Departments of Emergency, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, Wenzhou, Zhejiang, China.
Ren Fail. 2023;45(2):2285865. doi: 10.1080/0886022X.2023.2285865. Epub 2023 Nov 23.
Identifying patients at high risk for cardiac arrest-associated acute kidney injury (CA-AKI) helps in early preventive interventions. This study aimed to establish and validate a high-risk nomogram for CA-AKI.
In this retrospective dataset, 339 patients after cardiac arrest (CA) were enrolled and randomized into a training or testing dataset. The Student's -test, non-parametric Mann-Whitney test, or 2 test was used to compare differences between the two groups. Optimal predictors of CA-AKI were determined using the Least Absolute Shrinkage and Selection Operator (LASSO). A nomogram was developed to predict the early onset of CA-AKI. The performance of the nomogram was assessed using metrics such as area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC).
In total, 150 patients (44.2%) were diagnosed with CA-AKI. Four independent risk predictors were identified and integrated into the nomogram: chronic kidney disease, albumin level, shock, and heart rate. Receiver operating characteristic (ROC) analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset 0.774 (95%CI, 0.715-0.833) and testing dataset 0.763 (95%CI, 0.670-0.856). The AUC values for the two groups were calculated and compared using the Hanley-McNeil test. No statistically significant differences were observed between the groups. The calibration curve demonstrated good agreement between the predicted outcome and actual observations. Good clinical usefulness was identified using DCA and CIC.
An easy-to-use nomogram for predicting CA-AKI was established and validated, and the prediction efficiency of the clinical model has reasonable clinical practicability.
识别发生心搏骤停相关性急性肾损伤（CA-AKI）风险较高的患者有助于早期进行预防性干预。本研究旨在建立并验证 CA-AKI 的高危列线图。
本回顾性数据集纳入了 339 例心搏骤停（CA）后患者，并将其随机分为训练或测试数据集。采用 Student's t 检验、非参数 Mann-Whitney U 检验或 2 检验比较两组间的差异。采用最小绝对值收缩和选择算子（LASSO）确定 CA-AKI 的最佳预测因子。开发列线图预测 CA-AKI 的早期发生。采用曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）评估列线图的性能。
共有 150 例患者（44.2%）被诊断为 CA-AKI。确定了 4 个独立的风险预测因子并整合到列线图中：慢性肾脏病、白蛋白水平、休克和心率。受试者工作特征（ROC）分析显示，该列线图在训练数据集（0.774，95%CI：0.715-0.833）和测试数据集（0.763，95%CI：0.670-0.856）中对 CA-AKI 具有良好的鉴别性能。采用 Hanley-McNeil 检验比较两组的 AUC 值。两组间无统计学差异。校准曲线显示预测结果与实际观察结果之间具有良好的一致性。采用 DCA 和 CIC 评估具有良好的临床实用性。
建立并验证了一种用于预测 CA-AKI 的简便易用的列线图，且该临床模型的预测效能具有合理的临床实用性。
