Challenges of Outcome Prediction for Acute Stroke Treatment Decisions

Physicians often base their decisions to offer acute stroke therapies to patients around the question of whether the patient will benefit from treatment. This has led to a plethora of attempts at accurate outcome prediction for acute ischemic stroke treatment, which have evolved in complexity over the years. In theory, physicians could eventually use such models to make a prediction about the treatment outcome for a given patient by plugging in a combination of demographic, clinical, laboratory, and imaging variables. In this article, we highlight the importance of considering the limits and nuances of outcome prediction models and their applicability in the clinical setting. From the clinical perspective of decision-making about acute treatment, we argue that it is important to consider 4 main questions about a given prediction model: (1) what outcome is being predicted, (2) what patients contributed to the model, (3) what variables are in the model (considering their quantifiability, knowability at the time of decision-making, and modifiability), and (4) what is the intended purpose of the model? We discuss relevant aspects of these questions, accompanied by clinically relevant examples. By acknowledging the limits of outcome prediction for acute stroke therapies, we can incorporate them into our decision-making more meaningfully, critically examining their contents, outcomes, and intentions before heeding their predictions. By rigorously identifying and optimizing modifiable variables in such models, we can be empowered rather than paralyzed by them.

Automated summary

In the decision-making process about acute treatment, it is important to consider four main questions about a given prediction model: (1) what outcome is being predicted, (2) what patients contributed to the model, (3) what variables are in the model (considering their quantifiability, knowability at the time of decision-making, and modifiability), and (4) what is the intended purpose of the model? By acknowledging the limits of outcome prediction for acute stroke therapies, we can incorporate them into our decision-making more meaningfully, critically examining their contents, outcomes, and intentions before heeding their predictions. By rigorously identifying and optimizing modifiable variables in such models, we can be empowered rather than paralyzed by them.