Predicting the Emergence of Major Neurocognitive Disorder Within Three Months After a Stroke

Abstract

Background: Neurocognitive disorder (NCD) is common after stroke, with major NCD appearing in about 10% of survivors of a first-ever stroke. We aimed to classify clinical- and imaging factors related to rapid development of major NCD 3 months after a stroke, so as to examine the optimal composition of factors for predicting rapid development of the disorder. We hypothesized that the prediction would mainly be driven by neurodegenerative as opposed to vascular brain changes.Methods: Stroke survivors from five Norwegian hospitals were included from the “Norwegian COgnitive Impairment After STroke” (Nor-COAST) study. A support vector machine (SVM) classifier was trained to distinguish between patients who developed major NCD 3 months after the stroke and those who did not. Potential predictor factors were based on previous literature and included both vascular and neurodegenerative factors from clinical and structural magnetic resonance imaging findings. Cortical thickness was obtained via FreeSurfer segmentations, and volumes of white matter hyperintensities (WMH) and stroke lesions were semi-automatically gathered using FSL BIANCA and ITK-SNAP, respectively. The predictive value of the classifier was measured, compared between classifier models and cross-validated.Results: Findings from 227 stroke survivors [age = 71.7 (11.3), males = (56.4%), stroke severity NIHSS = 3.8 (4.8)] were included. The best predictive accuracy (AUC = 0.876) was achieved by an SVM classifier with 19 features. The model with the fewest number of features that achieved statistically comparable accuracy (AUC = 0.850) was the 8-feature model. These features ranked by their weighting were; stroke lesion volume, WMH volume, left occipital and temporal cortical thickness, right cingulate cortical thickness, stroke severity (NIHSS), antiplatelet medication intake, and education.Conclusion: The rapid (<3 months) development of major NCD after stroke is possible to predict with an 87.6% accuracy and seems dependent on both neurodegenerative and vascular factors, as well as aspects of the stroke itself. In contrast to previous literature, we also found that vascular changes are more important than neurodegenerative ones. Although possible to predict with relatively high accuracy, our findings indicate that the development of rapid onset post-stroke NCD may be more complex than earlier suggested.

Longitudinal Brain Changes After Stroke and the Association With Cognitive Decline

“…Due to noticeable underestimation of WMH across multiple thresholds, manual editing of the FSL BIANCA output was performed. Full description of WMH segmentation methods is given elsewhere (5).…”

“…Stroke and dementia both pose risks for each other and share many of the same neurodegenerative and cerebrovascular risk factors (2), with preexisting structural brain pathology being a frequent finding in stroke patients (3). Early onset (<3 months) of post-stroke dementia (PSD) has been found to be primarily associated with stroke lesion volume and white matter hyperintensity (WMH) load (4)(5)(6)(7). The likelihood of developing cognitive impairment after a stroke does however remain elevated for many years after the incident (8), with late onset (>6 months) being mainly associated with the presence of lacunes and a history of ischemic heart disease and stroke (9).…”

Longitudinal brain age prediction and cognitive function after stroke

“…As previously described (Aamodt et al, 2021), WMH segmentation was performed using the fully automated and supervised tool BIANCA (Griffanti et al, 2016). BIANCA is based on an algorithm using the k-nearest neighbor and classifies the probability of WMHs based on intensity and spatial features of voxels.…”

“…Several factors are associated with poorer outcomes after stroke, including old age, lower education, pre-stroke disability, left-sided stroke (Pendlebury, 2012; Pendlebury & Rothwell, 2009), diabetes mellitus, a history of stroke (Lo et al, 2019), larger stroke lesions and vascular factors, such as white matter hyperintensities (WMH) (Schellhorn et al, 2021a; Aamodt et al, 2021). Brain atrophy after stroke (Brodtmann et al, 2020; Levine et al, 2015) is also associated with cognitive decline (Mijajlovic et al, 2017; Haque et al, 2019), particularly if accompanied by hypertension (Sayed et al, 2020) or other vasculopathies (Schellhorn et al, 2021b), with global and medial temporal lobe atrophy and WMH load among the most predictive factors 12 months post-stroke (Jokinen et al, 2020; Casolla, 2019; Wang et al, 2021; Ball et al, 2021; Schellhorn et al, 2021a).…”

“…Advanced age is a major risk factor for both stroke and of poorer prognosis following stroke (Pendlebury & Rothwell, 2009), and is the primary risk factor for neurodegeneration (Hou et al, 2019) and lower cognitive reserve and resilience, rendering the individual more vulnerable to post stroke NCD (Mattson & Arumugam, 2018;Hou et al, 2019; Aamodt et al, 2021). The individual differences in cognitive function among seniors are substantial (Sanchez-Izquierdo & Fernandez-Ballesteros, 2021) and chronological age alone is therefore not a reliable predictor for pre-stroke function and post stroke outcome (Wagner et al, 2016).…”