Neurology最新文献

Background and objectives: The term "embolic stroke of undetermined source" (ESUS) encompasses a substantial but heterogeneous population of patients with ischemic stroke, underscoring the importance of identifying personalized treatment strategies. In subgroups of patients randomized in ESUS trials, we evaluated the effectiveness of anticoagulation compared with antiplatelet therapy in secondary ischemic stroke prevention.

Methods: A study-level meta-analysis was conducted on randomized controlled trials of patients with ESUS, comparing anticoagulation with antiplatelet therapy. The primary efficacy outcome was recurrent ischemic stroke, and safety outcomes were major bleeding and death. Subgroups assessed were age, sex, presence of patent foramen ovale (PFO), left atrial enlargement (LAE), and atrial cardiopathy. Pooled risk ratios (RRs) were meta-analyzed. Cochrane Risk of Bias Tool 2.0 was used for risk-of-bias assessment.

Results: A total of 7 randomized controlled trials involving 14,804 patients were analyzed, with 7,406 patients treated with anticoagulation and 7,398 treated with antiplatelet therapy. Compared with antiplatelet therapy, anticoagulation was associated with a similar rate of recurrent ischemic stroke (RR 0.91, 95% CI 0.80-1.05; I² = 0%). In ESUS with PFO, anticoagulation was associated with significantly lower risk of ischemic stroke (RR 0.59, 95% CI 0.35-0.98; I² = 0%). Heterogeneity was present in those with LAE: antiplatelet therapy was superior in trials allowing cardiac monitoring after randomization (RR 6.65, 95% CI 1.26-35.08; I² = 0%), but anticoagulation was superior in trials prohibiting cardiac monitoring after randomization (RR 0.25 95% CI 0.07-0.89). Subgroups based on age, sex, or presence of atrial cardiopathy did not benefit from anticoagulation over antiplatelet therapy.

Discussion: In this meta-analysis, an empiric anticoagulation approach is not beneficial for patients with ESUS. This finding highlights the importance of an individualized treatment strategy. Such a strategy should include prolonged cardiac monitoring for atrial fibrillation, particularly in patients with moderate-to-severe LAE. Anticoagulation treatment showed promise in patients with medically treated PFO. Other subgroups did not benefit from anticoagulation therapy. Large prospective studies within ESUS subgroups are needed to validate our findings.

In clinical and health services research, clustered data (also known as data with a multilevel or hierarchical structure) are frequently encountered. For example, patients may be clustered or nested within hospitals. Understanding when data have a multilevel structure is important because clustering of individuals can induce a homogeneity in outcomes within clusters, so that, even after adjusting for measured covariates, outcomes for 2 individuals in the same cluster are more likely to be similar than outcomes for 2 individuals from different clusters. Using conventional statistical regression models to analyze clustered data can result in incorrect conclusions being drawn. In particular, estimated CIs may be artificially narrow, and significance levels may be artificially low. As a result, one may conclude that there is a statistically significant association when there is none. To avoid this problem, investigators should ensure that their analyses use techniques that account for clustering of data. Generalized linear models estimated using generalized estimating equation (GEE) methods and multilevel regression models (also known as hierarchical regression models, mixed-effects models, or random-effects models) are two such techniques. We provide an introduction to clustered or multilevel data and describe how GEE models or multilevel models can be used for the analysis of multilevel data.