Unfortunately, that headline is the strongest takeaway available from these data.
Currently, endovascular therapy for stroke is recommended for all patients with a proximal arterial occlusion and can be treated within six hours. The much-ballyhooed “number needed to treat” for benefit is approximately five, and we have authors generating nonsensical literature with titles such as “Endovascular therapy for ischemic stroke: Save a minute—save a week” based on statistical calisthenics from this treatment effect.
But, anyone actually responsible for making decisions for these patients understands this is an average treatment effect. The profound improvements of a handful of patients with the most favorable treatment profiles obfuscate the limited benefit derived by the majority of those potentially eligible.
These authors have endeavored to apply a bit of precision medicine to the decision regarding endovascular intervention. Using ordinal logistic regression modeling, these authors used the MR CLEAN data to create a predictive model for good outcome (mRS score 0-2 at 90 days). These authors subsequently used the IMS-III data as their validation cohort. The final model displayed a C-statistic of 0.69 for the ordinal model and 0.73 for good functional outcome – which is to say, the output is closer to a coin flip than a informative prediction for use in clinical practice.
More importantly, however, is whether the substrate for the model is anachronistic, limiting its generalizability to modern practice. Beyond MR CLEAN, subsequent trials have demonstrated the importance of underlying tissue viability using either CT perfusion or MRI-based selection criteria when making treatment decisions. Their model is limited in its inclusion of just a measure of collateral circulation on angiogram, which is only a surrogate for potential tissue viability. Furthermore, the MR CLEAN cohort is comprised of only 500 patients, and the IMS-III validation only 260. This sample is far too small to properly develop a model for such a heterogenous set of patients as those presenting with proximal cerebrovascular occlusion. Finally, the choice of logistic regression can be debated, simply from a model standpoint, given its assumptions about underlying linear relationships in the data.
I appreciate the attempt to improve outcomes prediction for individual patients, particularly for a resource-intensive therapy such as endovascular intervention in stroke. Unfortunately, I feel the fundamental limitations of their model invalidate its clinical utility.
“Selection of patients for intra-arterial treatment for acute ischaemic stroke: development and validation of a clinical decision tool in two randomised trials”