(26) Development of a multivariable prediction model to assess potential drug-drug interactions in chronic kidney disease - A Retrospective Study

Background: CKD patients are highly vulnerable to potential drug-drug interactions (pDDIs), owing to multiple drug use. This can lead to ineffective therapeutic responses, significant morbidity, mortality, and serious adverse events.

Aim/

Objective: The objective of this study was to evaluate the number and types of potential drug-drug interactions (pDDIs) observed in the study population and to develop a pDDI prediction model based on various risk factors.

Methods:   A retrospective study was conducted at a tertiary care teaching hospital with 392 CKD patients. The relevant patient demographics and clinical details were collected and documented in case record forms. Using different drug interaction detecting databases, the acquired data were screened to identify pDDIs. Poisson regression was used to identify the independent risk factors associated with the number of pDDIs and develop a prediction model. Data entry and analysis were done using IBM SPSS software v20.0.

Results: A total of 2054 interacting drug pairs were found from the 392 patient files screened and male gender, comorbid conditions like ischemic heart disease, hypertension, diabetes mellitus, and congestive heart failure, a higher number of therapeutic subgroups and drugs per prescription, were identified as independent risk factors associated with an increase in the number of pDDIs. The presence of liver disease was the only factor that reduced the number of pDDIs.

Conclusion: Our study identified the significant risk factors for pDDIs in CKD patients and developed a prediction model. This can play a significant role in the early prediction of pDDIs using prior information about the patient characteristics and attributes of various administered drugs in CKD patients.

Keywords: Chronic kidney disease, potential drug-drug interactions, prediction model, risk factors