ArticleCNS neuroscience & therapeutics2025
Development and Validation of a Model to Predict Secondary Arrhythmia in Patients With Epilepsy.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Development and Validation of a Model to Predict Secondary Arrhythmia in Patients With Epilepsy.CNS neuroscience & therapeutics · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
objectiveCompared with healthy individuals, epilepsy patients are more prone to arrhythmias, which may contribute to poor prognosis. To enable early identification of this risk, we developed a clinical prognostic prediction model to assess the risk of arrhythmia comorbidity in epilepsy patients, thereby facilitating timely clinical intervention to improve patient outcomes.
methodsWe retrospectively collected clinical data from epilepsy patients treated at the Affiliated Hospital of Qingdao University between January 2022 and February 2025, including gender, age, medical history, antiseizure medications, electrocardiograms and electroencephalograms. A total of 495 eligible patients were enrolled and randomly divided into development and validation datasets at a 7:3 ratio. Variable selection was performed using LASSO regression with a penalty term, and the selected variables were incorporated into the construction of a logistic regression model. The area under the receiver operating characteristic curve (AUC) and its 95% confidence interval were used to preliminarily evaluate the model's discriminative ability, while cross-validation and bootstrapping were employed to assess its generalizability. Calibration curves and the Brier score were utilized to evaluate the model's calibration, and decision curve analysis was plotted to analyze the net clinical benefit.
resultThe C-indices for the development and validation datasets were 0.737 (95% CI 0.675-0.799) and 0.790 (95% CI: 0.707-0.884), respectively, with an overall C-index of 0.752 (95% CI: 0.701-0.804). The corresponding sensitivity and specificity were 74.6% and 68.1%, respectively. Finally, a nomogram was constructed for the visual presentation of the predictive model.
conclusionOur predictive model can accurately assess the risk of arrhythmia comorbidity in epilepsy patients, assisting clinicians in early intervention to improve prognosis.
Indexed as
Identifiers
What Socratic 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 Socratic graph.