Halak, Abdulrahman Al

Background: Acute stroke lesions become more hypodense on non-contrast CT as the ischemic injury matures. Lesions transition to established infarcts at which time recanalization treatment is thought to be futile, if not harmful. As it is not possible to directly quantify the degree of injury progression, eligibility for treatment is determined using temporal criteria. In this study we used an AI approach to segment and quantify the degree of hypoattenuation of acute stroke lesions on baseline CTs, and explored its impact on clinical outcome following mechanical thrombectomy. Methods: Imaging was collected from a multicentric registry (across 4 US and 5 EU sites) of consecutive large vessel occlusion (LVO) stroke patients who underwent mechanical thrombectomy. Baseline NCCT scans were processed using Brainomix 360 to automatically segment the ischemic core. Lesion hypoattenuation was then quantified by in-house software using the mean Net-Water-Uptake (NWU). Multivariate models were used to explore the association between NWU and clinical outcome (3-month modified Rankin scale–mRS), controlling for e-ASPECTS scores, age, and recanalization status. Interaction models were also used to examine the relationship between NWU e-ASPECTS scores. Results: Of the 1363 patients included (age: median 70, IQR: 50-79; 571 females), successful recanalization (mTICI 2b-3) was achieved for 1119 patients. Multivariate analysis found NWU (p=<.001, t=4.62; Fig1) to remain an independent predictor of outcome. There was a significant interaction effect between and ASPECTS score (p=0.029, Fig 2), suggesting that degree of hypoattenuation played a larger role in patients with lower aspects value. In subgroup analysis, NWU was not a significant predictor of outcome in patients that did not achieve successful recanalizion (mTICI 0-2a). Conclusions: These findings suggest that stroke lesion hypodensity measured using NWU might help identify patients at risk of futile recanalization, and that incorporating this information might be more relevant in patients with lower ASPECTS. The ability to determine treatment eligibility using information extracted from routine imaging would allow safe extension of treatment to patients with unclear onset time, and would account for differences in rates of infarct progression.

Background: There is an unmet need to better understand the likelihood of a good functional outcome in older and frailer populations undergoing endovascular therapy (EVT) following large-vessel occlusion (LVO) ischemic stroke. One approach is to improve the understanding of pre-morbid brain health (“brain frailty”) as a predictor of functional outcome. This study explored the relationship between the spatial pattern of brain atrophy and functional outcome to determine whether this is a predictor of outcomes following EVT. Methods: Patients with LVO from four hospitals in Europe and the USA were included. Three cohorts were used as a derivation cohort, and the fourth served for validation. Presenting CT imaging was processed using Brainomix in-house software to automatically localize and quantify atrophy (frailty-index). Masks from the derivation cohort were used to localize regions of atrophy significantly associated with poorer functional outcome (modified Ranking Scale 3-6) using a voxel-based, multivariate method (Sparse Canonical-Correlation-Analysis) (see Figure 1). For both derivation and validation cohorts, the degrees of overlap between each patient’s atrophy mask and the regions associated at a group level with poor outcome were calculated. This was used as an independent predictor of functional outcome in multivariate analysis adjusting for overall brain frailty-index, recanalization status, and ASPECT score Results: The derivation cohort consisted of 658 patients (mean age 69.6±15.0, median ASPECTS 9 [7-10]), while the validation cohort consisted of 447 patients (mean age 64.7 ±15.5, median ASPECTS 8 [5-10] ). Atrophy localized to the lateral ventricles and sylvian fissure was significantly associated with poorer outcomes (Figure 1). In both derivation and validation cohort, the presence of this spatial pattern of brain frailty (quantified using the degree of overlap) was an independent predictor of poor functional outcome (Table 1). Conclusion: A better understanding of pre-stroke brain health, such as the distribution of atrophy, may provide additional information to inform the prediction of outcome following LVO. This could improve patient selection for treatment following ischemic stroke through better prognostication and a more personalized approach to decision making.