Derivation and validation of a model to predict acute kidney injury following cardiac surgery in patients with normal renal function

The study aimed to construct a clinical model based on preoperative data for predicting acute kidney injury (AKI) following cardiac surgery in patients with normal renal function. A total of 22,348 consecutive patients with normal renal function undergoing cardiac surgery were enrolled. Among them, 15,701 were randomly selected for the training group and the remaining for the validation group. To develop a model visualized as a nomogram for predicting AKI, logistic regression was performed with variables selected using least absolute shrinkage and selection operator regression. The discrimination, calibration, and clinical value of the model were evaluated. The incidence of AKI was 25.2% in the training group. The new model consisted of nine preoperative variables, including age, male gender, left ventricular ejection fraction, hypertension, hemoglobin, uric acid, hypomagnesemia, and oral renin-angiotensin system inhibitor and non-steroidal anti-inflammatory drug within 1 week before surgery. The model had a good performance in the validation group. The discrimination was good with an area under the receiver operating characteristic curve of 0.740 (95% confidence interval, 0.726–0.753). The calibration plot indicated excellent agreement between the model prediction and actual observations. Decision curve analysis also showed that the model was clinically useful. The new model was constructed based on nine easily available preoperative clinical data characteristics for predicting AKI following cardiac surgery in patients with normal kidney function, which may help treatment decision-making, and rational utilization of medical resources.

本研究旨在基于术前临床数据构建预测模型，用于评估肾功能正常患者接受心脏手术后发生急性肾损伤（Acute Kidney Injury, AKI）的风险。共纳入22348例连续入组的肾功能正常心脏手术患者，按随机分组原则将其中15701例划入训练组，剩余患者纳入验证组。为构建可可视化呈现的列线图（nomogram）型AKI预测模型，本研究采用最小绝对收缩与选择算子（least absolute shrinkage and selection operator, LASSO）回归筛选变量，并通过逻辑回归分析搭建模型。随后对模型的区分度、校准度与临床应用价值进行评估。训练组患者的AKI发生率为25.2%。该新型模型共包含9项术前临床变量，具体为年龄、男性性别、左心室射血分数、高血压病史、血红蛋白水平、尿酸水平、低镁血症，以及术前1周内口服肾素-血管紧张素系统抑制剂与非甾体类抗炎药的情况。该模型在验证组中展现出良好的预测性能：区分度优异，受试者工作特征曲线（Receiver Operating Characteristic curve, ROC曲线）下面积为0.740（95%置信区间：0.726~0.753）；校准曲线显示模型预测结果与实际临床观测值拟合度极佳；决策曲线分析同样证实该模型具备临床实用价值。本研究基于9项易于获取的术前临床特征，成功构建了用于预测肾功能正常患者心脏手术后AKI风险的新型临床模型，该模型或可辅助临床治疗决策制定，助力医疗资源的合理配置与利用。