Potential Drug Interactions and Potentially Inappropriate Medications in Daily Radiooncology Practice : a Risk Assessment
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From the Department of Radiotherapy and Radiation Oncology of Marienhospital Herne – University Hospital – of the Ruhr-Universität Bochum Director: Prof. Dr. med. I. A. Adamietz Potential drug interactions and potentially inappropriate medications in daily radiooncology practice – a risk assessment Inaugural-Dissertation for the Attainment of a Doctor´s Degree in Medicine at the high Medical Faculty of the Ruhr-Universität Bochum Presented by Nina Sibylle Lamberty from Düsseldorf 2013 Dean of Faculty: Prof. Dr. med Klaus Überla Referee: Prof. Dr. med. Irenäus A. Adamietz Co-Referee: PD Dr. med Oliver Lindner Date of oral exam: 03.02.2015 Abstract Lamberty Nina Potential drug interactions and potentially inappropriate medications in daily radiooncology practice – a risk assess- ment Background: Radiation treatment of malignant disease includes patients with various extents of tumor and different comorbidities. Due to bad general condition approximately 20 % of irradiated subjects are in-patients. Beyond elimi- nation of tumor and treatment of its side effects drug interactions and potentially inappropriate medications constitute a major challenge in these radiooncologic patients. The layering of polypharmacy with age and radiation related physiological and functional changes and comorbidities might increase the risk for potential drug drug interactions (pDDs). This study attempted to quantify the frequency of pDDIs and potential inappropriate medications (PIMs) not related to i.v. chemotherapy in patients undergoing radiation treatment aiming the assessment of risk by medication. Methods: Medication profiles were analyzed by reviewing discharge letters of 120 cancer patients who had been admitted to the Marienhospital, Herne between November 2010 and November 2011. An improvement of the Karnofsky index during hospital treatment had been registered. The medication of all patients (n = 120) was screened for pDDIs at hospital admission and at discharge using the ABDA drug interaction software (Pharmatechnik®). Potential interactions, graded by their levels of severity were identified. The patient’s medication profile was evaluat- ed additionally in terms of potentially inappropriate medications due to an unacceptable risk-to-benefit ratio. Potential inappropriate medications in cancer patients were identified using the Priscus list (PIMs) and the Cave module (Indi- vidual inappropriate medications (IIMs). Logistic regression was applied to determine odds ratios for specific risk factors of pDDIs and potentially inappropriate medications i.e. age, gender and number of medications. Results: The evaluation of the admission and discharge medication revealed that there was a significant risk for being harmed by potential drug interactions although the Karnofsky index improved after radiooncologic treatment (Mean admission: 67.9; Mean discharge: 75.4). At hospital discharge significantly more pDDIs per patient (2.25) were detected than at hospital admission (1.59) (p = 0.001). Most potential drug interactions (46.7 %) involved non- anticancer agents such as antihypertensive drugs, corticosteroids, anticoagulants and NSAIDs. According to the Frechen-Score the most frequently involved drugs in therapeutically relevant pDDIs were cardiovascular drugs, insulin, corticosteroids and NSAIDs, whereas antipsychotics, antidepressants and antiemetics were rarely involved in potential drug interactions. In multivariate analysis, increased risk of receiving drug combinations in which there were potential drug interactions was associated with receipt of increasing numbers of drugs (p = 0.001). According to the Cave module 362 prescribed IIMs were inappropriate due to increased age (39 %) and underlying metabolic (25 %) or cardiovascular diseases (13.2 %). With increasing age (p = 0.003), number of comorbid diseases (p = 0.005) and the number of medications (p < 0.001) the proportion of patients receiving IIMs increased. The three most common IIMs were antihypertensives (18.3 %), NSAIDs (11.3 %), and corticosteroids (10.3 %). Of 79 patients aged > 65 46.8 % were taking at least one PIM, as defined by the german Priscus list at hospital discharge. Discussion: Although, there was an improvement of general performance status after hospital treatment the present study recorded a high prevalence of pDDIs and PIMs in the radiooncologic setting. Additional medication, including supportive therapies and concomitant medications should be weighed carefully for benefit versus risk of ADEs in the context of existing regimens prior to start of radiation. In the context of risk management recommendation guidelines for day-to-day routine were developed for radiooncologic patients. The risk by medications should be assessed peri- odically to reduce the overall risk potential. To my parents Table of contents 1 Introduction ............................................................................................................. 10 1.1 Background 10 1.2 Risk management and outline of the thesis 11 1.3 Literature review and introduction into the topic 12 1.3.1 Drug related problems 14 1.3.2 Drug drug interactions 17 1.3.3 Potential inappropriate medications (PIMs) 28 1.3.4 Medicine safety management 30 1.3.5 Documentation of drug interactions and inappropriate medications 32 1.3.6 Management of drug interactions and inappropriate medications 33 2 Aim of the thesis ..................................................................................................... 36 3 Material und methods .............................................................................................. 38 3.1 Material, records 38 3.2 Patients informed consent 38 3.3 Study population 38 3.4 Study design 39 3.5 Data ascertainment 39 3.5.1 Cave module 40 3.5.2 Steps of data extraction 40 3.5.3 Patient characteristics and medical conditions 42 3.5.4 Drug combinations examined 44 3.5.5 Chronic medications and supportive medications 45 3.5.6 Definition of the classification pattern used to estimate the risk potential for potential drug interactions 45 3.5.7 Evaluating potential drug interactions 47 3.5.8 Potential drug interactions classification and flagging 47 3.6 Potentially inappropriate medications 47 3.6.1 Cave module 48 3.6.2 Priscus list 48 3.7 Statistical analysis 48 4 Results ..................................................................................................................... 49 4.1 Patient characteristics 49 4.2 Comorbidities 51 4.2.1 Comorbidities in the study cohort 51 1 4.2.2 Comorbidity scores: Karnofsky index and Charlson comorbidity index 52 4.3 Prescribed medications 55 4.4 Analysis of potential drug interactions 60 4.4.1 Number of potential drug interactions 60 4.4.2 Classification of potential drug interactions 62 4.4.3 Correlation analysis 63 4.4.4 Medication classes involved frequently in potential drug interactions 66 4.4.5 Score predicting the interaction potential 73 4.4.6 Effects induced by potential drug interactions 75 4.5 Potentially inappropriate medications 79 4.5.1 Cave module: Frequency of IIMs and correlation analysis 79 4.5.2 Pricus list medications 85 5 Discussion ............................................................................................................... 87 5.1 Patient population and prevalence of potential drug interactions 87 5.2 Clinical relevance of drug combinations commonly involved in pDDIs in the radiooncologic setting 89 5.2.1 Major potential drug interactions (Category II) 90 5.2.2 Moderate potential drug interactions (Category III and Category IV) 91 5.2.3 Minor potential drug interactions (Category V) 96 5.2.4 Potential drug interactions with oral anticancer drugs 97 5.2.5 Score predicting the interaction potential (Gaertner et al., 2012) 97 5.3 Factors associated with the presence of pDDIs 100 5.4 Potentially inappropriate medications 102 5.4.1 Cave module and other classification systems (Beers criteria and STOPP criteria) 102 5.4.2 Priscus list 107 5.5 Critical appraisal of own methods 108 6 Key results, management strategies and conclusions ........................................... 111 6.1 Key results 111 6.2 Management strategies 112 6.2.1 Potential drug interactions 112 6.2.2 Potentially inappropriate medications 118 6.3 Conclusions and outlook 118 Bibliography ............................................................................................................. 120 Appendix .................................................................................................................. 137 A ABDA database 137 A 1.1 ABDA database 137 A 1.2 Evaluating drug interactions by the ABDA database 137 A 1.3 The ABDA database mode of action 138 2 A 2 Cave module 138 B Study data 140 B 1 Drug interactions 140 B 2 Potential inappropriate medications 145 B 3 Case report 149 B 3.1 Potential drug interactions with a CYP P450 inducer 149 B 3.2 Drug disease interactions – Example 1 151 B 3.3 Drug disease interaction – Example 2 152 C Potentially inappropriate medications 153 3 Abbreviations ABDA Bundesvereinigung Deutscher Apothekerverbände (Federal organization of the German pharmacist associations) ACE Angiotensin-converting enzyme ADE Adverse drug event ADR Adverse drug reaction ATC Anatomical therapeutical chemical BMI Body mass index CAD Coronary artery disease CI Confidence interval CNS Central nervous system CSEs HMG-CoA reductase inhibitors Corticost. Corticosteroids CPOE Computerized physician order entry CDSS Clinical decision support system CYP Cytochrome P450 isoenzyme DDI Drug-drug interaction