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.

Rate of clinically significant prostate cancer on repeat saturation biopsy after a diagnosis of atypical small acinar proliferation.

BACKGROUND: Atypical small acinar proliferation (ASAP) occurs in approximately 5% of prostate biopsies. Approximately 30%-40% of these patients may develop prostate cancer (PCa) within a 5-year period, often not clinically significant. Current guidelines recommend a repeat biopsy within 3-6 months after the initial diagnosis, but it seem not to be the best strategy. METHODS: Objectives-evaluating the natural history of ASAP, stratifying the risk of csPCa after ASAP, identifying predictive factors of PCa after atypical diagnosis. Materials and methods-retrospective single-institutional study on patients undergoing prostate biopsy for suspicious PCa (2005-2016). We evaluated the incidence of overall PCa, intermediate-high risk of PCa and csPCa in case of ASAP, according to D'Amico classification and Epstein modified criteria. RESULTS: Out of 4.567 patients undergoing prostate biopsy, ASAP was detected in 2.6% of cases. All patients with ASAP underwent repeat saturation biopsy within 6 months and PCa was diagnosed in 34.5%. According to D'Amico classification, 26%, 5.9%, and 2.5% had low, intermediate, and high-risk disease, respectively. According modified Epstein criteria, the incidence of csPCa was 12.6%. LRT showed that the overall probability to develop PCa doubled when PSA density (PSAD) moved from values lower than 0.13 ng/ml/cc to class 0.13-0.30 ng/ml/cc, and it tripled when PSAD was higher than 0.30 ng/ml/cc. CONCLUSIONS: The rate of csPCa in patients with an initial diagnosis of ASAP who had repeat biopsy was 12.6%. The overall PCa rate was 34.5%. Among patient undergoing RP, an upgrading from ncsPCa to csPCa was reported in 35% of cases. PSAD is the only predictive factor directly associated to the risk of developing PCa on repeat biopsy. These findings suggest that immediate repeat biopsy remains the correct strategy in absence of novel predictor factors and non-invasive diagnostic evaluations.

Evaluating health facility access using Bayesian spatial models and location analysis methods.

BACKGROUND: Floating catchment methods have recently been applied to identify priority regions for Automated External Defibrillator (AED) deployment, to aid in improving Out of Hospital Cardiac Arrest (OHCA) survival. This approach models access as a supply-to-demand ratio for each area, targeting areas with high demand and low supply for AED placement. These methods incorporate spatial covariates on OHCA occurrence, but do not provide precise AED locations, which are critical to the initial intent of such location analysis research. Exact AED locations can be determined using optimisation methods, but they do not incorporate known spatial risk factors for OHCA, such as income and demographics. Combining these two approaches would evaluate AED placement impact, describe drivers of OHCA occurrence, and identify areas that may not be appropriately covered by AED placement strategies. There are two aims in this paper. First, to develop geospatial models of OHCA that account for and display uncertainty. Second, to evaluate the AED placement methods using geospatial models of accessibility. We first identify communities with the greatest gap between demand and supply for allocating AEDs. We then use this information to evaluate models for precise AED location deployment. METHODS: Case study data set consisted of 2802 OHCA events and 719 AEDs. Spatial OHCA occurrence was described using a geospatial model, with possible spatial correlation accommodated by introducing a conditional autoregressive (CAR) prior on the municipality-level spatial random effect. This model was fit with Integrated Nested Laplacian Approximation (INLA), using covariates for population density, proportion male, proportion over 65 years, financial strength, and the proportion of land used for transport, commercial, buildings, recreation, and urban areas. Optimisation methods for AED locations were applied to find the top 100 AED placement locations. AED access was calculated for current access and 100 AED placements. Priority rankings were then given for each area based on their access score and predicted number of OHCA events. RESULTS: Of the 2802 OHCA events, 64.28% occurred in rural areas, and 35.72% in urban areas. Additionally, over 70% of individuals were aged over 65. Supply of AEDs was less than demand in most areas. Priority regions for AED placement were identified, and access scores were evaluated for AED placement methodology by ranking the access scores and the predicted OHCA count. AED placement methodology placed AEDs in areas with the highest priority, but placed more AEDs in areas with more predicted OHCA events in each grid cell. CONCLUSION: The methods in this paper incorporate OHCA spatial risk factors and OHCA coverage to identify spatial regions most in need of resources. These methods can be used to help understand how AED allocation methods affect OHCA accessibility, which is of significant practical value for communities when deciding AED placements.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods.

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran's I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran's I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran's I=0.452, P=0.001), and in the OLS regression residuals (Global Moran's I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=-0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=-0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.