Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study - Rajashekar, Deepthi; Hill, Michael D.; Demchuk, Andrew M.; Goyal, Mayank; Fiehler, Jens; Forkert, Nils D. (Frontiers in Neurology, 2021)
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 (MCLINICAL) 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 (MRELIEF) machine learning method for this purpose, while the second model employed a lesion-symptom mapping technique (MLSM) 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, MRELIEF required fewer brain regions and achieved a lower mean absolute error than MLSM 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.