Personalized Prehospital Triage in Acute Ischemic Stroke

Abstract

Background and Purpose- Direct transportation to a center with facilities for endovascular treatment might be beneficial for patients with acute ischemic stroke, but it can also cause harm by delay of intravenous treatment. Our aim was to determine the optimal prehospital transportation strategy for individual patients and to assess which factors influence this decision. Methods- We constructed a decision tree model to compare outcome of ischemic stroke patients after transportation to a primary stroke center versus a more distant intervention center. The optimal strategy was estimated based on individual patient characteristics, geographic location, and workflow times. In the base case scenario, the primary stroke center was located at 20 minutes and the intervention center at 45 minutes. Additional sensitivity analyses included an urban scenario (10 versus 20 minutes) and a rural scenario (30 versus 90 minutes). Results- Direct transportation to the intervention center led to better outcomes in the base case scenario 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). 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%. Other factors influencing the decision included door-to-needle times, door-to-groin times, and the door-in-door-out time. Conclusions- The preferred prehospital transportation strategy for suspected stroke patients depends mainly on the likelihood of large vessel occlusion, driving times, and in-hospital workflow times. We constructed a robust model that combines these characteristics and can be used to personalize prehospital triage, especially in more remote areas.

Keywords: decision tree; emergency medical services; quality-adjusted life years; thrombectomy; transportation.

Figure 1.

Schematic overview of the model structure. The model starts with the initial decision of transportation to the primary stroke center or to the nearest endovascular-capable intervention center. The short-run model calculates the probability of every possible pathway and the associated distribution of the modified Rankin Scale (mRS) score after 3 mo. 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. 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.

Figure 2.

The optimal transportation strategy based on the likelihood of large vessel occlusion. Primary stroke center: the nearest nonendovascular-capable stroke center; intervention center: the nearest endovascular-capable stroke center. 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).

Figure 3.

Screenshot of the online tool. This interactive tool can be used at https://mrpredicts.shinyapps.io/triage/. It shows the effect of changes in individual patient characteristics and regional workflow times on the optimal transportation strategy based on the likelihood of large vessel occlusion.