieved in 43.98% in nearest center, 44.57% in CSC first, 44.56% in Rhode Island, and 44.55% in distributive in an urban setting; 43.51% in nearest center, 44.35% in CSC first, and 44.32% in Rhode Island in a suburban setting; and 41.38% in nearest center, 43.29% in CSC first, and 42.91% in Rhode Island in a rural setting.

NNB was 113.18 for CSC first, 92.10 for Rhode Island, and 103.94 for distributive in an urban setting; 79.71 for CSC first and 64.91 for Rhode Island in a suburban setting; and 34.95 for CSC first and 34.55 for Rhode Island in a rural setting.

Stochastic model

Metrics of system performance in the stochastic model are described in table 3. Good clinical outcome was achieved in 43.76% in nearest center, 44.48% in CSC first, 44.44% in Rhode Island, and 44.44% in distributive in an urban setting; 43.38% in nearest center, 44.19% in CSC first, and 44.17% in Rhode Island in a suburban setting; and 41.10% in nearest center, 43.20% in CSC first, and 42.73% in Rhode Island in a rural setting.

Table 3

Performance of stochastic simulation with 10 000 patients for 1000 trials

NNB was 101.26 for CSC first, 85.98 for Rhode Island, and 96.25 for distributive in an urban setting; 83.18 for CSC first and 68.17 for Rhode Island in a suburban setting; and 31.47 for CSC first and 32.12 for Rhode Island in a rural setting.

In an urban setting, CSC first, Rhode Island, and distributive won 99.9%, 1.3%, and 1.3% of simulations, respectively. In a suburban setting, CSC first and Rhode Island won 98.9% and 5.4%, respectively. In a rural setting, CSC first won 100% of simulations and Rhode Island won 0%. In all three settings, nearest center won 0% of simulations.

Discussion

We simulated the effects of different EMS routing paradigms in a population of patients with AIS in order to derive objective metrics of system performance and patient centered outcomes in different geographical settings. We found that the nearest center routing model—the only one that does not permit hospital bypass in any clinical circumstance—leads to the worst population level clinical outcomes in urban, suburban, and rural settings, although a very small minority of patients may fare better under this model by presenting to a nearer hospital in time to receive IV tPA. While the CSC first routing model leads to the best population level clinical outcomes, Rhode Island and distributive—routing models that allow selective bypass—produce similar outcomes with greater bypass efficiency. Moreover, differences in system performance and clinical outcomes between competing routing paradigms become more pronounced with increasing separation between the nearest PSC and nearest CSC, and therefore, the choice of paradigm may be most impactful in rural settings.

The optimal strategy for EMS routing has been the subject of active debate.4 While there is widespread consensus that faster time to reperfusion confers considerable clinical benefit, there is growing but not yet universal agreement that hospital bypass—accelerating time to EVT while potentially delaying the initiation of IV tPA—is warranted in some cases. This notion is supported by recent studies indicating that a substantial number of patients with LVO do not receive EVT despite meeting eligibility criteria, and that real world implementation of hospital bypass may increase the rate of EVT and good clinical outcome in patients with LVO.16 17 With these data in mind, recently published guidelines from the Society of Neurointerventional Surgery advocate for primary transport to CSCs, only diverting to a closer center in specific situations.18 Similarly, the American Heart Association/American Stroke Association (AHA/ASA) mission: lifeline stroke initiative introduced the severity based stroke triage algorithm for EMS that recommends using stroke severity screening tools to identify possible LVO patients to route directly to CSCs in a specific time and distance window.19 However, the 2018 AHA/ASA guidelines for early management of stroke also recognize the importance of regional customizability and acknowledge the lack of sufficient evidence to recommend a specific scale or bypass threshold, although they do suggest that hospital bypass that delays IV tPA administration by 15 or more minutes should be avoided.20

Conditional probabilistic models by Schlemm et al,21 Holodinsky et al,22 and Milne et al 23 have reinforced the importance of situational factors, such as patient location, stroke severity, treatment times, and distance between PSC and CSC, on patient outcomes, and use this approach to compare the efficacy of the CSC first and nearest center models on a patient level to determine the best destination for an individual patient under specific conditions. Another model by Bogle et al 24 simulates the AHA/ASA algorithm in two counties in the USA. The reported outcomes focus on efficiency of routing by measuring overtriage and undertriage, and thus do not simulate changes in treatment and outcomes related to recanalization or ICH following IV tPA administration. While such research provides important insights to EMS providers and treating physicians, individuals with the larger task of selecting an EMS routing paradigm for an entire region would be better served by insight on the population level health impact of these models. Our approach aims to provide such insight while incorporating real world factors that influence treatment pathway and expected clinical outcome.

Proponents of the nearest center model note that bypassing a closer center can cause patients to miss the treatment window for IV tPA. Indeed, our simulation validates this concern by virtue of the smaller percentage of patients that receive IV tPA in the CSC first and Rhode Island models compared with the nearest center model. However, despite rendering a small minority of patients ineligible for IV tPA, EMS routing models that allow hospital bypass generally confer a substantial outcome benefit at a population level by shortening the time for EVT.

Overall, the relative performance of CSC first, Rhode Island, and distributive in terms of population level outcomes is comparable. The Rhode Island and distributive models are intermediate approaches to nearest center and CSC first paradigms that introduce a heuristic element based on clinical stroke severity in order to classify patients into populations with different probabilities of underlying LVO that may be used as the basis of subsequent routing. We found that the CSC first paradigm produces the highest rates of good clinical outcome on a population level in all settings, likely due to the dramatic impact of interhospital transfer on time to treatment. However, Rhode Island and distributive produce nearly identical outcomes as CSC first, but do so with greater bypass efficiency. In particular, these paradigms use a clinical severity threshold to predict the presence of LVO and implement bypass selectively, thereby subjecting a smaller number of patients to bypass.

There are limitations to this analysis. First, factors beyond the outcome measures reported here may influence the choice of routing model. For example, compared with the CSC first approach, the Rhode Island and distributive models better distribute patient load between PSC and CSC and thereby reduce resource strain at the CSC and maintain expertise at the PSC. Second, several model parameters are assumed to be independent of one another, which in some cases may overlook correlations between parameters. For example, the clinical outcome of a patient with LVO following recanalization is likely dependent on NIHSS.25 Since clinical outcome distributions are taken from clinical trial data, this percentage currently does not vary with NIHSS in our model. These simplifying assumptions are necessary in the absence of high level clinical trial data to inform these interdependencies. Finally, eligibility for EVT or IV tPA was determined based on exclusion criteria from high level clinical trial data, or equivalently, evidence based guidelines in effect at the time, but actual clinical practice may deviate from a strictly trial or guidelines based approach at some sites. Nevertheless, this limitation affects all routing paradigms and may not meaningfully affect their relative performance.

Summary

EMS routing paradigms that allow at least some degree of bypass consistently yield better population level outcomes than a nearest center approach. Paradigms that allow selective bypass of patients with high probability of LVO yield similar outcomes as CSC first, but with greater bypass efficiency. The magnitude of performance discrepancy is greatest when separation between hospitals is large. Head to head comparisons between different EMS routing paradigms in the same population are improbable in the real world, and thoughtful, informed simulation can therefore quantitatively inform the choice of EMS routing paradigm and associated triage policies in a variety of settings.

Acknowledgments

All authors meet ICMJE guidelines for authorship. MHZ made substantial contribution to the design and construction of the work, acquisition and analysis of the data, drafting and revising, approved the final version, and agrees to be accountable. APK made substantial contribution to the conception and design of the work, analysis and interpretation of the data, drafting and revising, approved the final version, and agrees to be accountable.

References

Footnotes

  • Funding This work was supported by RSNA grant No RMS1722.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Patient consent for publication Not required.