ArticleFrontiers in pharmacology2024
The clinical features and risk factors of coagulopathy associated with cefoperazone/sulbactam: a nomogram prediction model.
Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Extreme and rapidly reversible prolongation of prothrombin time during cefoperazone/sulbactam therapy in a patient with gastrointestinal dysfunction and parenteral nutrition: a case report.Frontiers in pharmacology · 2026Article
- Model-guided dose optimization of cefoperazone-sulbactam and levofloxacin for pneumonia and urinary tract infections in patients with hepatic dysfunction.American journal of translational research · 2026Article
- Interpretable Machine Learning for Predicting Cefoperazone-Sulbactam-Associated Coagulation Abnormalities in Elderly Inpatients: A Dual-Center Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
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9 authors.
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Abstract
Background: Cefoperazone/sulbactam (CPZ/SAM) is an important treatment option for infections caused by multidrug-resistant gram-negative bacteria. However, it is associated with an increased risk of coagulation disorders (CD) and causes severe bleeding in some instances. Early identification of risk factors and prediction of CD related to CPZ/SAM are crucial for prevention and treatment. This study aimed to explore the risk factors and developed a nomogram model for predicting the risk of coagulopathy in patients undergoing CPZ/SAM treatment. Methods: A total of 1719 patients who underwent CPZ/SAM in the Affiliated Hospital of Southwest Medical University from August 2018 to August 2022, were recruited as the training cohort. For validation, 1,059 patients treated with CPZ/SAM from September 2022 to August 2024 were enrolled. Patients were divided into the CD and the N-CD groups. The occurrence of CD was designated as the dependent variable. The univariate and multivariate logistic regression analysis was performed to identify the risk factors of CD. A nomogram model was constructed from the multivariate logistic regression analysis and internally validated for model discrimination and calibration. The performance of the nomogram was estimated using the concordance index (C-index) and calibration curve. Results: The multivariate logistic regression analysis resulted in the following independent risk factors for CD: baseline INR level (OR: 5.768, 95% CI: 0.484∼11.372, Conclusion: The nomogram model facilitated accurate prediction of CD in patients undergoing CPZ/SAM. And this could potentially contribute to reducing the incidence of CPZ/SAM-associated CD and consequently improving patients' outcomes.
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