Back
For best output, select "Paper Size" as "A4" and "Margin" as "0" or "None".
To save or print to PDF, please select Print Destination > Save as PDF, enable Background Graphics under "More Settings", then click "Save".
During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Post-procedural bleeding is a significant complication of percutaneous renal biopsy, a crucial diagnostic tool in nephrology. The absence of a precise, quantitative risk assessment system often leaves clinicians reliant on experience. This study aimed to develop and validate a robust risk prediction model for post-biopsy bleeding by integrating pre-puncture clinical characteristic parameters, thereby providing an objective tool to improve patient safety.
A retrospective analysis was conducted on 846 patients who underwent renal biopsy between January 2022 and January 2025. Patients were stratified into a non-bleeding group (n=794) and a bleeding group (n=52) based on established post-operative bleeding criteria. Univariate analysis identified potential risk factors, which were subsequently refined using LASSO regression for variable selection to prevent overfitting. Independent predictors were identified via binary logistic regression. A nomogram prediction model was constructed using these independent factors. The model's performance was evaluated by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve, calibration plots, and Decision Curve Analysis (DCA). Internal validation was performed using the Bootstrap method (n=1000 resamples).
单因素分析显示，穿刺次数、肾病综合征状态、24 h尿蛋白定量、血红蛋白（Hb）、活化部分凝血活酶时间（APTT）、估算肾小球滤过率（eGFR）及D-二聚体水平在各组间差异均有统计学意义（P<0.05）。LASSO回归分析选取11个变量进行进一步分析，经二元logistic回归分析证实，较低的Hb水平（OR：0.112，95% CI：0.028～0.442，P=0.002）和较低的eGFR水平（OR：0.043，95% CI：0.007～0.273，P=0.001）是出血的独立保护因素。最终预测模型结合标准化Hb和eGFR，表现出良好的判别能力，AUC为0.853（95% CI：0.787-0.918）。校准图显示预测结果与观察结果之间具有良好的一致性（绝对误差=0.024，Hosmer-Lemeshow检验p值=0.856）。DCA分析表明，该模型在合理的概率阈值范围内具有临床实用性。
The developed nomogram, utilizing readily available pre-procedural clinical parameters (Hb and eGFR), effectively predicts the risk of bleeding following renal biopsy. This tool provides a quantitative means for clinicians to identify high-risk patients, enabling the implementation of personalized preventive strategies to improve procedural safety.”
