Wang Xiao, Ke Hongqin, Zhu Jianyong, Zhao Lijun, Liu Yanhong, He Yan, Wu Wenwen, Yu Huibin
Department of Pharmacy, Renmin Hospital, Hubei University of Medicine, No. 39 Chaoyang Middle Road, Shiyan, Hubei, 442000, China.
School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan, Hubei, 442000, China.
BMC Pharmacol Toxicol. 2025 Jul 1;26(1):127. doi: 10.1186/s40360-025-00962-8.
The prevalence of meropenem-related thrombocytopenia has risen in tandem with the growing utilization of meropenem in clinical settings. Consequently, we aimed to develop a risk prediction model for meropenem-related thrombocytopenia in patients with pulmonary infection and to enhance the safety for the clinical administration of meropenem.
A retrospective case-control study was conducted to collect data. The training group consisted of patients who were treated with meropenem for pulmonary infection at a tertiary A hospital in Shiyan from January 2018 to December 2021. The external validation group was formed from patients at another tertiary A hospital from January 2019 to January 2020. Multivariable logistic regression analysis was employed to investigate the risk factors linked to meropenem-related thrombocytopenia. Subsequently, these factors were utilized to develop a nomogram for predicting meropenem-related thrombocytopenia. The Bootstrap method was conducted to internally validate the nomogram model. The discrimination, calibration, and clinical effectiveness of the model were assessed through the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
A total of 625 patients were included in the training group. Among them, 73 patients experienced meropenem-related thrombocytopenia. Multivariate logistic regression analysis revealed that hypertension, baseline platelet count, and combined with cephalosporins or penicillins were independent risk factors for meropenem-related thrombocytopenia in patients with pulmonary infection. A risk prediction model was developed based on these four factors. The model demonstrated an AUC (area under the ROC curve) of 0.774 (95%CI: 0.718 ~ 0.829), with a sensitivity of 0.685 and a specificity of 0.737. The optimal critical value was determined to be 0.137. Internal validation yielded an AUC of 0.761, while external validation resulted in an AUC of 0.750 (95%CI: 0.702 ~ 0.799). The calibration diagram indicated a high level of agreement between the predicting and actual probabilities. Furthermore, the DCA demonstrated that the model had significant clinical benefit and practical value.
A risk prediction model based on hypertension, baseline platelet count, combined with cephalosporins or penicillins could effectively predict the occurrence of meropenem-related thrombocytopenia in patients with pulmonary infection.
随着美罗培南在临床环境中的使用增加，美罗培南相关血小板减少症的患病率也随之上升。因此，我们旨在建立一种针对肺部感染患者美罗培南相关血小板减少症的风险预测模型，并提高美罗培南临床应用的安全性。
进行一项回顾性病例对照研究以收集数据。训练组由2018年1月至2021年12月在十堰某三甲医院接受美罗培南治疗肺部感染的患者组成。外部验证组由2019年1月至2020年1月在另一家三甲医院的患者组成。采用多变量逻辑回归分析来研究与美罗培南相关血小板减少症相关的危险因素。随后，利用这些因素建立一个预测美罗培南相关血小板减少症的列线图。采用Bootstrap方法对列线图模型进行内部验证。通过受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型的区分度、校准度和临床有效性。
训练组共纳入625例患者。其中，73例患者发生美罗培南相关血小板减少症。多变量逻辑回归分析显示，高血压、基线血小板计数以及联合使用头孢菌素或青霉素是肺部感染患者美罗培南相关血小板减少症的独立危险因素。基于这四个因素建立了一个风险预测模型。该模型的ROC曲线下面积（AUC）为0.774（95%CI：0.718～0.829），灵敏度为0.685，特异度为0.737。确定最佳临界值为0.137。内部验证得到的AUC为0.761，外部验证得到的AUC为0.750（95%CI：0.702～0.799）。校准图显示预测概率与实际概率之间具有高度一致性。此外，DCA表明该模型具有显著的临床益处和实用价值。
基于高血压、基线血小板计数、联合使用头孢菌素或青霉素的风险预测模型可以有效预测肺部感染患者美罗培南相关血小板减少症的发生。
