Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study

Background: Clinical stroke rehabilitation decision making relies on multi-modal data, including imaging and other clinical assessments. However, most previously described methods for predicting long-term stroke outcomes do not make use of the full multi-modal data available. The aim of this work was to develop and evaluate the benefit of nested regression models that utilise clinical assessments as well as image-based biomarkers to model 30-day NIHSS. Method: 221 subjects were pooled from two prospective trials with follow-up MRI or CT scans, and NIHSS assessed at baseline, as well as 48-hours and 30 days after symptom onset. Three prediction models for 30-day NIHSS were developed using a support vector regression model: one clinical model based on modifiable and non-modifiable risk factors (M_CLINICAL) and two nested regression models that aggregate clinical and image-based features that differed with respect to the method used for selection of important brain regions for the modelling task. The first model used the widely accepted RreliefF (M_RELIEF) machine learning method for this purpose, while the second model employed a lesion-symptom mapping technique (M_LSM) often used in neuroscience to investigate structure-function relationships and identify eloquent regions in the brain. Results: The two nested models achieved a similar performance while considerably outperforming the clinical model. However, M_RELIEF required fewer brain regions and achieved a lower mean absolute error than M_LSM while being less computationally expensive. Conclusion: Aggregating clinical and imaging information leads to considerably better outcome prediction models. While lesion-symptom mapping is a useful tool to investigate structure-function relationships of the brain, it does not lead to better outcome predictions compared to a simple data-driven feature selection approach, which is less computationally expensive and easier to implement.