Kim Jee Yun, Kim Kyun Young, Yee Jeong, Gwak Hye Sun
College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea.
Department of Pharmacy, Catholic Kwandong University International St. Mary's Hospital, Incheon, South Korea.
Front Pharmacol. 2022 Mar 7;13:815188. doi: 10.3389/fphar.2022.815188. eCollection 2022.
Vancomycin-associated acute kidney injury (AKI) remains a major challenge for patients and clinicians. This study aimed to construct a risk scoring system for vancomycin-associated AKI. We retrospectively reviewed medical records of patients who underwent therapeutic drug monitoring for vancomycin from June 2018 to July 2019. We selected possible risk factors for AKI by univariate and multivariable logistic regression analyses and developed a scoring system for vancomycin-associated AKI. Machine learning methods were utilized to predict risk factors for the occurrence of AKI. The incidence of vancomycin-associated AKI was 31.7% among 104 patients included in this study. A bodyweight ≤60 kg (two points), a Charlson comorbidity index ≥3 (two points), a vancomycin trough serum level >15 μg/ml (one point), and concomitant use of ≥6 nephrotoxic agents (two points) were included to construct a risk scoring system based on the coefficient from the logistic regression model. The area under the receiver operating characteristic curve (AUROC) (mean, 95% confidence interval (CI)) across 10 random iterations using five-fold cross-validated multivariate logistic regression, elastic net, random forest, support vector machine (SVM)-linear kernel, and SVM-radial kernel models was 0.735 (0.638-0.833), 0.737 (0.638-0.835), 0.721 (0.610-0.833), 0.739 (0.648-0.829), and 0.733 (0.640-0.826), respectively. For total scores of 0-1, 2-3, 4-5, 6-7, the risk of vancomycin-associated AKI was 5, 25, 45, and 65%, respectively. Our scoring system can be applied to clinical settings in which several nephrotoxic agents are used along with vancomycin therapy.
万古霉素相关的急性肾损伤(AKI)对患者和临床医生来说仍然是一个重大挑战。本研究旨在构建一个万古霉素相关AKI的风险评分系统。我们回顾性分析了2018年6月至2019年7月接受万古霉素治疗药物监测的患者的病历。通过单因素和多因素逻辑回归分析选择AKI的可能风险因素,并开发了一个万古霉素相关AKI的评分系统。利用机器学习方法预测AKI发生的风险因素。本研究纳入的104例患者中,万古霉素相关AKI的发生率为31.7%。纳入体重≤60 kg(2分)、Charlson合并症指数≥3(2分)、万古霉素血清谷浓度>15 μg/ml(1分)以及同时使用≥6种肾毒性药物(2分),根据逻辑回归模型的系数构建风险评分系统。使用五折交叉验证的多因素逻辑回归、弹性网络、随机森林、支持向量机(SVM)-线性核和SVM-径向核模型,在10次随机迭代中,受试者操作特征曲线(AUROC)下面积(均值,95%置信区间[CI])分别为0.735(0.638 - 0.833)、0.737(0.638 - 0.835)、0.721(0.610 - 0.833)、0.739(0.648 - 0.829)和0.733(0.640 - 0.826)。总分为0 - 1分、2 - 3分、4 - 5分、6 - 7分时,万古霉素相关AKI的风险分别为5%、25%、45%和65%。我们的评分系统可应用于同时使用多种肾毒性药物和万古霉素治疗的临床环境。