Clarity and Applicability of Drug-Drug Interaction Management Guidelines (original) (raw)
Related papers
Annals of Pharmacotherapy, 2007
Background: Pharmacists contribute to the detection and prevention of drug therapy–related problems, including drug–drug interactions. Little is known about compliance with pharmacy practice guidelines for the management of drug–drug interaction alerts. Objective: To measure the compliance of community pharmacists with Dutch guidelines for the management of drug–drug interactions and to determine patient- and prescriber-related determinants for noncompliance. Methods: Sixteen clinically relevant drug–drug interactions were included in the study based on certain described criteria. From June to August 2005, Dutch pharmacists (N = 149) collected alerts occurring in daily patient care for these interactions as well as information related to the patient, the alert itself, the prescriber, and the management of the alert. Noncompliance was measured by comparing the management executed by the pharmacy with the national guidelines. Results: Overall compliance with the guidelines was 69.3% (...
Evaluation of drug–drug interaction screening software combined with pharmacist intervention
International Journal of Clinical Pharmacy, 2012
Background Drug-drug interactions (DDI) in hospitalized patients are highly prevalent and an important source of adverse drug reactions. DI computerized screening system can prevent the occurrence of some of these events. Objective To evaluate the impact of drug-drug interaction (DDI) screening software combined with active intervention in preventing drug interactions. Setting The study was conducted at General Hospital of Vitória da Conquista (HGVC), Brazil. Method A quasi-experimental study was used to evaluate the impact of IM-Pharma, a locally developed drug-drug interaction screening system, coupled with pharmacist intervention on adverse drug events in the hospital setting. Main outcome measure The proportion of patients co-prescribed two interacting drugs were measured in two phases, prior the implementation of IM-Pharma and during the intervention period. DDI rates per 100 patient days were calculated before and after implementation. Risk ratios were estimated by Poisson regression models. Results A total of 6,834 instances of drug-drug interactions were identified; there was an average of 3.3 DDIs per patient in phase one and 2.5 in phase two, a reduction of 24 % (P = 0.03). There was a 71 % reduction in high-severity drug-drug interaction (P \ 0.01). The risk for all DDIs decreased 50 % after the implementation of IM-Pharma (P \ 0.01), and for those with high-severity, the reduction was 81 % (P \ 0.01). Conclusion The performance of IM-Pharma combined with pharmacist intervention was positive with an expressive reduction in the risk of DDIs.
Drug Safety, 2013
BACKGROUND: Offering a drug-drug interaction (DDI) checker on-demand instead of computertriggered alerts is a strategy to avoid alert fatigue. OBJECTIVE: The purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients. METHODS: The study was conducted at the University Hospital Zurich, an 850-bed teaching hospital. The hospital-wide use of the on-demand DDI checker was measured for prescribers and consulting pharmacologists. The number of DDIs identified on-demand was compared to the number that would have resulted by computer-triggering and this was compared to patient-specific recommendations by a consulting pharmacist. RESULTS: The on-demand use was analyzed during treatment of 64,259 inpatients with 1,316,884 prescriptions. The DDI checker was popular with nine consulting pharmacologists (648 checks/consultant). A total of 644 prescribing physicians used it infrequently (eight checks/prescriber). Among prescribers, internists used the tool most frequently and obtained higher numbers of DDIs per check (1.7) compared to surgeons (0.4). A total of 16,553 DDIs were identified on-demand, i.e., <10 % of the number the computer would have triggered (169,192). A pharmacist visiting 922 patients on a medical ward recommended 128 adjustments to prevent DDIs (0.14 recommendations/patient), and 76 % of them were applied by prescribers. In contrast, computer-triggering the DDI checker would have resulted in 45 times more alerts on this ward (6.3 alerts/patient). CONCLUSIONS: The on-demand DDI checker was popular with the consultants only. However, prescribers accepted 76 % of patient-specific recommendations by a pharmacist. The prescribers' limited on-demand use indicates the necessity for developing improved safety concepts, tailored to suit these consumers. Thus, different approaches have to satisfy different target groups.
Detection of potential drug interactions – a model for a national pharmacy register
European Journal of Clinical Pharmacology, 2006
Objective: The widespread use of pharmaceuticals prescribed by different physicians has caused the Swedish government to propose a new legislation with registration of all prescriptions dispensed at the Swedish pharmacies. In the present study, we wanted to examine the frequency, distribution and determinants of potential drug interactions. Methods: The prescriptions from all individuals (n=8,214) with two or more prescriptions during October 2003 to December 2004 were collected from the ongoing Jämtland cohort study of a total of about 11,000 individuals. Potential drug-drug interactions were detected with a computerized interaction detection system and classified according to clinical relevance (types A-D). Results: On average each individual filled 14.6 (men 14.3, women 14.8) prescriptions during the study period. 3.6% of the individuals used more than 15 different drugs. The number of detected potential drug interactions type A-D was 4,941 (men 1,949, women 2,992). The risk of receiving a potential interaction type A-D was estimated as the cumulative incidence 0.26 (2,116/8,214) overall, 0.22 (748/3,467) for men and 0.29 (1,368/4,747) for women during the 15-month study period. The age adjusted risk, RR adj , for women was estimated as 1.30. Excluding sex hormones and modulators of the genital system, the RR adj was 0.96, with no elevated risk for women. For potential interactions type D, that might have serious clinical consequences, 167 (cumulative incidence 0.0203) individuals (72 men, cumulative incidence 0.0208, 95 women cumulative incidence 0.0200) were detected. The risk of receiving a combination of potentially interacting drugs was positively correlated to age and polypharmacy. The cumulative incidence for elderly was estimated as 0.36 (65-84 years) and 0.39 (85 years and above). The relative risk for individuals with 15 drugs or more was estimated as 3.67 (95% CI 3.46-3.90). Conclusion: In a general population there were relatively few severe potential drug interactions. The new Swedish national pharmacy register will provide health care professionals with a powerful tool to systematically review all prescriptions. An alert system should focus on the more potential drug interactions, type C-D, with close monitoring of elderly and patients with polypharmacy.
THE PROFESSIONAL MEDICAL JOURNAL, 2017
There are several clinically significant outcomes of drug-drug interactions (DDIs) which have been classified as one of the serious forms of adverse drug reactions that may lead to prolongation of hospital stays along with severe cases of mortality and morbidities. It may cause due to the selection of two or more interacting drugs to be prescribed to patient. Objectives: Therefore it is indispensable to attain a collective level of therapeutic decision making so that any potential DDIs can be minimized that ultimately turn out to be safe and beneficial to patient. Study Design: The current study is based upon surveys to evaluate utilization of medications that have a narrow therapeutic range with high incidence to develop DDIs and to access the knowledge, attitude as well as practice of using such drug products in relation to these facts, though very few such studies have been identified, yet the relevant data is insufficient locally. The study design was selected to be qualitative and cross sectional. Period: January 2016 till August 2016 in Karachi, Pakistan. Settings: The questionnaire was well constructed for physicians, pharmacists as well as nurses who were selected as the participant of the study and a former consent from the respondents was obtained. Method: Coefficient of spearman correlation & Cronbach's α values were calculated in order to validate the questionnaire (α = 0.927 and p = 0.918). The information based on practice along with demographics of study participant was included as first segment of questionnaire while their knowledge regarding drug interactions was included as second part. Mean scores were calculated and responses were analysed by ANOVA in relation to the knowledge of members relating to drug interactions of vancomycin, warfarin and valproic acid. Results: Mean scores of perception were found in order of 1.590.16, 1.549.02 and 2.020.83 for physicians, pharmacists and nurses. No significant differences were observed between physicians and pharmacists cohorts in identifying the drug interactions whereas noteworthy variations were observed in the group of nurses (p < 0.05). Conclusion: Such investigations are vital in their prospect to highlight the importance for the design, implementation and monitoring of an effectual tool for the guidance of various healthcare members involved in identification and management of DDIs. Furthermore, results also signify the need of sophisticated support systems for valuable clinical judgments.
European Journal of Clinical Pharmacology, 2013
Purpose To investigate the impact of the integration of the drug-drug interaction database SFINX into primary health care records on the prevalence of potentially serious drugdrug interactions. Methods The study was a controlled before-and-after study on the prevalence of potential drug-drug interactions before and after the implementation of SFINX at 15 primary healthcare centres compared with 5 centres not receiving the intervention. Data on dispensed prescriptions from health care centres were retrieved from the Swedish prescribed drug register and analysed (post-intervention). All drugs dispensed during each 4 month period were regarded as potentially interacting. Results Use of SFINX was associated with a 17% decrease, to 1.81×10 −3 from 2.15×10 −3 interactions per prescribed drug-drug pair, in the prevalence of potentially serious drugdrug interactions (p00.042), whereas no significant effect was observed in the control group. The change in prevalence of potentially serious drug-drug interactions did not differ significantly between the two study groups. The majority of drug-drug interactions identified were related to chelate formation. Conclusion Prescriptions resulting in potentially serious drug-drug interactions were significantly reduced after integration of the drug-drug interaction database SFINX into electronic health records in primary care. Further studies are needed to demonstrate the effectiveness of drug-drug interaction warning systems.
Integrated Pharmacy Research and Practice, 2022
Background: Drug-drug interactions (DDIs) can cause treatment failure and serious adverse drug reactions, leading to morbidity and mortality. Due to their significant effects on the patient's health, community pharmacists (CPs) competence in detecting and preventing these interactions is essential to provide optimal health services. Thus, this study aimed to explore the performance of the CPs in situations involving the presence of potential DDIs. Methods: A cross-sectional, simulated patient study was conducted in 235 community pharmacies in the Khartoum locality. Two scenarios were used to evaluate the performance of the CPs. Ten final year B. Pharm. students were selected to act as simulated patients (SPs); they were trained for two weeks to familiarize their roles. All encounters were documented immediately after leaving the pharmacy by the SPs in the data collection form. Results: All planned SPs visits were completed, resulting in 470 visits. None of the CPs asked about the patients' medication history in both scenarios. After the SPs provided information about the drug used currently by the patient, 13.6% and 23.4% of the CPs had identified the potential DDIs in scenario 1 and scenario 2, respectively. In scenario 1, 59.4% distinguished the interaction of simvastatin with both drugs, while, in scenario 2, 74.5% recognized the interaction of warfarin with both drugs. In identifying DDIs, around half of the CPs were dependent on their knowledge or using drug interaction checker programs. The most common intervention made by the CPs was referring the patient to the prescriber (56.3% CPs in scenario 1 and 60% CPs in scenario 2). Conclusion: CPs practice in identifying and managing potential DDIs was poor. The current CPs practices need substantial improvement. Therefore, professional education and the use of software programs in community pharmacies should be encouraged.