Zhang Xiaoying, Ruan Xianghan, Yu Yao, Sun Tongyan, Zhang Jiaqiang, Cong Xuhui, Lou Jingsheng, Li Hao, Cao Jiangbei, Liu Yanhong, Mi Weidong
Department of Anesthesiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
Medical School of Chinese People's Liberation Army, Beijing, China.
BMC Geriatr. 2025 May 30;25(1):390. doi: 10.1186/s12877-025-06023-3.
To develop and externally validate a risk prediction model for postoperative acute kidney injury (PO-AKI) in elderly patients undergoing noncardiac surgery, addressing the current gap in predictive tools for this vulnerable population.
A multicenter retrospective cohort study presented according to TRIPOD + AI statement.
Conducted in 21 tertiary hospitals across 11 provinces in China from January 2009 to April 2022.
Elderly patients (≥ 65 years) undergoing noncardiac procedures.
The endpoint was PO-AKI within seven days post-surgery, diagnosed using the KDIGO criteria. Data were extracted from electronic medical records for model derivation and validation.
The study included 163,131 elderly patients, with 52,494 for model discovery, 7,899 and 80,641 for external validation. The model incorporated nine variables: age, heart disease history, preoperative hyponatremia, renal surgery (yes/no), surgery type, surgery duration, intraoperative diuretics usage, first-aid vasopressors usage, and blood transfusion. The model demonstrated acceptable discriminative ability with AUROC values of 0.803, 0.793, 0.770, and 0.774 across the training, internal validation, and two external validation datasets, respectively. The calibration plots and decision curve analyses yielded commendable results in both training and validation sets. To streamline usability, we employed risk scores and categorized the population into low-, medium-, and high-risk subgroups.
Clinicians could implement this externally validated risk prediction model to stratify PO-AKI risks in elderly patients during the early postoperative phases of noncardiac surgery.
开发并外部验证一种用于接受非心脏手术的老年患者术后急性肾损伤（PO-AKI）的风险预测模型，以填补针对这一脆弱人群的预测工具方面的当前空白。
根据TRIPOD + AI声明进行的多中心回顾性队列研究。
于2009年1月至2022年4月在中国11个省份的21家三级医院开展。
接受非心脏手术的老年患者（≥65岁）。
终点为术后7天内的PO-AKI，采用KDIGO标准进行诊断。从电子病历中提取数据用于模型推导和验证。
该研究纳入了163,131例老年患者，其中52,494例用于模型发现，7,899例和80,641例用于外部验证。该模型纳入了九个变量：年龄、心脏病史、术前低钠血症、肾脏手术（是/否）、手术类型、手术持续时间、术中利尿剂使用、急救血管加压药使用和输血。该模型在训练集、内部验证集和两个外部验证数据集上分别显示出可接受的判别能力，AUROC值分别为0.803、0.793、0.770和0.774。校准图和决策曲线分析在训练集和验证集上均产生了值得称赞的结果。为简化可用性，我们采用了风险评分并将人群分为低、中、高风险亚组。
临床医生可应用这种经过外部验证的风险预测模型，在非心脏手术的术后早期阶段对老年患者的PO-AKI风险进行分层。
