Prognosis and Prediction of Asymptomatic Intracranial Hemorrhage After Endovascular Thrombectomy: A Multi-Center Study.

PURPOSE: The impact of asymptomatic intracranial hemorrhage (aICH) on functional outcomes after endovascular thrombectomy (EVT) remains unclear, and tools for forecasting this complication are lacking. We aim to evaluate the clinical relevance of aICH and establish a prediction model. METHODS: Data of patients who received EVT for acute anterior-circulation large vessel occlusion in 3 comprehensive hospitals were retrospectively analyzed. Asymptomatic intracranial hemorrhage was defined as any hemorrhage detected after EVT that did not fulfill the definition of symptomatic intracranial hemorrhage in the European Cooperative Acute Stroke Study. Logistic regression models were performed to assess the impact of aICH on 90-day functional outcomes and identify the predictors of aICH, which were then used to establish a prediction model. The discrimination, calibration, and clinical utility of the model were evaluated. RESULTS: This study included 460 patients, among whom 152 (33.0%) developed aICH after EVT. Asymptomatic intracranial hemorrhage was negatively associated with 90-day excellent outcomes (adjusted odds ratio [OR]: 0.414, 95% confidence interval [CI]: 0.230-0.745, p=0.003) and good outcome (adjusted OR: 0.603, 95% CI: 0.374-0.971, p=0.037), but not with mortality (adjusted OR: 1.110, 95% CI: 0.611-2.017, p=0.732) after adjusted for other predictors of functional outcome. Pre-stroke anticoagulant therapy (OR: 2.233, 95% CI: 1.073-4.647, p=0.032), Alberta stroke program early CT score (OR: 0.842, 95% CI: 0.754-0.939, p=0.002), site of occlusion (internal carotid artery occlusion as the reference; M1 segment of middle cerebral artery occlusion, OR: 2.827, 95% CI: 1.409-5.674, p=0.003; tandem occlusion, OR: 3.928, 95% CI: 1.752-8.806, p=0.001), intravenous thrombolysis (OR: 2.091, 95% CI: 1.362-3.209, p=0.001), and successful recanalization (OR: 0.383, 95% CI: 0.213-0.689, p=0.001) were identified as the predictors of aICH, which were incorporated into a nomogram model. The area under the receiver operating characteristic curve of the model was 0.707 (95% CI: 0.657-0.757), and the calibration plot demonstrated good consistency between actual observed and predicted probability of aICH. Decision curve analysis showed that patients might benefit from the model. CONCLUSION: Asymptomatic intracranial hemorrhage was negatively associated with favorable functional outcome after EVT. We established a nomogram model for predicting aICH, which requires external clinical validation. CLINICAL IMPACT: The impact of asymptomatic intracranial hemorrhage after endovascular thrombectomy on mid-term functional outcome has been controversial. We found that asymptomatic intracranial hemorrhage may also decreased the likelihood of 90-day favourable functional outcome after endovascular thrombectomy, supporting the notion that asymptomatic intracranial hemorrhage at the acute stage may not be benign. Moreover, we established a prediction model for this complication, which may improve clinical evaluation and management of patients who would receive endovascular thrombectomy for large vessel occlusion.

[Research on Oil Sands Spectral Characteristics and Oil Content by Remote Sensing Estimation].

Visible and near infrared spectroscopy is a proven technology to be widely used in identification and exploration of hydrocarbon energy sources with high spectral resolution for detail diagnostic absorption characteristics of hydrocarbon groups. The most prominent regions for hydrocarbon absorption bands are 1,740-1,780, 2,300-2,340 and 2,340-2,360 nm by the reflectance of oil sands samples. These spectral ranges are dominated by various C-H overlapping overtones and combination bands. Meanwhile, there is relatively weak even or no absorption characteristics in the region from 1,700 to 1,730 nm in the spectra of oil sands samples with low bitumen content. With the increase in oil content, in the spectral range of 1,700-1,730 nm the obvious hydrocarbon absorption begins to appear. The bitumen content is the critical parameter for oil sands reserves estimation. The absorption depth was used to depict the response intensity of the absorption bands controlled by first-order overtones and combinations of the various C-H stretching and bending fundamentals. According to the Pearson and partial correlation relationships of oil content and absorption depth dominated by hydrocarbon groups in 1,740-1,780, 2,300-2,340 and 2,340-2,360 nm wavelength range, the scheme of association mode was established between the intensity of spectral response and bitumen content, and then unary linear regression(ULR) and partial least squares regression (PLSR) methods were employed to model the equation between absorption depth attributed to various C-H bond and bitumen content. There were two calibration equations in which ULR method was employed to model the relationship between absorption depth near 2,350 nm region and bitumen content and PLSR method was developed to model the relationship between absorption depth of 1,758, 2,310, 2,350 nm regions and oil content. It turned out that the calibration models had good predictive ability and high robustness and they could provide the scientific basis for rapid estimation of oil content in oil sands in future.

A partition-based approach to identify gene-environment interactions in genome wide association studies.

It is believed that almost all common diseases are the consequence of complex interactions between genetic markers and environmental factors. However, few such interactions have been documented to date. Conventional statistical methods for detecting gene and environmental interactions are often based on the linear regression model, which assumes a linear interaction effect. In this study, we propose a nonparametric partition-based approach that is able to capture complex interaction patterns. We apply this method to the real data set of hypertension provided by Genetic Analysis Workshop 18. Compared with the linear regression model, the proposed approach is able to identify many additional variants with significant gene-environmental interaction effects. We further investigate one single-nucleotide polymorphism identified by our method and show that its gene-environmental interaction effect is, indeed, nonlinear. To adjust for the family dependence of phenotypes, we apply different permutation strategies and investigate their effects on the outcomes.

A robust model-free approach for rare variants association studies incorporating gene-gene and gene-environmental interactions.

Recently more and more evidence suggest that rare variants with much lower minor allele frequencies play significant roles in disease etiology. Advances in next-generation sequencing technologies will lead to many more rare variants association studies. Several statistical methods have been proposed to assess the effect of rare variants by aggregating information from multiple loci across a genetic region and testing the association between the phenotype and aggregated genotype. One limitation of existing methods is that they only look into the marginal effects of rare variants but do not systematically take into account effects due to interactions among rare variants and between rare variants and environmental factors. In this article, we propose the summation of partition approach (SPA), a robust model-free method that is designed specifically for detecting both marginal effects and effects due to gene-gene (G×G) and gene-environmental (G×E) interactions for rare variants association studies. SPA has three advantages. First, it accounts for the interaction information and gains considerable power in the presence of unknown and complicated G×G or G×E interactions. Secondly, it does not sacrifice the marginal detection power; in the situation when rare variants only have marginal effects it is comparable with the most competitive method in current literature. Thirdly, it is easy to extend and can incorporate more complex interactions; other practitioners and scientists can tailor the procedure to fit their own study friendly. Our simulation studies show that SPA is considerably more powerful than many existing methods in the presence of G×G and G×E interactions.

Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches.

Genome-wide association studies have been successful at identifying common disease variants associated with complex diseases, but the common variants identified have small effect sizes and account for only a small fraction of the estimated heritability for common diseases. Theoretical and empirical studies suggest that rare variants, which are much less frequent in populations and are poorly captured by single-nucleotide polymorphism chips, could play a significant role in complex diseases. Several new statistical methods have been developed for the analysis of rare variants, for example, the combined multivariate and collapsing method, the weighted-sum method and a replication-based method. Here, we apply and compare these methods to the simulated data sets of Genetic Analysis Workshop 17 and thereby explore the contribution of rare variants to disease risk. In addition, we investigate the usefulness of extreme phenotypes in identifying rare risk variants when dealing with quantitative traits. Finally, we perform a pathway analysis and show the importance of the vascular endothelial growth factor pathway in explaining different phenotypes.