Editorial Comment—What Can Models Teach Us About Stroke Treatment?

Abstract

The report in this issue of Stroke on the cost-effectiveness of recombinant tissue plasminogen activator (rt-PA)1 suggests – with notable uncertainty – that, when compared with standard acute stroke care, thrombolytic therapy provides, on average, greater health benefits (in terms of average quality adjusted life years [QALYs]) at a reasonable average medical cost. This is not itself a unique finding; other similar efforts suggest – with notably greater certainty – that rt-PA is a good value for money.2–4 What have we learned from these modeling exercises? Is it true, as the authors propose, that we need another trial of rt-PA? Before considering the answer, it is useful to briefly review a few basics of disease models – what they are and what they can teach us. Disease models are mathematical representations of a clinical condition, its development, and its outcome, and models are often used to evaluate the impact of potential diagnostic or therapeutic strategies. These models are usually implemented in computer code, ranging from something as simple as a spreadsheet formula to sophisticated clinical event simulations. Sandercock and colleagues1 frame the question of rt-PA use as a decision tree, with a Markov model as a “calculation engine.” The inputs to this model are epidemiological, clinical trial, and health economic data (specifically applicable to the context of the …