经导管主动脉瓣置换术后永久起搏器植入风险预测模型系统评价与荟萃分析表格数据集

数据集获取（Figshare）

• 源站标题：Table 2_Risk prediction models for permanent pacemaker implantation following transcatheter aortic valve replacement: a systematic review and meta-analysis.xlsx
• DOI：10.3389/fcvm.2025.1563597.s002
• 发布日期：2025-09-25
• 文件数：1 个
• 文件总体积：11.87 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Risk_prediction_models_for_permanent_pacemaker_implantation_following_transcatheter_aortic_valve_replacement_a_systematic_review_and_meta-analysis_xlsx/30206224

命令行下载

curl -L -o "Table 2_Risk prediction models for permanent pacemaker implantation following transcatheter aortic valve replacement: a systematic review and meta-ana.xlsx" "https://ndownloader.figshare.com/files/58243336"

文件清单

• Table 2_Risk prediction models for permanent pacemaker implantation following transcatheter aortic valve replacement: a systematic review and meta-ana.xlsx（11.87 KB）

文件总体积 11.87 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

ObjectiveTo systematically evaluate the methodological quality and predictive performance of risk prediction models for permanent pacemaker implantation (PPMI) following transcatheter aortic valve replacement (TAVR), identify key predictive factors, and assess the risk of bias and clinical applicability of these models.
MethodsA comprehensive search was conducted across multiple databases, including PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and SinoMed. The search included all records from database inception to January 1, 2025. Two independent researchers screened studies and extracted relevant data.
ResultsA total of 11 studies were included, covering 11 risk prediction models with sample sizes ranging from 184–35,410. The incidence of PPMI after TAVR varied between 7.3% and 31.0%. Frequently identified predictors (present in at least two studies) included right bundle branch block (RBBB), self-expandable valves, PR interval, QRS interval, and atrioventricular block (AVB). All models reported the area under the receiver operating characteristic curve (AUROC), ranging from 0.660–0.916, with seven studies providing calibration metrics. Internal validation was performed in three studies, while one study included both internal and external validation. Ten studies were assessed as having a high risk of bias, primarily due to deficiencies in data analysis. The pooled AUROC for the nine validated models was 0.76 (95% confidence interval: 0.72–0.80), indicating moderate discriminatory ability.
ConclusionExisting risk prediction models for PPMI after TAVR demonstrate moderate predictive performance but are limited by a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should focus on developing more robust models through larger sample sizes, rigorous methodologies, and multi-center external validation.
Systematic Review RegistrationThe protocol for this study is registered with https://www.crd.york.ac.uk/PROSPERO

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2025.1563597.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Risk_prediction_models_for_permanent_pacemaker_implantation_following_transcatheter_aortic_valve_replacement_a_systematic_review_and_meta-analysis_xlsx/30206224）。