Development and internal validation of a model for predicting cefoperazone/sulbactam-associated coagulation disorders in Chinese inpatients.

Abstract

Background and aim: The use of cefoperazone/sulbactam (CPZ/SAM) could commonly cause vitamin K-dependent coagulation disorders and even hemorrhage sometimes. However, there is a lack of prediction tools estimating the risk for this. This study aimed at developing and internally validating a model for predicting CPZ/SAM-associated coagulation disorders in Chinese inpatients.

Methods: A case-control study was conducted in 11,092 adult inpatients admitted to a Chinese general hospital between 2020 and 2021 and treated with CPZ/SAM. Patients with CPZ/SAM-associated coagulation disorders were identified through the Adverse Drug Events Active Surveillance and Assessment System-II and subsequent manual evaluation. Controls were selected from eligible patients who didn't develop coagulation disorders after CPZ/SAM therapy, with a 1:1 propensity score matching. The final predictors were obtained by univariable and multivariable logistic regression analyses. Internal validation and calibration for the model were performed using 1000 bootstrap resamplings.

Results: 258 patients were identified as CPZ/SAM-associated coagulation disorders in 2184 patients eligible for inclusions and exclusions and the incidence was 11.8%. A final population of 252 cases and 252 controls was included for model development and validation. Malnutrition (OR = 2.41 (1.56-3.77)), history of recent bleeding (OR = 1.95 (1.32-2.90)), treatment duration (OR = 1.10 (1.07-1.14)), combination with carbapenems (OR = 4.43 (1.85-11.88)), and serum creatinine (OR = 1.01 (1.00-1.01)) were identified as final predictors. The model showed good discrimination, calibration, and clinical practicality, with the validated area under the receiver operating characteristic curve being 0.723 (0.683-0.770).

Conclusions: The model with good performance quantifies the risk for CPZ/SAM-associated coagulation disorders, and may support individual assessment and interventions to mitigate the risk after external validation.