Skip main navigation

Modeling the Optimal Transportation for Acute Stroke Treatment

The Impact of the Drip-and-Drive Paradigm
Originally publishedhttps://doi.org/10.1161/STROKEAHA.119.027493Stroke. 2020;51:275–281

Abstract

Background and Purpose—

Health systems are faced with the challenge of ensuring fast access to appropriate therapy for patients with acute stroke. The paradigms primarily discussed are mothership and drip and ship. Less attention has been focused on the drip-and-drive (DD) paradigm. Our aim was to analyze whether and under what conditions DD would predict the greatest probability of good outcome for patients with suspected ischemic stroke in Northwestern Germany.

Methods—

Conditional probability models based on the decay curves for endovascular therapy and intravenous thrombolysis were created to determine the best transport paradigm, and results were displayed using map visualizations. Our study area consisted of the federal states of Lower Saxony, Hamburg, and Schleswig-Holstein in Northwestern Germany covering an area of 64 065 km2 with a population of 12 703 561 in 2017 (198 persons per km2). In several scenarios, the catchment area, that is, the region that would result in the greatest probability of good outcomes, was calculated for each of the mothership, drip-and-ship, and the DD paradigms. Several different treatment time parameters were varied including onset-to-first-medical-response time, ambulance-on-scene time, door-to-needle time at primary stroke center, needle-to-door time, door-to-needle time at comprehensive stroke center, door-to-groin-puncture time, needle-to-interventionalist-leave time, and interventionalist-arrival-to-groin-puncture time.

Results—

The mothership paradigm had the largest catchment area; however, the DD catchment area was larger than the drip-and-ship catchment area so long as the needle-to-interventionalist-leave time and the interventionalist-arrival-to-groin-puncture time remain <40 minutes each. A slowed workflow in the DD paradigm resulted in a decrease of the DD catchment area to 1221 km2 (2%).

Conclusions—

Our study suggests the largest catchment area for the mothership paradigm and a larger catchment area of DD paradigm compared with the drip-and-ship paradigm in Northwestern Germany in most scenarios. The existence of different paradigms allows the spread of capacities, shares the cost and hospital income, and gives primary stroke centers the possibility to provide endovascular therapy services 24/7.

Introduction

Patients with anterior circulation acute ischemic stroke caused by large vessel occlusion (LVO) have better clinical outcomes after receiving endovascular therapy (EVT) in addition to intravenous thrombolysis (IVT).1,2 Time delays to these reperfusion therapies have a profound impact on clinical outcomes3,4 meaning health systems are faced with the challenge of ensuring fast access to the appropriate therapies for patients with acute ischemic stroke.5,6 The 2 paradigms predominantly discussed have been: (1) the mothership paradigm where patients with acute ischemic stroke are directly transported to a comprehensive stroke center (CSC), which minimizes time to EVT; and (2) the drip and ship (DS) where patients with acute ischemic stroke are first transported to the nearest primary stroke center (PSC) to minimize time to IVT and then transferred to the nearest CSC if EVT is necessary. Recently, a third approach—the drip-and-drive (DD) paradigm—has been proposed where the EVT-capable neurointerventionalist is transported to the patient in the PSC (Figure I in the online-only Data Supplement). The proposed DD paradigm represents a promising approach that led to faster treatment times when compared with the DS paradigm.7,8

However, there are advantages and disadvantages to each of these paradigms. The approach that leads to the highest probability of good outcome for the patient depends on several factors such as the probability of an underlying LVO, the distance to the nearest PSC or CSC, the transfer time between PSC and CSC, and the efficiency of the nearest PSC in transferring patients to CSC. To support stroke systems of care in the triage of stroke patients, models taking into consideration various probabilities and time considerations have been developed.9–14 They allow to calculate the catchment area of each approach, that is, the region that would result in the greatest probability of good outcomes for patients with suspected ischemic stroke.

In this study, we used conditional probability modeling to determine whether and under what conditions the DD paradigm would predict the greatest probability of good outcomes for patients with suspected ischemic stroke in Northwestern Germany. We hypothesized that the DD paradigm would have larger catchment areas than the DS paradigm.

Methods

The data that support the findings of this study are available from the corresponding author on reasonable request.

Terminology

For the purposes of this analysis, a CSC is a hospital that provides both EVT and IVT around the clock every day of the year. A PSC is a hospital that provides IVT around the clock every day of the year and has the necessary equipment and a locally available angiosuite staff to provide EVT but, however, does not have in-house EVT-capable neurointerventionalists. Technicians and radiologists of the PSCs are familiar to EVT procedures and the material used.

A CSC+ is a university medical center offering a neuroendovascular fellowship program. More than 200 endovascular stroke procedures per year and >400 other neurointerventional cases per year are performed. A CSC+ is adequately staffed to allow for an EVT-capable neurointerventionalist to travel to a PSC in the DD paradigm. At least 8 neurointerventionalists work at the CSC+ of whom at least 4 provide the on-call service at the CSC and a further 3 neurointerventionalists cover the on-call service for EVT performed at the PSCs. Neurointerventionalists from the CSC+ are familiar with the location and functioning of angiography suites and stroke units of the PSC, as well as with the teams of neurology, radiology, and anesthesiology.

Models

Previously, conditional probability models that predict probability of good outcome (modified Rankin Scale score of 0–1 at 90 days poststroke) for patients with suspected LVO at a population level based on time from stroke onset to treatment have been generated.12 The models are based on the decay in good outcome rates over times for both IVT and EVT15,16 and incorporate the probability of an underlying LVO, geography, and hospital efficiency to model the probability of good outcome based on time from onset to treatment in both the mothership and the DS transport paradigms. In this analysis, the models have been extended to also reflect workflow in the DD paradigm. See the online-only Data Supplement (Supplemental Equations: Breakdown of Model Components and Figure II in the online-only Data Supplement) for details on model components. Our analysis was not restricted to any specific date or time of day.

Territory

In this study, we analyzed prehospital stroke triage in the federal states of Lower Saxony, Hamburg, and Schleswig-Holstein in Northwestern Germany. The area covers 64 065 km2 with a population of 12 703 561 in 2017 (ie, 198 persons per km2). People >65 years of age, known to have a greater risk of stroke,17 represent 21.5% of the total population.18,19 In this area, there are 23 CSCs, 5 CSC+, and 21 PSCs. Prehospital triage strategy paradigms not including DD have previously been evaluated for a part of this region.20

Modeling Scenarios

Several different combinations of treatment efficiencies at the PSC and CSC were created to mimic the workflow at efficient, inefficient, and overloaded hospitals to ascertain the effect of this on the best transport option. Nine different modeling scenarios were investigated using a combination of different patient populations and treatment efficacies (Table). The baseline patient population used was those who would have a score of ≥5 on the Rapid Arterial Occlusion Evaluation scale and thus screen positive for a probable LVO (see the online-only Data Supplement for diagnostic breakdown of patients).21 As sensitivity analyses, scenarios were also generated using the following LVO screening tools: Los Angeles Motor Scale ≥4,22 Cincinnati Stroke Triage Assessment Tool ≥2,23 and Face Arm Speech Test positive.24

Table. Modeling Scenarios

Scenario IScenario IIScenario IIIScenario IVScenario VScenario VIScenario VIIScenario VIIIScenario IX
Prehospital parameters
 Screening toolRACE ≥5RACE ≥5RACE ≥5RACE ≥5RACE ≥5RACE ≥5LAMS ≥4*C-STAT ≥2*FAST+*
 Onset-to-first-medical-response time303030303060*303030
 Ambulance-on-scene time303030303060*303030
Primary stroke center parameters
 Door-to-needle time303030303030303030
 Needle-to-door time202020202020202020
Comprehensive stroke center parameters
 Door-to-needle time30303040*80*30303030
 Door-to-groin-puncture time60606080*130*60606060
 Door-to-groin-puncture time (streamlined workflow)30303040*50*30303030
DD parameters
 Needle-to-interventionalist-leave time1020*101010101010
 Interventionalist-arrival-to-groin-puncture time2030*202020202020
Percentage of patients treated
 Patients with ischemic stroke receiving thrombolysis (who presented within 4.5 h of onset)808080808080808080
 Patients with LVO who receive EVT909090909090909090

Times are shown in minutes. C-STAT indicates Cincinnati Stroke Triage Assessment Tool; CSC, comprehensive Stroke Center; DD, drip-and-drive; EVT, endovascular therapy; FAST, Face Arm Speech Test; LAMS, Los Angeles Motor Scale; LVO, large vessel occlusion; PSC, primary stroke center; and RACE, Rapid Arterial Occlusion Evaluation scale.

*Change in comparison to scenario I or scenario IV, respectively.

†If the patient arrives at the CSC within 90 min of imaging at the PSC, the patient is not reimaged, and thus door-to-groin puncture workflow is shortened.

We assumed that 80% of patients with LVO or non-LVO occlusions with onset-to-treatment time <4.5 hours would receive IVT and that 90% of LVO patients would be treated with EVT. In sensitivity analyses, the percentage of patients with onset-to-treatment time <4.5 hours who receive IVT was varied to 50%.

In scenario I, the following default parameters were used based on our own experience and proposed quality measures25–27: onset-to-first-medical-response time, 30 minutes; ambulance-on-scene time, 30 minutes; door-to-needle time at PSC, 30 minutes; needle-to-door time, 20 minutes; door-to-needle time at CSC, 30 minutes; door-to-groin-puncture time, 60 minutes. In case that a transferred patient arrives at the CSC within 90 minutes of imaging at the PSC, the patient is not reimaged and thus door-to-groin-puncture time is only 30 minutes. In scenario II, the following additional default parameters were used based on our own experience: needle-to-interventionalist-leave time of 10 minutes and interventionalist-arrival-to-groin-puncture time of 20 minutes.

CSCs and PSCs had similar door-to-needle times to ensure that the analyses were performed without favoring either transportation paradigm. However, this assumption was interrogated in a sensitivity analysis with a longer door-to-needle time of 60 minutes and a needle-to-door time of 30 minutes at the PSCs. Scenarios IV and V of this sensitivity analysis with slowed workflow parameters both at the CSC and PSC could represent either work overload during normal business hours or slow workflow during off hours.

Map Generation

Results are visualized using maps generated using the DESTINE mapping software (DESTINE Health, Inc, Calgary, AB, Canada). The maps depict all CSCs as light blue pinpoints, PSCs as yellow pinpoints, and CSC+ as dark blue pinpoints. The maps are color coded to indicate which transport option predicts the best probability of good outcome. Green areas indicate mothership predicts the best probability of good outcome, red indicates DS predicts the best probability of good outcome, and purple indicates DD predicts the best probability of good outcome. The color intensity increases as probability of good outcome increases. Gray indicates areas with no road infrastructure. After map generation, the catchment area (km2), that is, the region in which each transport option predicted best outcomes, was calculated.

Ethics Statement

Institutional review board approval was not sought, as no patient data were used for this study.

Results

We modeled the best transport option for patients in Northwestern Germany using 9 different scenarios (Figure 1).

Figure 1.

Figure 1. Catchment areas of paradigms. Shown is the catchment area in square kilometer of the 3 paradigms in scenarios I through IX given a total area of 64 065 km2.

In the comparison of mothership and DS only (scenario I), the area where mothership predicts the best outcomes was 51 852 km2 (81%), and the area where DS predicts the best outcomes was 12 212 km2 (19%).

In the comparison of mothership, DS, and DD (scenario II), mothership predicted the best outcomes in the majority of the region (49 992 km2, 78%), DS predicted the best outcomes in 9% of the region (5697 km2), and DD predicted the best outcomes in 13% of the region (8376 km2; Figure 2A).

Figure 2.

Figure 2. Modeled transportation option maps in Northwestern Germany. A, Comparison of mothership paradigm, drip-and-ship paradigm, and drip-and-drive paradigm (scenario II). B, Modeling of a delay in the drip-and-drive paradigm (scenario III). C, Modeling of a work overload in the comprehensive stroke centers (scenario IV). D, Modeling of an extreme work overload in the comprehensive stroke centers (scenario V).

In scenario III the workflow in the DD paradigm was slowed by increasing the needle-to-interventionalist-leave time to 20 minutes and the interventionalist-arrival-to-groin-puncture time to 30 minutes. This resulted in a decrease of the DD catchment area to 1221 km2 (2%), an increase of the mothership catchment area to 51 801 km2 (81%), and the DS catchment area to 11 044 km2 (17%; Figure 2B).

In scenario IV, a work overload at the CSC was simulated by increasing the door-to-needle time at the CSC to 40 minutes and the door-to-groin-puncture time to 80 minutes for direct admits and to 40 minutes for patients shipped from a PSC. This led to a decrease of the mothership catchment area to 37 479 km2 (59%) and an increase of the DS and DD catchment areas (8638 km2, 13%; 17 948 km2, 28%, respectively; Figure 2C).

In scenario V, an extreme work overload of the CSC was simulated by further increasing the door-to-needle time at the CSC to 80 minutes and the door-to-groin-puncture time to 130 minutes for direct admits and 50 minutes for shipped patients. This led to a substantial decrease of the mothership catchment area to 2887 km2 (45%) and an increase of the DS and DD catchment areas to 17 562 (27%) and 43 616 km2 (68%), respectively (Figure 2D).

In scenario VI, workflow in the prehospital environment was slowed, and both the onset-to-first-medical-response-time and the ambulance-on-scene-time were increased to 60 minutes. This led to a small increase of the mothership catchment area to 50 224 km2 (78%), a decrease of the DS catchment area to 5578 km2 (9%), and an increase of the DD catchment area to 8263 km2 (13%).

In sensitivity analyses, the catchment area was similar using Los Angeles Motor Scale ≥4 (scenario VII) or Cincinnati Stroke Triage Assessment Tool ≥2 (scenario VIII) as a screening tool compared with using Rapid Arterial Occlusion Evaluation ≥5 (scenario II) with a mothership area of 74% to 78%, a DS area of 9% to 11%, and a DD area of 13% to 15%. The use of Face Arm Speech Test/Cincinnati Prehospital Stroke Scale (scenario IX) led to a decrease of the mothership catchment area to 41 238 km2 (64%), an increase of the DS catchment area to 9007 km2 (14%), and an increase of the DD catchment area to 13 820 km2 (22%).

The results of the sensitivity analysis with a door-to-needle time of 60 minutes and a needle-to-door time of 30 minutes at the PSCs are shown in Figure III in the online-only Data Supplement. The mothership catchment area increased to 96% to 99%, the DD catchment area decreased to 0% to 4%, and the DS catchment area decreased to 0% to 1% in all scenarios with exception of scenario V. In scenario V, the mothership catchment area increased from 4% to 43%, the DD catchment area decreased from 69% to 47%, and the DS catchment area decreased from 27% to 11%.

In further sensitivity analyses, decreasing the percentage of patients with occlusions arriving within 4.5 hours of symptom onset who receive IVT to 50% led to an increase of the mothership area and a decrease of the DS area and DD area in all scenarios (Figure IV in the online-only Data Supplement). With the exception of scenario III, the DD area remained larger than the DS area.

Discussion

In our study, the mothership paradigm had the largest catchment area in Northwestern Germany in most scenarios. The sensitivity analysis showed that the predicted probability of good outcomes declines as the work flow at the PSC is slowed. This is in accordance with a recent modeling work showing that when a PSC is in close proximity to an efficient CSC, it must maintain efficient door-to-needle times to retain its significance.9,12,13 The DD paradigm showed an approximately one-third larger catchment area than the DS paradigm.

The DD paradigm offers several advantages. First, previous studies have shown significantly shorter transfer times moving the neurointerventionalist from CSC+ to PSC compared with transporting the patient from the PSC to the CSC resulting in shorter onset to EVT times.7,8 Although in this model, we used the same transport time for the neurointerventionalist traveling from CSC+ to PSC as that of the patient moving from same PSC to CSC+, we still demonstrated that the catchment area of DD remains larger than DS so long as the needle-to-interventionalist-leave time and the interventionalist-arrival-to-groin-puncture time remain <40 minutes each. The DD workflow is efficient and allows for parallel processing where, while the neurointerventionalist is en route, the patient can be transported to the angiosuite, intubated, and prepared for the intervention. Treatment times could be further reduced if local resources allow for a local member of the Radiology Department to start the procedure by placing the guide catheter in the cervical arteries. A recent study has shown that patients treated by DD received EVT 79 minutes faster than patients treated by DS.8

Second, the vast majority of patients with stroke symptoms are not eligible for EVT, and transporting these patients via the mothership paradigm can result in delays in treatment and overburdening of CSCs with non-LVO patients. To identify patients with LVO, several prehospital stroke severity scales have been developed.28,29 However, given the low prevalence of LVO and the limited accuracy of these scales, the risk of misclassification and suboptimal triage decisions remains high.28 An ongoing randomized controlled trial is currently analyzing the risk of misclassification of non-LVO patients and the possibility of early recanalization after IVT and will be completed in 2020 (RACECAT [Direct Transfer to an Endovascular Center Compared to Transfer to the Closest Stroke Center in Acute Stroke Patients With Suspected Large Vessel Occlusion]; NCT0279596225). In case of misclassification of an LVO patient, DD avoids risks inherent in patient transport during shipping.30

Third, in the DD paradigm, the workload and associated costs and hospital income are shared between PSC and CSC. There are necessary tradeoffs for each transport paradigm. The mothership and DS paradigms centralizing all EVT procedures at a few CSCs are supported by the increasing evidence that high-volume stroke centers (>50 endovascular stroke procedures per year) have lower procedural times, higher reperfusion rates, and better clinical outcomes.31,32 Moreover, postprocedural care with neurocritical care and neuroscience nurses may justify concentrating EVT at specialized centers.32 However, especially in the mothership paradigm, low patient volumes at the PSC might have the unintended effect of decreasing efficiency at the PSC. Moreover, the mothership paradigm alone may overwhelm the CSC and increase the door-to-needle time and door-to-groin-puncture time. If treatment times increase at the CSC, DS and DD increase in favorability as shown in scenarios IV and V. The DD paradigm mitigates some of these risks by spreading procedure volume across multiple hospitals, which allows for both adequate workload to maintain skill sets and optimization of the use of stroke beds throughout a region. Moreover, an increasing workload at the CSCs due to a high incidence of emergency EVT will interfere with planned interventions resulting in treatment delays of planned procedures and longer hospitalizations associated with additional risks to patients and increasing costs.

Finally, the neurointerventionalist’s experience plays an important role in efficient decision-making and achieving quick and safe reperfusion. Serious concerns have been raised about the maintenance of an adequate educational experience and expertise in interventional neuroradiology, if, in answer to the need of EVT, the supply of newly trained neurointerventionalists exceeds procedure volume demands.33 These concerns might be mitigated by sharing an interventional stroke team among CSCs and PSCs.

Limitations

Some limitations have to be considered when interpreting the results of our modeling study.

First, the purpose of this study was to apply a previously published modeling framework12 to the new transport paradigm of DD. The purpose of the study was not to critique or change the structure of the modeling framework itself. An interesting future direction of this work could be to vary the slopes of the decay curves used in the modeling framework to produce further estimates of uncertainty. Decay curves used in this study were derived from clinical trials containing highly selected patients and observational studies in North America and Europe and might not be completely translatable to the population of Northwestern Germany.

Second, the models used in this study are population based and thus do not consider individual patient-level factors such as age, occlusion site, and premorbid status. Moreover, the percentage of treated patients influences the modeled results and might be different in other countries. Thus, the results of our model may not generalize to other cities. However, in our sensitivity analyses with a decrease of LVO and non-LVO patients receiving IVT within 4.5 hours of symptom onset to 50%, the DD area remained larger than the DS area.

Third, travel times through the fastest ground transport were considered, whereas air transport and many nongeographic factors such as difficult weather conditions or traffic congestion were not taken into account. Moreover, we did not consider mobile stroke units because this technology is not yet in widespread use given financial and other logistical concerns.

Finally, we did not include measures of cost-effectiveness. However, in a previous Health Technology Optimization Analysis, the delivery strategies that were clinically optimal for patients were also the most cost-effective.34

Conclusions

In conclusion, using conditional probability modeling, our study suggests the largest catchment area for the mothership paradigm and a larger catchment area where the DD paradigm predicts the best patient outcomes compared with the DS paradigm in Northwestern Germany in most scenarios. The existence of different paradigms allows the spread of capacities, shares the cost and hospital income, and gives PSCs the possibility to provide EVT services 24/7.

Footnotes

The online-only Data Supplement is available with this article at https://www.ahajournals.org/doi/suppl/10.1161/STROKEAHA.119.027493.

Correspondence to Marielle Ernst, MD, Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Haus Ost 22 (O 22), Martinistr. 52, 20246 Hamburg, Germany. Email

References

  • 1. Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, et al; HERMES Collaborators. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials.Lancet. 2016; 387:1723–1731. doi: 10.1016/S0140-6736(16)00163-XCrossrefMedlineGoogle Scholar
  • 2. Bhogal P, Andersson T, Maus V, Mpotsaris A, Yeo L. Mechanical thrombectomy-A brief review of a revolutionary new treatment for thromboembolic stroke.Clin Neuroradiol. 2018; 28:313–326. doi: 10.1007/s00062-018-0692-2CrossrefMedlineGoogle Scholar
  • 3. Meretoja A, Keshtkaran M, Saver JL, Tatlisumak T, Parsons MW, Kaste M, et al. Stroke thrombolysis: save a minute, save a day.Stroke. 2014; 45:1053–1058. doi: 10.1161/STROKEAHA.113.002910LinkGoogle Scholar
  • 4. Meretoja A, Keshtkaran M, Tatlisumak T, Donnan GA, Churilov L. Endovascular therapy for ischemic stroke: Save a minute-save a week.Neurology. 2017; 88:2123–2127. doi: 10.1212/WNL.0000000000003981CrossrefMedlineGoogle Scholar
  • 5. Turc G, Bhogal P, Fischer U, Khatri P, Lobotesis K, Mazighi M, et al. European Stroke Organisation (ESO)- European Society for Minimally Invasive Neurological Therapy (ESMINT) guidelines on mechanical thrombectomy in acute ischemic stroke.J Neurointerv Surg. 2019; 11:535–538. doi: 10.1136/neurintsurg-2018-014568CrossrefMedlineGoogle Scholar
  • 6. Haverkamp C, Ganslandt T, Horki P, Boeker M, Dörfler A, Schwab S, et al. Regional differences in thrombectomy rates: secondary use of billing codes in the MIRACUM (Medical Informatics for Research and Care in University Medicine) consortium.Clin Neuroradiol. 2018; 28:225–234. doi: 10.1007/s00062-017-0656-yCrossrefMedlineGoogle Scholar
  • 7. Brekenfeld C, Goebell E, Schmidt H, Henningsen H, Kraemer C, Tebben J, et al. ‘Drip-and-drive’: shipping the neurointerventionalist to provide mechanical thrombectomy in primary stroke centers.J Neurointerv Surg. 2018; 10:932–936. doi: 10.1136/neurintsurg-2017-013634CrossrefGoogle Scholar
  • 8. Wei D, Oxley TJ, Nistal DA, Mascitelli JR, Wilson N, Stein L, et al. Mobile interventional stroke teams lead to faster treatment times for thrombectomy in large vessel occlusion.Stroke. 2017; 48:3295–3300. doi: 10.1161/STROKEAHA.117.018149LinkGoogle Scholar
  • 9. Holodinsky JK, Williamson TS, Kamal N, Mayank D, Hill MD, Goyal M. Drip and ship versus direct to comprehensive stroke center: conditional probability modeling.Stroke. 2017; 48:233–238. doi: 10.1161/STROKEAHA.116.014306LinkGoogle Scholar
  • 10. Schlemm E, Ebinger M, Nolte CH, Endres M, Schlemm L. Optimal transport destination for ischemic stroke patients with unknown vessel status: use of prehospital triage scores.Stroke. 2017; 48:2184–2191. doi: 10.1161/STROKEAHA.117.017281LinkGoogle Scholar
  • 11. Bogle BM, Asimos AW, Rosamond WD. Regional evaluation of the severity-based stroke triage algorithm for emergency medical services using discrete event simulation.Stroke. 2017; 48:2827–2835. doi: 10.1161/STROKEAHA.117.017905LinkGoogle Scholar
  • 12. Holodinsky JK, Williamson TS, Demchuk AM, Zhao H, Zhu L, Francis MJ, et al. Modeling stroke patient transport for all patients with suspected large-vessel occlusion.JAMA Neurol. 2018; 75:1477–1486. doi: 10.1001/jamaneurol.2018.2424CrossrefMedlineGoogle Scholar
  • 13. Milne MS, Holodinsky JK, Hill MD, Nygren A, Qiu C, Goyal M, et al. Drip ‘n ship versus mothership for endovascular treatment: modeling the best transportation options for optimal outcomes.Stroke. 2017; 48:791–794. doi: 10.1161/STROKEAHA.116.015321LinkGoogle Scholar
  • 14. Xu Y, Parikh NS, Jiao B, Willey JZ, Boehme AK, Elkind MSV. Decision analysis model for prehospital triage of patients with acute stroke.Stroke. 2019; 50:970–977. doi: 10.1161/STROKEAHA.118.023272LinkGoogle Scholar
  • 15. Emberson J, Lees KR, Lyden P, Blackwell L, Albers G, Bluhmki E, et al; Stroke Thrombolysis Trialists’ Collaborative Group. 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.Lancet. 2014; 384:1929–1935. doi: 10.1016/S0140-6736(14)60584-5CrossrefMedlineGoogle Scholar
  • 16. Saver JL, Goyal M, van der Lugt A, Menon BK, Majoie CB, Dippel DW, et al; HERMES Collaborators. Time to treatment with endovascular thrombectomy and outcomes from ischemic stroke: a meta-analysis.JAMA. 2016; 316:1279–1288. doi: 10.1001/jama.2016.13647CrossrefMedlineGoogle Scholar
  • 17. Wolf PA, Kannel WB, Dawber TR. Prospective investigations: the Framingham study and the epidemiology of stroke.Adv Neurol. 1978; 19:107–120.MedlineGoogle Scholar
  • 18. Statistisches Amt für Hamburg und Schleswig-Holstein. Die Bevölkerung in Hamburg und Schleswig-Holstein nach Alter und Geschlecht 2017.2018. https://www.statistik-nord.de/zahlen-fakten/bevoelkerung/bevoelkerungsstand-und-entwicklung/dokumentenansicht/bevoelkerung-in-schleswig-holstein-1/. Accessed May 5, 2019.Google Scholar
  • 19. Landesamt für Statistik Niedersachsen. Bevölkerung nach Alter, Geschlecht und Familienstand 2017.2019. https://www.statistik.niedersachsen.de/startseite/themen/bevolkerung/themenbereich-bevoelkerung-statistische-berichte-172949.html. Accessed May 5, 2019.Google Scholar
  • 20. Schlemm L, Endres M, Scheitz JF, Ernst M, Nolte CH, Schlemm E. Comparative evaluation of 10 prehospital triage strategy paradigms for patients with suspected acute ischemic stroke.J Am Heart Assoc. 2019; 8:e012665. doi: 10.1161/JAHA.119.012665LinkGoogle Scholar
  • 21. Perez de la Ossa N, Carrera D, Gorchs M, Querol M, Millan M, Gomis M, et al. Design and validation of a prehospital stroke scale to predict large arterial occlusion: the Rapid Arterial Occlusion Evaluation scale.Stroke. 2014; 45:87–91LinkGoogle Scholar
  • 22. Llanes JN, Kidwell CS, Starkman S, Leary MC, Eckstein M, Saver JL. The Los Angeles Motor Scale (LAMS): a new measure to characterize stroke severity in the field.Prehosp Emerg Care. 2004; 8:46–50.CrossrefMedlineGoogle Scholar
  • 23. Katz BS, McMullan JT, Sucharew H, Adeoye O, Broderick JP. Design and validation of a prehospital scale to predict stroke severity: cincinnati prehospital stroke severity scale.Stroke. 2015; 46:1508–1512. doi: 10.1161/STROKEAHA.115.008804LinkGoogle Scholar
  • 24. Harbison J, Hossain O, Jenkinson D, Davis J, Louw SJ, Ford GA. Diagnostic accuracy of stroke referrals from primary care, emergency room physicians, and ambulance staff using the Face Arm Speech Test.Stroke. 2003; 34:71–76. doi: 10.1161/01.str.0000044170.46643.5eLinkGoogle Scholar
  • 25. Kamal N, Holodinsky JK, Stephenson C, Kashayp D, Demchuk AM, Hill MD, et al. Improving door-to-needle times for acute ischemic stroke: effect of rapid patient registration, moving directly to computed tomography, and giving alteplase at the computed tomography scanner.Circ Cardiovasc Qual Outcomes. 2017; 10:e003242.LinkGoogle Scholar
  • 26. Ormseth CH, Sheth KN, Saver JL, Fonarow GC, Schwamm LH. The American Heart Association’s Get With the Guidelines (GWTG)-stroke development and impact on stroke care.Stroke Vasc Neurol. 2017; 2:94–105. doi: 10.1136/svn-2017-000092CrossrefMedlineGoogle Scholar
  • 27. Kamal N, Smith EE, Jeerakathil T, Hill MD. Thrombolysis: improving door-to-needle times for ischemic stroke treatment - A narrative review.Int J Stroke. 2018; 13:268–276. doi: 10.1177/1747493017743060CrossrefMedlineGoogle Scholar
  • 28. Heldner MR, Hsieh K, Broeg-Morvay A, Mordasini P, Bühlmann M, Jung S, et al. Clinical prediction of large vessel occlusion in anterior circulation stroke: mission impossible?J Neurol. 2016; 263:1633–1640. doi: 10.1007/s00415-016-8180-6CrossrefMedlineGoogle Scholar
  • 29. Pride GL, Fraser JF, Gupta R, Alberts MJ, Rutledge JN, Fowler R, et al; Standards and Guidelines Committee of the Society of NeuroInterventional Surgery (SNIS). Prehospital care delivery and triage of stroke with emergent large vessel occlusion (ELVO): report of the standards and guidelines committee of the society of neurointerventional surgery.J Neurointerv Surg. 2017; 9:802–812. doi: 10.1136/neurintsurg-2016-012699CrossrefMedlineGoogle Scholar
  • 30. Becker LR, Zaloshnja E, Levick N, Li G, Miller TR. Relative risk of injury and death in ambulances and other emergency vehicles.Accid Anal Prev. 2003; 35:941–948. doi: 10.1016/s0001-4575(02)00102-1CrossrefMedlineGoogle Scholar
  • 31. Gupta R, Horev A, Nguyen T, Gandhi D, Wisco D, Glenn BA, et al. Higher volume endovascular stroke centers have faster times to treatment, higher reperfusion rates and higher rates of good clinical outcomes.J Neurointerv Surg. 2013; 5:294–297. doi: 10.1136/neurintsurg-2011-010245CrossrefMedlineGoogle Scholar
  • 32. Saber H, Navi BB, Grotta JC, Kamel H, Bambhroliya A, Vahidy FS, et al. Real-world treatment trends in endovascular stroke therapy.Stroke. 2019; 50:683–689. doi: 10.1161/STROKEAHA.118.023967LinkGoogle Scholar
  • 33. Fiorella D, Hirsch JA, Woo HH, Rasmussen PA, Shazam Hussain M, Hui FK, et al. Should neurointerventional fellowship training be suspended indefinitely?J Neurointerv Surg. 2012; 4:315–318. doi: 10.1136/neurintsurg-2012-010471CrossrefMedlineGoogle Scholar
  • 34. Yan C, Zheng Y, Hill MD, Mann B, Jeerakathil T, Kamal N, et al. Health technology optimization analysis: conceptual approach and illustrative application.MDM Policy Pract. 2018; 3:2381468318774804. doi: 10.1177/2381468318774804CrossrefGoogle Scholar

eLetters(0)

eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.