ArticleFrontiers in neurology
Latent profile analysis of emergency department presentation characteristics in patients with acute ischemic stroke.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to identify potential profile types characterizing emergency department presentations among patients with acute ischemic stroke (AIS), analyze differences in demographic, clinical features, and clinical outcomes across profiles, and explore associations between presentation characteristics and prognosis (modified Rankin Scale, mRS) to inform precision intervention strategies. Methods: Using convenience sampling, 220 AIS patients admitted to our hospital from January to December 2024 were enrolled. The Latent Profile Analysis (LPA) model was expanded to include eight indicators capturing multidimensional prehospital behavioral constructs: time from onset to symptom recognition, time from symptom recognition to decision-making, time from onset to presentation, and Stroke Knowledge Awareness Score, emergency medical services (EMS) utilization, living alone status, residential area, and hospital transfer pathway. Latent Profile Analysis (LPA) was performed using Mplus 8.0 software incorporating all eight indicators. Demographic characteristics, clinical features, and clinical outcomes were compared across profiles using χ Results: LPA successfully identified four latent profiles of emergency department presentation characteristics among AIS patients, with optimal model fit (AIC = 7354.379, BIC = 7452.794, Entropy = 0.976). Based on the expanded indicator set, these profiles were recharacterized as: rapid ems-activated profile (31.36%, Conclusion: Emergency presentation patterns among AIS patients exhibit significant heterogeneity, allowing classification into four latent profiles with distinct behavioral characteristics. Patients across profiles demonstrate marked differences in presentation efficiency, stroke knowledge levels, and clinical outcomes. Latent profile classification independently predicts functional prognosis beyond traditional clinical confounders. Developing stratified intervention strategies based on core profile characteristics can help reduce presentation delays, enhance treatment efficacy, and provide critical guidance for optimizing emergency management of AIS.
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