Brain atrophy and endovascular treatment effect in acute ischemic stroke: a secondary analysis of the MR CLEAN trial

Association of Brain Atrophy With Functional Outcome and Recovery Trajectories After Thrombectomy: Post Hoc Analysis of the ESCAPE-NA1 Trial

BACKGROUND AND OBJECTIVES

Brain frailty may impair the ability of acute stroke patients to cope with the injury, irrespective of their chronologic age, resulting in impaired recovery. We aim to investigate the impact of brain atrophy on functional outcome assessed at different time points after endovascular thrombectomy (EVT).

METHODS

In this retrospective post hoc analysis of the ESCAPE-NA1 trial, we analyzed CT imaging data for cortical atrophy by using the GCA scale, including region-specific scales, and subcortical atrophy by using the intercaudate distance to inner table width (CC/IT) ratio. The primary outcome was 90-day mRS (ordinal shift analysis), and the secondary outcome was the mRS score over time. Adjustments were made for age, sex, baseline NIHSS, final infarct volume, stroke laterality, total Fazekas score, and nerinetide-alteplase interaction. Sensitivity analyses were additionally performed in only those patients for whom MRI data were available.

RESULTS

Of 1,102 participants (mean age of 69.5 ± 13.7 years; 554 men), 818 (74%) had GCA = 0, 220 (20%) had GCA = 1, and 64 (6%) had GCA = 2/3. The median CC/IT ratio was 0.12 (IQR0.10-0.15). Cortical atrophy (GCA ≥ 1 vs GCA 0) was associated with worse 90-day mRS (acOR = 1.62 [95% CI 1.22-2.16]; p = 0.001), lower rates of 90-day mRS0-2 (aOR = 0.65 [95% CI 0.45-0.94]; p = 0.022), and higher mortality (aOR = 2.12 [95% CI 1.28-3.5]; p = 0.003), regardless of the region assessed. Subcortical atrophy was associated with worse 90-day mRS (acOR [per 0.01 increase in CC/IT ratio] = 1.07 [95% CI 1.04-1.11]; p < 0.001) and lower rates of 90-day mRS0-2 (aOR = 0.92 [95% CI 0.88-0.97]; p = 0.001). Furthermore, with various degrees of atrophy, we observed heterogeneity in mRS measurements during follow-up: worse mRS scores for higher atrophy grades (p < 0.001). Compared with participants with GCA = 0, the mRS for participants with GCA = 1 was higher at 30 days (adjusted difference = 0.41 [95% CI 0.18-0.65]) and remained worse at 90 days (adjusted difference = 0.72 [95% CI 0.49-0.95]). Similar effects were seen for participants with worse cortical atrophy, regardless of the region assessed, and worse subcortical atrophy. Furthermore, 26/63(41%) and 124/274(45%) patients with severe cortical/subcortical atrophy (GCA 2/3 and highest CC/IT ratio quartile, respectively) achieved good functional outcome (mRS0-2), compared with 539/812(66.4%) with no cortical atrophy and 209/274(76%) in the lowest CC/IT ratio quartile.

DISCUSSION

In this large RCT-derived population, participants with brain atrophy, as visually assessed on acute noncontrast computed tomography imaging, showed less favorable stroke recovery after EVT and worse 90-day functional outcomes compared with participants without brain atrophy. This may support physicians with recovery expectations when planning post-EVT care with patients and their families.

Cortical and Subcortical Brain Atrophy Assessment Using Simple Measures on NCCT Compared with MRI in Acute Stroke

BACKGROUND AND PURPOSE

Brain atrophy is an important surrogate for brain reserve, the capacity of the brain to cope with acquired injuries such as acute stroke. It is unclear how well atrophy measurements on MR imaging can be reproduced using NCCT imaging. We aimed to compare pragmatic atrophy measures on NCCT with MR imaging in patients with acute ischemic stroke.

MATERIALS AND METHODS

This is a post hoc analysis, including baseline NCCT and 24-hour follow-up MR imaging data from the Safety and Efficacy of Nerinetide (NA-1) in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial. Cortical atrophy was measured using the global cortical atrophy scale, and subcortical atrophy was measured using the intercaudate distance-to-inner-table width (CC/IT) ratio. Agreement and correlation between these measures on NCCT and MR imaging were calculated using the Gwet agreement coefficient 1 and Pearson correlation coefficients, respectively.

RESULTS

Among 1105 participants in the ESCAPE-NA1 trial, interpretable NCCT and 24-hour MR imaging were available in 558 (50.5%) patients (mean age, 67.2 [SD, 13.7] years; 282 women). Cortical atrophy assessments performed on NCCT underestimated atrophy severity compared with MR imaging (eg, patients with global cortical atrophy of ≥1 assessed on NCCT = 133/558 [23.8%] and on MR imaging = 247/558 [44.3%]; a 20.5% difference). Overall, cortical (ie, global cortical atrophy) atrophy assessments on NCCT had substantial or better agreement with MR imaging (Gwet agreement coefficient 1 of > 0.784; P < .001). Subcortical atrophy measures (CC/IT ratio) showed strong correlations between NCCT and MR imaging (Pearson correlation = 0.746, P < .001).

CONCLUSIONS

Brain atrophy can be evaluated using simple measures in emergently acquired NCCT. Subcortical atrophy assessments on NCCT show strong correlations with MR imaging. Although cortical atrophy assessments on NCCT are strongly correlated with MR imaging ratings, there is a general underestimation of atrophy severity on NCCT.

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

Objective

Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.

Methods

A total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared.

Results

Automated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.

Conclusion

We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.