Yu Chengxuan, Guo Daihong, Yao Chong, Zhu Yu, Liu Siyuan, Kong Xianghao
Pharmacy Department, Medical Security Center, Chinese PLA General Hospital, Beijing, China.
Graduate School, Chinese PLA General Hospital, Beijing, China.
Front Pharmacol. 2021 Jun 14;12:657853. doi: 10.3389/fphar.2021.657853. eCollection 2021.
Drug-induced acute kidney injury (D-AKI) is associated with increased mortality and longer hospital stays. This study aims to establish a nomogram to predict the occurrence of D-AKI in hospitalized patients in a multi-drug environment. A single center retrospective study among adult hospitalized patients was conducted from July 2019 to September 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by our hospital. According to the propensity score matching algorithm, four controls per case were matched to eliminate the confounding bias caused by individual baseline variables. The predictors for D-AKI were obtained by logistic regression equation and used to establish the nomogram. Among 51,772 hospitalized patients, 332 were diagnosed with D-AKI. After matching, 288 pairs and 1,440 patients were included in the study, including 1,005 cases in the development group and 435 cases in the validation group. Six variables were independent predictors for D-AKI: alcohol abuse, the concurrent use of nonsteroidal anti-inflammatory drugs or diuretics, chronic kidney disease, lower baseline red blood cell count and neutrophil count ≥7 × 10/L. The area under the curve (AUC) of the prediction model in the development group and validation group were 0.787 (95%CI, 0.752-0.823) and 0.788 (95%CI, 0.736-0.840), respectively. The GiViTI calibration belts showed that the model had a good prediction accuracy for the occurrence of D-AKI ( > 0.05). This nomogram can help identify patients at high risk of D-AKI, which was useful in preventing the progression of D-AKI and treating it in the early stages.
药物性急性肾损伤(D-AKI)与死亡率增加和住院时间延长相关。本研究旨在建立一种列线图,以预测在多药环境下住院患者发生D-AKI的情况。基于我院开发的药物不良事件主动监测与评估系统-2,于2019年7月至2019年9月对成年住院患者进行了一项单中心回顾性研究。根据倾向评分匹配算法,每例病例匹配4个对照,以消除个体基线变量引起的混杂偏倚。通过逻辑回归方程获得D-AKI的预测因子,并用于建立列线图。在51772例住院患者中,332例被诊断为D-AKI。匹配后,288对共1440例患者纳入研究,其中开发组1005例,验证组435例。六个变量是D-AKI的独立预测因子:酗酒、同时使用非甾体抗炎药或利尿剂、慢性肾脏病、较低的基线红细胞计数以及中性粒细胞计数≥7×10⁹/L。开发组和验证组预测模型的曲线下面积(AUC)分别为0.787(95%CI,0.752-0.823)和0.788(95%CI,0.736-0.840)。GiViTI校准带显示该模型对D-AKI的发生具有良好的预测准确性(P>0.05)。这种列线图有助于识别D-AKI高危患者,这对于预防D-AKI的进展和早期治疗很有用。