Elena Zapata-Arriaza, MD

Venema E, Lingsma HF, Chalos V, Mulder MJHL, Lahr MMH, van der Lugt A, et al. Personalized Prehospital Triage in Acute Ischemic Stroke: A Decision-Analytic Model. Stroke. 2019;50:313–320.

Delay in the administration of required treatment in ischemic stroke can worsen the patient’s functional prognosis. Which patient needs direct transfer to a primary stroke center or to an intervention center is still a challenge in decision making.

To determine optimal prehospital transportation strategy, the authors performed a decision – analytic model. As described in Figure 1, this model starts with the initial decision of transportation to the primary stroke center or to the nearest endovascular-capable intervention center. The benefit of direct transportation to the intervention center was defined as the average amount of quality-adjusted life years (QALYs) gained by this strategy (difference of >0.02 QALYs (=1 week in full health) was considered clinically relevant). The short-run model calculates the probability of every possible pathway and the associated distribution of the modified Rankin Scale (mRS) score after 3 months. It takes into account driving times, in-hospital workflow characteristics, and time-dependent treatment effects. In each annual cycle of the following Markov model, patients can remain in the same health state or die. These probabilities are based on the age and sex-dependent annual mortality rates, adjusted for previously reported death hazard rate ratios of stroke patients.

Schematic overview of the model structure.

Figure 1. Schematic overview of the model structure. The decision node is represented with a square. The circles represent chance nodes, the circles marked with an M represent Markov models and the triangles represent terminal nodes. EVT indicates endovascular treatment; IVT, treatment with intravenous thrombolytics; and LVO, large vessel occlusion.

The entire range of LVO likelihood (0%-100%) was modeled to calculate the threshold of  each transportation strategy benefit. For the sensitivity analyses, the Rapid Arterial Occlusion Evaluation scale for examples of low risk (14%) and high risk (66%) was employed.

Furthermore, the authors assessed the following cases in the model: a base case (nearest primary stroke center was located at a 20 minutes drive by ambulance, while the intervention center was located at 45 minutes. Driving time between the centers was 35 minutes); an urban scenario (10 minutes from scene to primary stroke center; 20 minutes from scene to intervention center; and 15 minutes intercenter driving time); and a more rural scenario (30, 90, and 75 minutes, respectively). Finally, the authors used Tornado-analysis to explore the importance of model parameters, by varying each parameter at a time while the others were held constant.

Within the main results, the authors found that direct transportation to the intervention center would improve functional outcomes when the likelihood of a large vessel occlusion as a cause of the ischemic stroke was >33%. With a high likelihood of large vessel occlusion (66%, comparable with a Rapid Arterial Occlusion Evaluation score of 5 or above), the benefit of direct transportation to the intervention center was 0.10 quality-adjusted life years (=36 days in full health). Regarding optimal transportation strategy based on the likelihood of large vessel occlusion, in the urban scenario, direct transportation to an intervention center was beneficial when the risk of large vessel occlusion was 24% or higher. In the rural scenario, this threshold was 49% (Figure 2). In the cases with longer workflow times in the primary stroke center, transportation to the intervention center was more favorable, and transportation to the primary stroke center was never preferred for a patient with contra-indications for IVT.

The optimal transportation strategy based on the likelihood of large vessel occlusion.

Figure 2. The optimal transportation strategy based on the likelihood of large vessel occlusion. (A) represents the base case scenario (primary stroke center at 20 min and intervention center at 45 min); (B) the urban scenario (10 and 20 min, respectively); and (C) the rural scenario (30 and 90 min).

Regarding probabilistic sensitivity analysis, the authors found that when the risk of having an LVO was high, direct transportation to the intervention center was preferred in 94% of the simulations and only in 6% of the simulations when mentioned LVO risk was low.

The authors developed an online tool in which input factors and regional workflow times would be adjusted for an individual patient in a specific scenario (https://mrpredicts.shinyapps.io/triage/).

The main rendering of this paper is that direct transportation to an intervention center can be beneficial for patients with a high risk of having an LVO, but will likely lead to worse outcomes when the risk is low, especially in scenarios with longer driving times. Also, the online tool elaboration, which allows individual and local variations choice in order to help the pre-hospital decision, involves a useful way to rule transportation strategy. Such tool can be employed fast in every region to decide final destination, given the possibility of patient characteristic, geographic location or workflow adjustment. An advantage of this model is the independence of specific prehospital stroke scale for model elaboration; however, LVO likelihood is based on different triage stroke scales, so according mentioned scales likelihood transportation strategy would change. So, before this interesting and useful model spreads its performance, prehospital stroke scales should be widely validated, and every center needs an accurate measurement of transport times to each center of their treatment network. Anyway, the elaboration of these models simplifies in a fast and reproducible way the decision making in the transport strategy of patients with ischemic stroke.