Individualized prescriptive inference in ischaemic stroke

Area of Science:

• Neuroscience
• Medical Statistics
• Computational Biology

Background:

• Randomized controlled trials (RCTs) are the gold standard for ischaemic stroke treatment, often assuming population homogeneity.
• Brain complexity (functional, connective, vascular) introduces heterogeneity, violating RCT assumptions and causing errors in treatment inference.
• Quantifying the impact of this heterogeneity on interventional inference is challenging due to its counterfactual nature.

Purpose of the Study:

• To evaluate the impact of complex modeling and detailed lesion data on individualized treatment prescriptions for ischaemic stroke.
• To compare the accuracy of treatment effect estimation using models of varying complexity under confounded outcomes and noisy responses.
• To determine if complex models can enhance prescriptive inference in ischaemic stroke, even with imperfect data.

Main Methods:

• Conducted extensive semi-synthetic, biologically plausible virtual interventional trials (100M+ simulations).
• Generated virtual trial data using meta-analytic data (connective, functional, genetic, receptor) and high-resolution acute ischaemic lesion maps (4K+).
• Estimated treatment effects using models of differing complexity, incorporating confounding and response noise.

Main Results:

• Individualized prescriptions from simple models (fitted to unconfounded data) were less accurate than those from complex models.
• Complex models demonstrated superior accuracy even when fitted to confounded data.
• Richly represented lesion data within complex models significantly improved individualized prescriptive inference.

Conclusions:

• Complex modeling approaches incorporating detailed lesion data are crucial for accurate individualized treatment in ischaemic stroke.
• Simple models and assumptions of homogeneity are insufficient for precise treatment recommendations in heterogeneous patient populations.
• Advanced computational methods offer substantial potential to enhance clinical decision-making for ischaemic stroke patients.