数据表1：房颤伴舒张功能障碍患者导管消融后左心室舒张功能改善预测模型的开发与验证——一项回顾性分析

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Development and validation of a predictive model for left ventricular diastolic improvement following catheter ablation in atrial fibrillation with diastolic dysfunction: a retrospective analysis.docx
• DOI：10.3389/fcvm.2025.1689041.s001
• 发布日期：2026-01-29
• 文件数：1 个
• 文件总体积：294.53 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_validation_of_a_predictive_model_for_left_ventricular_diastolic_improvement_following_catheter_ablation_in_atrial_fibrillation_with_diastolic_dysfunction_a_retrospective_analysis_docx/31188727

命令行下载

curl -L -o "Data Sheet 1_Development and validation of a predictive model for left ventricular diastolic improvement following catheter ablation in atrial fibrill.docx" "https://ndownloader.figshare.com/files/61468714"

文件清单

• Data Sheet 1_Development and validation of a predictive model for left ventricular diastolic improvement following catheter ablation in atrial fibrill.docx（294.53 KB）

文件总体积 294.53 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundThe impact of catheter ablation on left ventricular diastolic function in patients with atrial fibrillation (AF) and left ventricular diastolic dysfunction (LVDD) remains poorly characterised. There are no robust tools available to predict improvement in LVDD post-ablation.
MethodsThe retrospective study enrolled 141 patients diagnosed with AF and LVDD undergoing ablation. LVDD improvement was defined as a reduction of at least one grade in LVDD grade (LVDDG) within 12 ± 3 months after ablation. The predictive model was constructed using a logistic regression with 10-fold validation with stratification. The performance of the model was evaluated using ROC analysis (AUC), calibration plots, and decision curve analysis (DCA). An exploratory sensitivity analysis applying the updated 2025 ASE recommendations for LVDD assessment was additionally performed.
Results49.6% (70/141) of patients showed improvement in LVDD. The predictive model incorporated four independent predictors: higher baseline LVDDG, lower left ventricular mass index (LVMI), lower left ventricular ejection fraction (LVEF), and absence of a history of stroke. The AUC was 0.786 (95% CI 0.760–0.809) in the training cohort and 0.756 (95% CI: 0.677–0.835) in the validation cohort, with acceptable calibration and net clinical benefit across a restricted range of threshold probabilities. In the sensitivity analysis using the redefined LVDD endpoint, discrimination remained comparable (AUC 0.783, 95% CI 0.703–0.856).
ConclusionsA novel model integrating LVDDG, LVMI, LVEF and stroke history effectively predicts post-ablation LVDD improvement in AF patients with LVDD, aiding patient selection for ablation and guiding adjunctive therapies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1689041.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_validation_of_a_predictive_model_for_left_ventricular_diastolic_improvement_following_catheter_ablation_in_atrial_fibrillation_with_diastolic_dysfunction_a_retrospective_analysis_docx/31188727）。