Li Wentao, Yu Weiguang, Chen Ying, Tan Wenyun, Zhang Fan, Zhang Yingqi
Department of Neurology, The First Hospital of Hebei Medical University, No.89 Donggang Road, Shijiazhuang, Hebei Province, 050031, China.
Department of Orthopedics, The First Affiliated Hospital, Sun Yat-sen University, No. 58, Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, China.
BMC Nephrol. 2025 May 26;26(1):257. doi: 10.1186/s12882-025-04150-y.
This study aims to construct and internally validate a comprehensive nomogram designed for accurately predicting the incidence of acute kidney injury (AKI) in patients undergoing repair surgery for acute Stanford Type A aortic dissection (ATAAD), thereby enhancing postoperative risk management and patient care strategies.
A retrospective analysis of 1471 consecutive patients diagnosed with ATAAD through computed tomography angiography (CTA) and confirmed by surgery at four tertiary medical centers from February 2010 to July 2023 was conducted. The study involved a comprehensive evaluation of 36 variables, categorizing patients into non-AKI and AKI groups. Advanced statistical techniques, including LASSO regression and Logistic regression, were employed. A sophisticated nomogram prediction model was developed using R language, and its efficacy was assessed using the concordance index (C-index), area under the receiver operating characteristic curve (AUC-ROC), and decision curve analysis.
Seven key factors independently predicting AKI were identified, including heart failure (a condition where the heart can't pump blood as well), hyperlipidemia (high levels of fats in the blood), arterial dissection (a serious condition where there is a tear in the wall of a blood vessel), renal insufficiency, blood urea nitrogen (BUN), abnormal electrocardiogram (ECG), and total cholesterol (TC). The AUC-ROC, a measure of the model's ability to distinguish between classes, was 0.850 (95% CI: 0.823-0.877) for the training set, with high sensitivity (76%) and specificity (99%). For the validation set, the AUC-ROC was 0.840 (95% CI: 0.798-0.833), with sensitivity and specificity of 78% and 94%, respectively. The nomogram demonstrated a recalibrated C-index of 0.854 for the training set and 0.752 for the validation set. Decision curve analysis revealed the nomogram's significant net benefit across various clinical threshold probabilities.
The AKI nomogram exhibits robust predictive capabilities, establishing itself as a crucial clinical tool for the early identification of patients at risk for AKI following ATAAD repair surgery. By delivering personalized risk assessments, this nomogram not only optimizes postoperative management strategies but also plays a vital role in enhancing patient outcomes through timely and proactive interventions.
本研究旨在构建并进行内部验证一个综合列线图，用于准确预测急性斯坦福A型主动脉夹层（ATAAD）修复手术患者急性肾损伤（AKI）的发生率，从而加强术后风险管理和患者护理策略。
对2010年2月至2023年7月期间在四个三级医疗中心通过计算机断层扫描血管造影（CTA）诊断并经手术确诊为ATAAD的1471例连续患者进行回顾性分析。该研究对36个变量进行了全面评估，将患者分为非AKI组和AKI组。采用了包括LASSO回归和逻辑回归在内的先进统计技术。使用R语言开发了一个复杂的列线图预测模型，并使用一致性指数（C指数）、受试者操作特征曲线下面积（AUC-ROC）和决策曲线分析对其疗效进行评估。
确定了七个独立预测AKI的关键因素，包括心力衰竭（心脏泵血功能不佳的一种状况）、高脂血症（血液中脂肪水平高）、动脉夹层（血管壁出现撕裂的一种严重状况）、肾功能不全、血尿素氮（BUN）、异常心电图（ECG）和总胆固醇（TC）。训练集用于衡量模型区分不同类别的能力的AUC-ROC为0.850（95%CI：0.823-0.877），敏感性高（76%），特异性高（99%）。验证集的AUC-ROC为0.840（95%CI：0.798-0.833），敏感性和特异性分别为78%和94%。列线图在训练集的重新校准C指数为0.854，在验证集为0.752。决策曲线分析显示列线图在各种临床阈值概率下具有显著的净效益。
AKI列线图具有强大的预测能力，成为ATAAD修复手术后早期识别有AKI风险患者的关键临床工具。通过提供个性化的风险评估，该列线图不仅优化了术后管理策略，还通过及时和积极的干预在改善患者预后方面发挥了至关重要的作用。
