Baseline clinical and neuroradiological predictors of outcome in patients with large ischemic core undergoing mechanical thrombectomy: A retrospective multicenter study.

BACKGROUND: Recent randomized trials have shown the benefit of mechanical thrombectomy (MT) also in patients with an established large ischemic core. AIMS: The purpose of this study was to define baseline predictors of clinical outcome in patients with large vessel occlusion (LVO) in the anterior circulation and an Alberta Stroke Program Early CT score (ASPECTS) ⩽ 5, undergoing MT. MATERIAL AND METHODS: The databases of 16 comprehensive stroke centers were retrospectively screened for patients with LVO and ASPECTS ⩽5 that received MT. Baseline clinical and neuroradiological features, including the differential contribution of all ASPECTS regions to the composite score, were collected. Primary clinical outcome measure was a 90-day modified Rankin Scale (mRS) score of 0-2. Statistical analysis used a logistic regression model and random forest algorithm. RESULTS: A total of 408 patients were available for analysis. In multivariate model, among baseline features, lower age (odd ratio (OR) = 0.962, 95% confidence interval (CI) = 0.943-0.982) and lower National Institute of Health Stroke Scale (NIHSS) score (OR = 0.911, 95% CI = 0.862-0.963) were associated with the mRS score 0-2. Involvement of the M2 (OR = 0.398, 95% CI = 0.206-0.770) or M4 (OR = 0.496, 95% CI = 0.260-0.945) ASPECTS regions was associated with an unfavorable outcome. Random forest analysis confirmed that age and baseline NIHSS score are the most important variables influencing clinical outcome, whereas involvement of cortical regions M5, M4, M2, and M1 can have a negative impact. CONCLUSION: Our retrospective analysis shows that, along with age and baseline clinical impairment, presence of early ischemic changes involving cortical areas has a role in clinical outcome in patients with large ischemic core undergoing MT. DATA ACCESS STATEMENT: The data that support the findings of this study are available upon reasonable request.

From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling.

Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information releases. Therefore, increasing the availability and timeliness of price information has become a national and international priority. We present two new datasets in which mobile app-based crowdsourced daily price observations, voluntarily submitted by self-selected participants, are validated in real-time within spatio-temporal markets (pre-processed data). Then, they are reweighted weekly using their geo-location to resemble a formal sample design and allow for more reliable statistical inference (post-sampled data). Using real-time data collected in Nigeria, we assess the accuracy and propose that our reweighted estimates are more accurate with respect to the unweighted version. Results have important implications for governments, food chain actors, researchers and other organisations.