Kallmes, David F

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.

Background As the use of flow diverters has expanded in recent years, predicting successful outcomes has become more challenging for certain aneurysms. Objective To provide neurointerventionalists with an understanding of the available machine learning algorithms for predicting the success of flow diverters in occluding aneurysms. Methods This study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the four major medical databases (PubMed, Embase, Scopus, Web of Science) were screened. The study included original research articles that evaluated the predictive abilities of various machine learning algorithms for determining the success of flow diverters in achieving aneurysm occlusion. Results Five studies out of 217 were included based on our criteria. The included studies used various variables (patient demographics, aneurysm and parent artery characteristics, flow diverter and hemodynamic-related features, and angiographic parametric imaging) to predict flow diverter treatment outcomes. The machine learning algorithms used, along with their respective accuracy rates, were as follows: logistic regression (61% and 85%), support vector machine (88%), Gaussian support vector machine (90%), linear support vector machine (85%), decision tree (80%), random forest (87%), k-nearest neighbors (83% and 85%), XGBoost (87%), CatBoost (86%), deep neural networks (77.9%), and recurrent neural networks (74%).Two studies trained the machine learning models with both all features and the most significant features. Both studies observed that the accuracy of machine learning models decreased by removing the insignificant features. Conclusion The current literature indicates that machine learning algorithms can be trained to predict the success of flow diverters with an accuracy of up to 90%.