Li Xing, Liu Min, Duan Di-Fei, Yan Yu, Ma Dengyan
Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
PeerJ. 2024 Dec 18;12:e18741. doi: 10.7717/peerj.18741. eCollection 2024.
Bleeding complications following percutaneous renal biopsy (PRB) are a significant clinical concern. This study aimed to validate and refine existing prediction models for post-biopsy bleeding to support more accurate clinical decision-making.
Clinical data from 471 PRB patients were examined in this prospective analysis. Ultrasounds were performed immediately and 6 h post-biopsy to identify perinephric hematomas. Patients exhibiting severe pain, a hemoglobin drop of >10 g/L, symptomatic hypotension, hematuria within 7 days post-procedure underwent repeat ultrasound to assess for bleeding complications. Univariate and multivariable logistic regression analyses were conducted to identify factors associated with bleeding risk. The predictive performance of three kidney biopsy risk calculators (KBRC) was evaluated using the area under the receiver operating characteristic (AUROC) curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) to determine clinical utility. Nomograms were developed for each model to facilitate clinical application.
Univariate analysis identified body mass index (BMI), hemoglobin, and ultrasound findings as significant predictors of bleeding complications. In multivariable analysis, BMI, immediate ultrasound, and 6-h ultrasound data remained significant ( < 0.05). The three models compared included: KBRC-5 (age, body mass index (BMI), platelet count, hemoglobin, kidney size), KBRC-5 with immediate ultrasound data (IKBRC), and KBRC-5 with 6-h hematoma size (SKBRC). The AUROC values for these models were 0.683, 0.786, and 0.867, respectively ( < 0.001). NRI and IDI analyses demonstrated that adding immediate or 6-h ultrasound data significantly improved the risk reclassification ability of the KBRC-5 model ( < 0.05). DCA indicated that IKBRC provided the highest net benefit for risk thresholds between 25% and 77%, while SKBRC was superior for thresholds between 10% and 95%. Nomograms were constructed for each model, allowing clinicians to estimate the probability of bleeding complications by summing scores for each predictor. Calibration curves showed good agreement between predicted and observed probabilities.
Incorporating real-time ultrasound data post-PRB significantly enhances the predictive accuracy and risk reclassification capability of bleeding risk models. These findings provide critical insights for guiding clinical management decisions in patients undergoing renal biopsy.
经皮肾活检（PRB）后的出血并发症是一个重要的临床问题。本研究旨在验证和完善现有的活检后出血预测模型，以支持更准确的临床决策。
在这项前瞻性分析中，检查了471例PRB患者的临床数据。在活检后立即和6小时进行超声检查，以确定肾周血肿。出现严重疼痛、血红蛋白下降>10 g/L、症状性低血压、术后7天内出现血尿的患者接受重复超声检查，以评估出血并发症。进行单因素和多因素逻辑回归分析，以确定与出血风险相关的因素。使用受试者操作特征曲线下面积（AUROC）、净重新分类改善（NRI）、综合辨别改善（IDI）和决策曲线分析（DCA）评估三种肾活检风险计算器（KBRC）的预测性能，以确定临床实用性。为每个模型制定列线图，以促进临床应用。
单因素分析确定体重指数（BMI）、血红蛋白和超声检查结果是出血并发症的重要预测因素。在多因素分析中，BMI、即时超声和6小时超声数据仍然具有显著性（<0.05）。比较的三个模型包括：KBRC-5（年龄、体重指数（BMI）、血小板计数、血红蛋白、肾脏大小）、带有即时超声数据的KBRC-5（IKBRC）和带有6小时血肿大小的KBRC-5（SKBRC）。这些模型的AUROC值分别为0.683、0.786和0.867（<0.001）。NRI和IDI分析表明，添加即时或6小时超声数据显著提高了KBRC-5模型的风险重新分类能力（<0.05）。DCA表明，IKBRC在25%至77%的风险阈值下提供了最高的净效益，而SKBRC在10%至95%的阈值下表现更优。为每个模型构建了列线图，使临床医生能够通过对每个预测因素的得分求和来估计出血并发症的概率。校准曲线显示预测概率和观察概率之间具有良好的一致性。
PRB后纳入实时超声数据可显著提高出血风险模型的预测准确性和风险重新分类能力。这些发现为指导肾活检患者的临床管理决策提供了关键见解。
