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Association of stroke lesion distributions with atrial fibrillation detected after stroke

2023-05-24 · Yiming Chen, Sihui Wang, Dong Xu

Background Atrial fibrillation is often missed by traditional intermittent electrocardiogram monitoring after ischemic stroke due to its paroxysmal and asymptomatic nature. The knowledge of the unique characteristics of the population with atrial fibrillation detected after stroke (AFDAS) enables more ischemic stroke patients to benefit from more aggressive anticoagulation therapy and AF management. Method This is an observational, retrospective, MRI imaging-based single-center study. Patients with AFDAS were matched in 1:3 ratio with patients without AF (NoAF)and patients with known AF before stroke(KAF) in PSM model based on age, gender, and time from stroke onset to admission. Multivariate logistic models were used to test the association of MRI-based stroke lesion distribution, other clinical parameters and AF. A backward stepwise elimination regression was conducted to identify the most important variables. Results Compared to the NoAF group(n=103), the patients with AFDAS (n=42) had more cortical involvement(p=0.016), as well as temporal(p<0.001) and insular lobes(p=0.018) infraction. After performing a backward stepwise elimination model in regression analysis, the temporal lobe infraction(OR 0.274, 95%CI 0.090-0.838, p=0.023) remained independently associated with the detection of AF. Compared to the KAF group(n=89), LAD(OR 1.113, 95%CI 1.022-1.211, p=0.014), Number of lobes infarction(p=0.012), 3-lobes involvement(OR 0.177, 95%CI 0.056-0.559, p=0.003), and left hemisphere lobe involvement(OR 5.966, 95%CI 2.273-15.817, p<0.001)) were independently associated with AFDAS and KAF. Conclusions Ischemic stroke patients with AF detected after stroke present more temporal lobe infraction and cortical involvement. These lesion distribution characteristics with clinical characteristics together may help in stratifying patients with long-term cardiac monitoring after stroke.

📄 PDF Abstract BibTeX arXiv:2305.14609

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KAF A Kernel Activation Function is a non-parametric activation function defined as a one-dimensional kernel approximator: $$ f(s) = \sum_{i=1}^D \alpha_i \kappa( s, d_i)…

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