Bao Peng, Sun Yuzhen, Qiu Peng, Li Xiaohui
Fuwai Central China Cardiovascular Hospital, Zhengzhou University, Zhengzhou, China.
Department of Rehabilitation, First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, China.
Front Pharmacol. 2024 Aug 28;15:1389140. doi: 10.3389/fphar.2024.1389140. eCollection 2024.
Vancomycin-associated acute kidney injury (AKI) leads to underestimated morbidity in the intensive care unit (ICU). It is significantly important to predict its occurrence in advance. However, risk factors and nomograms to predict this AKI are limited.
This was a retrospective analysis of two databases. A total of 1,959 patients diagnosed with AKI and treated with vancomycin were enrolled from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. According to the 7:3 ratio, the training set (n = 1,372) and the internal validation set (n = 587) were randomly allocated. The external validation set included 211 patients from the eICU Collaborative Research Database (eICU). Next, to screen potential variables, the least absolute shrinkage and selection operator (LASSO) regression was utilized. Subsequently, the nomogram was developed by the variables of the selected results in the multivariable logistic regression. Finally, discrimination, calibration, and clinical utility were evaluated to validate the nomogram.
The constructed nomogram showed fine discrimination in the training set (area under the receiver operator characteristic curve [AUC] = 0.791; 95% confidence interval [CI]: 0.758-0.823), internal validation set (AUC = 0.793; 95% CI: 0.742-0.844), and external validation set (AUC = 0.755; 95% CI: 0.663-0.847). Moreover, it also well demonstrated calibration and clinical utility. The significant improvement ( < 0.001) in net reclassification improvement (NRI) and integrated differentiation improvement (IDI) confirmed that the predictive model outperformed others.
This established nomogram indicated promising performance in determining individual AKI risk of vancomycin-treated critical care patients, which will be beneficial in making clinical decisions.
万古霉素相关性急性肾损伤（AKI）导致重症监护病房（ICU）的发病率被低估。提前预测其发生具有重要意义。然而，预测这种AKI的危险因素和列线图有限。
这是对两个数据库的回顾性分析。从重症监护医学信息集市IV（MIMIC-IV）数据库中纳入了1959例诊断为AKI并接受万古霉素治疗的患者。按照7:3的比例，随机分配训练集（n = 1372）和内部验证集（n = 587）。外部验证集包括来自电子ICU协作研究数据库（eICU）的211例患者。接下来，为筛选潜在变量，采用了最小绝对收缩和选择算子（LASSO）回归。随后，根据多变量逻辑回归中所选结果的变量构建列线图。最后，评估辨别力、校准度和临床实用性以验证列线图。
构建的列线图在训练集（受试者操作特征曲线下面积[AUC] = 0.791；95%置信区间[CI]：0.758 - 0.823）、内部验证集（AUC = 0.793；95% CI：0.742 - 0.844）和外部验证集（AUC = 0.755；95% CI：0.663 - 0.847）中显示出良好的辨别力。此外，它还很好地展示了校准度和临床实用性。净重新分类改善（NRI）和综合鉴别改善（IDI）的显著改善（< 0.001）证实该预测模型优于其他模型。
该列线图在确定接受万古霉素治疗的重症患者个体AKI风险方面表现出良好性能，这将有助于做出临床决策。
