Drug related problems in admitted geriatric patients: The impact of clinical pharmacist interventions

Abstract

Background: Geriatric patients are at high risk of Drug Related Problems (DRPs) due to multi- morbidity associated polypharmacy, age related physiologic changes, pharmacokinetic and pharmacodynamics alterations. These patients are often excluded from premarketing trials that can further increase the occurrence of DRPs. This study was aimed to identify DRPs and determinants in geriatric patients admitted to medical and surgical wards, and to evaluate the impact of clinical pharmacist interventions for treatment optimization. Methods: A prospective observational study was conducted among geriatric patients admitted to medical and surgical wards of Jimma University Medical Center from April to July 2017. Clinical pharmacists reviewed patients drug therapy, identified drug related problems and provided interventions. Data were analyzed by using SPSS statistical software version 20.0. Descriptive statistics were performed to determine the proportion of drug related problems. Logistic regression analyses were performed to identify the determinants of drug related problems. Results: A total of 200 geriatric patients were included in the study. The mean age of the participants was 67.3 years (SD7.3). About 82% of the patients had at least one drug related problems. A total of 380 drug related problems were identified and 670 interventions were provided. For the clinical pharmacist interventions, the prescriber acceptance rate was 91.7%. Significant determinants for drug related problems were polypharmacy (adjusted odds ratio [AOR]=4.350, 95% C.I: 1.212-9.260, p = 0.020) and number of comorbidities (AOR=1.588, 95% C.I: 1.029-2.450, p = 0.037). Conclusions: Drug related problems were substantially high among geriatric inpatients. Geriatric patients with polypharmacy and comorbidities need special attention to prevent drug related problems. Involving clinical pharmacist in the clinical team resulted in the improved acceptance rate of treatment optimization.