ArticleDiagnostics (Basel, Switzerland)2023
Development and Validation of a Predictive Model Based on LASSO Regression: Predicting the Risk of Early Recurrence of Atrial Fibrillation after Radiofrequency Catheter Ablation.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Development and validation of a machine learning-based prediction model for prolonged length of stay after laparoscopic gastrointestinal surgery: a secondary analysis of the FDP-PONV trial.BMC gastroenterology · 2025Trial
- Development of a nomogram with machine learning-assisted feature selection for predicting left atrial appendage thrombosis and severe spontaneous echo contrast in patients with non-paroxysmal atrial fibrillation.BMC cardiovascular disorders · 2026Article
- Single-Cell RNA Sequencing Revealing Dysregulated Perturbations of Tregs in Psoriasis and Construction of a Treg-Related Diagnostic Model via a 101- Combination Machine Learning Computational FrameworkEndocrine, metabolic & immune disorders drug targets · 2026Article
- Development of a LASSO dynamic prediction system for interbody cage subsidence following OLIF surgery.NPJ digital medicine · 2025Article
- A Novel Clinical Score Integrating Low-Voltage Zones and Biomarkers Predicts Atrial Fibrillation Recurrence Post-Ablation.Clinical cardiology · 2025Article
- Survival prediction modelling in patients with acute ST-segment elevation myocardial infarction with LASSO regression and explainable machine learning.Frontiers in medicine · 2025Article
- Development and validation of a novel nomogram for recurrent hemoptysis after bronchial artery embolization: a population-based cohort study.Frontiers in medicine · 2025Article
- Evaluating Prognosis of Gastrointestinal Metastatic Neuroendocrine Tumors: Constructing a Novel Prognostic Nomogram Based on NETPET Score and Metabolic Parameters from PET/CT Imaging.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Postoperative recurrence prediction model for atrial fibrillation: a meta-analysis.American journal of translational research · 2024Review
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Abstract
backgroundAlthough recurrence rates after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF) remain high, there are a limited number of novel, high-quality mathematical predictive models that can be used to assess early recurrence after RFCA in patients with AF. PURPOSE: To identify the preoperative serum biomarkers and clinical characteristics associated with post-RFCA early recurrence of AF and develop a novel risk model based on least absolute shrinkage and selection operator (LASSO) regression to select important variables for predicting the risk of early recurrence of AF after RFCA.
methodsThis study collected a dataset of 136 atrial fibrillation patients who underwent RFCA for the first time at Peking University Shenzhen Hospital from May 2016 to July 2022. The dataset included clinical characteristics, laboratory results, medication treatments, and other relevant parameters. LASSO regression was performed on 100 cycles of data. Variables present in at least one of the 100 cycles were selected to determine factors associated with the early recurrence of AF. Then, multivariable logistic regression analysis was applied to build a prediction model introducing the predictors selected from the LASSO regression analysis. A nomogram model for early post-RFCA recurrence in AF patients was developed based on visual analysis of the selected variables. Internal validation was conducted using the bootstrap method with 100 resamples. The model's discriminatory ability was determined by calculating the area under the curve (AUC), and calibration analysis and decision curve analysis (DCA) were performed on the model.
resultsIn a 3-month follow-up of AF patients (
conclusionsWe have developed and validated a risk prediction model for the early recurrence of AF after RFCA, demonstrating strong clinical applicability and diagnostic performance. This model plays a crucial role in guiding physicians in preoperative assessment and clinical decision-making. This novel approach also provides physicians with personalized management recommendations.
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