Endovascular thrombectomy plus medical care versus medical care alone for large ischaemic stroke: 1-year outcomes of the SELECT2 trial.

BACKGROUND: Multiple randomised trials have shown efficacy and safety of endovascular thrombectomy in patients with large ischaemic stroke. The aim of this study was to evaluate long-term (ie, at 1 year) evidence of benefit of thrombectomy for these patients. METHODS: SELECT2 was a phase 3, open-label, international, randomised controlled trial with blinded endpoint assessment, conducted at 31 hospitals in the USA, Canada, Spain, Switzerland, Australia, and New Zealand. Patients aged 18-85 years with ischaemic stroke due to proximal occlusion of the internal carotid artery or of the first segment of the middle cerebral artery, showing large ischaemic core on non-contrast CT (Alberta Stroke Program Early Computed Tomographic Score of 3-5 [range 0-10, with lower values indicating larger infarctions]) or measuring 50 mL or more on CT perfusion and MRI, were randomly assigned, within 24 h of ischaemic stroke onset, to thrombectomy plus medical care or to medical care alone. The primary outcome for this analysis was the ordinal modified Rankin Scale (range 0-6, with higher scores indicating greater disability) at 1-year follow-up in an intention-to-treat population. The trial is registered at ClinicalTrials.gov (NCT03876457) and is completed. FINDINGS: The trial was terminated early for efficacy at the 90-day follow-up after 352 patients had been randomly assigned (178 to thrombectomy and 174 to medical care only) between Oct 11, 2019, and Sept 9, 2022. Thrombectomy significantly improved the 1-year modified Rankin Scale score distribution versus medical care alone (Wilcoxon-Mann-Whitney probability of superiority 0·59 [95% CI 0·53-0·64]; p=0·0019; generalised odds ratio 1·43 [95% CI 1·14-1·78]). At the 1-year follow-up, 77 (45%) of 170 patients receiving thrombectomy had died, compared with 83 (52%) of 159 patients receiving medical care only (1-year mortality relative risk 0·89 [95% CI 0·71-1·11]). INTERPRETATION: In patients with ischaemic stroke due to a proximal occlusion and large core, thrombectomy plus medical care provided a significant functional outcome benefit compared with medical care alone at 1-year follow-up. FUNDING: Stryker Neurovascular.

Enhancing long-term survival prediction with two short-term events: Landmarking with a flexible varying coefficient model.

Patients with cardiovascular diseases who experience disease-related short-term events, such as hospitalizations, often exhibit diverse long-term survival outcomes compared to others. In this study, we aim to improve the prediction of long-term survival probability by incorporating two short-term events using a flexible varying coefficient landmark model. Our objective is to predict the long-term survival among patients who survived up to a pre-specified landmark time since the initial admission. Inverse probability weighting estimation equations are formed based on the information of the short-term outcomes before the landmark time. The kernel smoothing method with the use of cross-validation for bandwidth selection is employed to estimate the time-varying coefficients. The predictive performance of the proposed model is evaluated and compared using predictive measures: area under the receiver operating characteristic curve and Brier score. Simulation studies confirm that parameters under the landmark models can be estimated accurately and the predictive performance of the proposed method consistently outperforms existing methods that either do not incorporate or only partially incorporate information from two short-term events. We demonstrate the practical application of our model using a community-based cohort from the Atherosclerosis Risk in Communities (ARIC) study.

Factors associated with blood mercury concentrations and their interactions with three glutathione S-transferase genes (GSTT1, GSTM1, and GSTP1): an exposure assessment study of typically developing Jamaican children.

BACKGROUND: Jamaican soil is abundant in heavy metals including mercury (Hg). Due to availability and ease of access, fish is a traditional dietary component in Jamaica and a significant source of Hg exposure. Mercury is a xenobiotic and known neuro-toxicant that affects children's neurodevelopment. Human glutathione S-transferase (GST) genes, including GSTT1, GSTM1, and GSTP1, affect Hg conjugation and elimination mechanisms. METHODS: In this exposure assessment study we used data from 375 typically developing (TD) 2-8-year-old Jamaican children to explore the association between environmental Hg exposure, GST genes, and their interaction effects on blood Hg concentrations (BHgCs). We used multivariable general linear models (GLMs). RESULTS: We identified the child's age, consumption of saltwater fish, canned fish (sardine, mackerel), string beans, grain, and starches (pasta, macaroni, noodles) as the environmental factors significantly associated with BHgCs (all P < 0.05). A significant interaction between consumption of canned fish (sardine, mackerel) and GSTP1 in relation to BHgC using either a co-dominant or recessive genetic model (overall interaction P = 0.01 and P < 0.01, respectively) indicated that consumption of canned fish (sardine, mackerel) was significantly associated with higher mean BHgC only among children with the GSTP1 Ile105Val, Ile/Ile [Ratio of mean Hg (95% CI) = 1.59 (1.09, 2.32), P = 0.02] and Ile/Val [Ratio of mean Hg (95% CI) = 1.46 (1.12, 1.91), P = 0.01] genotypes. CONCLUSIONS: Since this is the first study from Jamaica to report these findings, replication in other populations is recommended.

Regression analysis of multivariate recurrent event data allowing time-varying dependence with application to stroke registry data.

In multivariate recurrent event data, each patient may repeatedly experience more than one type of event. Analysis of such data gets further complicated by the time-varying dependence structure among different types of recurrent events. The available literature regarding the joint modeling of multivariate recurrent events assumes a constant dependency over time, which is strict and often violated in practice. To close the knowledge gap, we propose a class of flexible shared random effects models for multivariate recurrent event data that allow for time-varying dependence to adequately capture complex correlation structures among different types of recurrent events. We developed an expectation-maximization algorithm for stable and efficient model fitting. Extensive simulation studies demonstrated that the estimators of the proposed approach have satisfactory finite sample performance. We applied the proposed model and the estimating method to data from a cohort of stroke patients identified in the University of Texas Houston Stroke Registry and evaluated the effects of risk factors and the dependence structure of different types of post-stroke readmission events.

Metabolomic profiles in Jamaican children with and without autism spectrum disorder.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, the underlying metabolic perturbations associated with ASD which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls. Objective: To elucidate potential metabolomic signatures associated with ASD in children and identify specific metabolites that may serve as biomarkers for the disorder. Methods: We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica (ERAJ-2), which was a 1:1 age (±6 months)-and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and imputation of missing values, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Results: Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three of these metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally, the amino acid sarcosine exhibited a significant association with ASD. Conclusions: These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.

Metabolomic Profiles in Jamaican Children With and Without Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, metabolic perturbations associated with ASD, which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls in order to identify specific metabolites that may serve as biomarkers for the disorder. We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica, an age and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and missing data imputation, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. The amino acid sarcosine exhibited a significant association with ASD. These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.