ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026
Development and validation of a prediction model for ischemic stroke recurrence risk in patients with ischemic stroke and atrial fibrillation.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe recurrence rate of atrial fibrillation (AF)-related ischemic stroke (IS) remains persistently high, significantly increasing patient mortality, disability, and socioeconomic burden. This underscores an urgent need for a practical tool to predict long-term recurrence risk. This study aimed to investigate the key risk factors for recurrent IS in patients with AF and IS, and to construct and validate a recurrence risk prediction model using LASSO regression.
methodsWe retrospectively enrolled 113 patients with AF complicated by IS between 2017 and 2024, with a follow-up period of up to 7 years. LASSO regression was employed to screen predictive factors and develop a risk model. The model's performance was evaluated using the concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). A restricted cubic spline analysis was conducted to examine the non-linear relationship between age and the risk of recurrent IS. Subgroup analyses were performed using Cox regression models.
resultsDuring the 7-year follow-up, recurrent IS occurred in 45.13% of patients. The LASSO regression-based prediction model, incorporating 19 predictive factors, demonstrated high predictive power with an AUC of 0.917. Furthermore, the model's predictive ability improved over time, achieving a C-index of 0.760 at the seventh year of follow-up. A significant non-linear relationship was identified between age and recurrence risk.
conclusionA LASSO-derived model accurately estimates the long-term risk of recurrent IS after AF-related stroke. Age has a non-linear influence on recurrence, and the efficacy of anticoagulation may be modified by smoking status. These findings support the development of individualized prevention strategies.
Indexed as
Identifiers
42209838What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.