血运重建后心绞痛预测模型开发与验证数据（Table 1）

数据集获取（Figshare）

• 源站标题：Table 1_Development and validation of a machine learning predictive model for one-month post-revascularization angina in patients who had undergone PCI or CABG.docx
• DOI：10.3389/fcvm.2026.1747832.s001
• 发布日期：2026-02-13
• 文件数：1 个
• 文件总体积：31.73 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_predictive_model_for_one-month_post-revascularization_angina_in_patients_who_had_undergone_PCI_or_CABG_docx/31330438

命令行下载

curl -L -o "Table 1_Development and validation of a machine learning predictive model for one-month post-revascularization angina in patients who had undergone PC.docx" "https://ndownloader.figshare.com/files/61860232"

文件清单

• Table 1_Development and validation of a machine learning predictive model for one-month post-revascularization angina in patients who had undergone PC.docx（31.73 KB）

文件总体积 31.73 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

BackgroundRecurrent angina pectoris following coronary revascularization via percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) poses significant clinical challenges, associated with reduced quality of life and increased healthcare burden. Traditional risk tools have limitations in predicting short-term recurrence. This study aimed to develop and validate a machine learning (ML) predictive model for post-revascularization angina (PRA).
MethodsThis study used patient data from 38 clinical research centers in 23 provinces of China from 2016 to 2018. Data from 626 patients in a derivation cohort recruited from 28 centers across 16 Chinese provinces and 127 in an external validation cohort from another 10 centers across 10 provinces were analyzed. The Boruta algorithm selected key features, and eight ML models were trained on 70% of the derivation cohort, internally validated on 30%, and externally validated. Performance metrics included area under the curve (AUC), decision curve analysis (DCA), accuracy, sensitivity, specificity, and F1 score. The Shapley Additive explanation (SHAP) values provided model interpretability.
ResultsThe Boruta algorithm selected six features: New York Heart Association (NYHA) classification, cardiac troponin T (cTnT), prothrombin time (PT), depression severity, abdominal circumference, and diastolic blood pressure (DBP). The Random forest (RF) model outperformed others, achieving an AUC of 0.90 (accuracy 0.88, sensitivity 0.77, specificity 0.92, F1 0.78) in internal validation and 0.87 in external validation. The SHAP algorithm confirmed the features’ predictive importance, with higher NYHA class, elevated cTnT, and depression severity positively influencing PRA risk.
ConclusionsThis RF model offers a robust, interpretable tool for early PRA risk stratification, integrating cardiac, hemostatic, psychological, and metabolic factors. It supports personalized post-revascularization care, though prospective, multi-ethnic validation is needed to enhance generalizability.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcvm.2026.1747832.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_validation_of_a_machine_learning_predictive_model_for_one-month_post-revascularization_angina_in_patients_who_had_undergone_PCI_or_CABG_docx/31330438）。