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Development and validation of a risk prediction model for meropenem-related thrombocytopenia in patients with
Xiao Wang1,2, Hongqin Ke3, Jianyong Zhu4
1Department of Pharmacy, Renmin Hospital, Hubei University of Medicine, No. 39 Chaoyang Middle Road, Shiyan, Hubei, 442000, China.
Objective:
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.
Methods:
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).
Results:
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.
Conclusion:
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.
Insights
This study developed a risk prediction model for meropenem-related thrombocytopenia in pulmonary infection patients. The model, incorporating hypertension, baseline platelet count, and combined antibiotic use, effectively predicts this adverse event.
Area of Science:
- Pharmacology
- Clinical Medicine
- Medical Informatics
Background:
- Meropenem utilization is increasing, leading to a rise in meropenem-related thrombocytopenia.
- Accurate risk assessment is crucial for safe meropenem administration in patients with pulmonary infections.
Purpose of the Study:
- To develop and validate a predictive model for meropenem-related thrombocytopenia in pulmonary infection patients.
- To identify key risk factors associated with meropenem-induced thrombocytopenia.
- To improve the clinical safety of meropenem therapy.
Main Methods:
- Retrospective case-control study involving two tertiary hospitals.
- Multivariable logistic regression to identify risk factors.
- Nomogram construction for risk prediction, validated internally and externally.
- Assessment of model performance using ROC curves, calibration curves, and DCA.
Main Results:
- The final model included hypertension, baseline platelet count, and concurrent use of cephalosporins or penicillins.
- The model achieved an AUC of 0.774 in the training set and 0.750 in the external validation set.
- The model demonstrated good calibration and significant clinical utility.
Conclusions:
- A nomogram incorporating hypertension, baseline platelet count, and combined antibiotic therapy can effectively predict meropenem-related thrombocytopenia.
- This tool aids in the safer clinical application of meropenem for pulmonary infections.
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