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With the development of a clinical decision-making tool, an individualized approach to deciding which stroke patients should get endovascular therapy may be on its way, researchers reported.

Combining various baseline characteristics, multivariable regression produced a model that showed (C statistic 0.73, 95% CI 0.67-0.79) at discriminating which thrombectomy recipients were likely to have a good functional outcome, defined as a modified Rankin Scale (mRS) score from 0 to 2, at 90 days after stroke.

An ordinal model predicting the difference in the odds of a good outcome between stroke therapy with and without intra-arterial treatment had similarly moderate predictive power on external validation (C statistic 0.69, 95% CI 0.64-0.73), according to Esmee Venema, MD, MSc, of Erasmus Medical Center in Rotterdam in the Netherlands, and colleagues in a study published online in the BMJ. Venema's group had their model made available .

"Mean predicted absolute treatment benefit was an 11.8% higher probability of mRS score 0-2 compared with the probability without intra-arterial treatment, and varied from −2.3% to 24.3% between individual patients in the combined derivation and validation cohort," the authors noted.

Venema and colleagues derived the model from all 500 patients in the Multicenter Randomised Clinical Trial for Acute Ischaemic Stroke in the Netherlands (MR CLEAN), who were all enrolled at 16 Dutch hospitals.

A separate validation cohort consisted of 260 patients from the Interventional Management of Stroke III (IMS III) trial, a group that with proven occlusions in the anterior circulation on non-invasive vessel imaging and an available mRS score. This latter group was enrolled at 58 centers across the U.S., Canada, Australia, and Europe.

"We found modest interaction with treatment for history of ischaemic stroke, collateral score, and time from stroke onset to groin puncture," the investigators wrote. "For collateral score and time to groin puncture, interaction with effect of intra-arterial treatment was already shown in previous subgroup analyses. Both variables are clinically likely to cause an interaction with intra-arterial treatment."

"However, previous stroke has not been studied for interaction with treatment before, and was an unexpected finding in our study," they noted. "It may be a chance finding, since it was not reproduced in the validation cohort and we have no clinical explanation."

As for the actual use of the tool in patients with acute ischemic stroke, Venema and colleagues emphasized that the C statistic of 0.69 for the ordinal outcome was a conservative measure.

"It assesses discrimination between exact categories of the mRS, instead of discrimination between two groups with different outcome (e.g., mRS score 0-2 v mRS score 3-6). Externally validated C statistics of all cut-offs were better than the ordinal C statistic (e.g., 0.73 for good functional outcome and 0.75 for mortality)."

"Nevertheless, the relatively small sample size and inclusion of interaction terms in the model may have resulted in some optimism and overfitting, despite shrinkage of the regression coefficients," they admitted.

In addition, the authors said, their model suffered from poor calibration due to the superior outcomes of patients in the IMS III trial.

"Despite its limitations, the currently developed model is the first to predict the effect of intra-arterial treatment for individual patients on arrival at the emergency department. When compared with other models used in neurovascular practice, HAS-BLED (C statistic 0.65) and CHA₂DS₂-VASc (0.61), it performs accurately," Venema's group maintained.

"More importantly, our study shows that treatment should not be withheld based on one characteristic," they wrote. "Some patients belonging to one of the subgroups that are considered as having no benefit of intra-arterial treatment, such as poor collaterals or low ASPECTS, may still benefit from intra-arterial treatment substantially if other characteristics are favourable. This emphasises the importance of making personalized treatment decisions, instead of using average treatment effects, and shows the need for combining multiple clinical and radiological baseline characteristics instead of withholding treatment based on one characteristic."

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