African Journal for Physical, Health Education, Recreation and Dance (AJPHERD) Supplement 1:1 (September), 2014, pp. 93-104.
Investigating potential drug-drug interactions associated with polypharmacy in the elderly at Dr George Mukhari Academic Hospital, Gauteng Province, South Africa A.S. ANNOR, N. SCHELLACK AND A.G.S. GOUS University of Limpopo, Medunsa Campus, Ga-Rankuwa, South Africa, Ga-Rankuwa, South Africa. E-mail:
[email protected] Abstract In elderly people, multiple chronic and degenerative disorders are highly prevalent. Clinicians tend to prescribe multiple medications which increase their exposure to drug-drug interactions. This was a retrospective cross-sectional study, which enrolled files of 500 patients aged 65 years and older, with five or more medications in their current prescriptions. The objectives of the study were to determine the prevalence, identify and classify potential drug-drug interactions (DDIs) in elderly patients and to determine the feasibility of using a computerised drug interactions checker. A total of 5300 potential DDIs were identified with an average of ten interactions per patient (range 0-44). Of the 500 patients, only 12 (2.4%) patients had no potential DDIs. The majority, 488 (97.6%) had potential DDIs. Potential DDIs of risk rating X, all major in severity, were identified nine times (0.17%; n=5300). In category D, 321(6.06%) interactions were recorded of which 55.45% (178) were of major severity. The majority of the interactions in this study were that for risk rating C, of which a total of 4077 (76.92%; n=5300) was recorded. Most of the drug interactions noted in this study sample was of a moderate severity necessitating the pharmacist/clinician to monitor the therapy as the interaction may result in exacerbation of the patient’s condition and/or require an alteration in therapy. Reliable, regularly updated clinical decision support systems and information technology such as a computerised drug interactions checker could be a vital accessory to assist the pharmacist to help identify, monitor and prevent dangerous drug combinations being given to patients. Keywords: Adverse drug reactions, drug-drug interactions, elderly, polypharmacy. How to cite this article: Annor, A.S., Schellack, N. & Gous, A.G.S. (2014). Investigating potential drug-drug interactions associated with polypharmacy in the elderly at Dr George Mukhari Academic Hospital, Gauteng Province, South Africa. African Journal for Physical, Health Education, Recreation and Dance, September (Supplement 1:1), 93-104.
Introduction Drug-drug interactions (DDIs) occur when one drug interferes pharmacodynamically or pharmacokinetically with another drug. DDIs are related to adverse drug reactions (ADRs) and hospitalisation. They may also lead to a sub-therapeutic effect of a co-administered drug. Polypharmacy is defined as using five or more drugs irrespective of their appropriateness of use (Masoodi, 2008). Polypharmacy is common in out-patients and has been identified as a major risk factor for drug-drug interactions (Lin, Wang & Bai, 2011). Drug
94 Annor, Schellack and Gous interactions are significant contributors to morbidity. In the United States and Europe, doctor’s office visits for an adverse drug event increased from 9% of the population per year at age 25-44 years to as high as 56.8% between the ages of 65 and 74 years. The risk for an adverse drug event is 13% with the use of two medications, but the risk increases to 58% for five medications. If seven or more medications are used, the incidence of adverse drug events increases to 82% (Masoodi, 2008). Various studies have reported potential DDIs in medical prescriptions. Lin et al. (2011) showed that approximately one-quarter (25%) of 81,650 out-patients who visited a medical centre in Taiwan over a period of three months in 2004 had potential drug-drug interactions. In a study on the geriatric profile in Universitas Hospital, South Africa, 21% of the total admissions were over 65 years and used an average of six medications (Van Staden & Weich, 2007). Many DDIs are avoidable, but those that are not, require awareness of the interaction to allow for proper management and appropriate dosage adjustment (Lin, 2003). The elderly (i.e. those aged 65 or older) are at increased risk of adverse drug reactions (ADRs) due to a combination of factors such as physiological decline (e.g. reduced renal and hepatic clearance), co-morbidity resulting in the possibility of drug disease interactions and in the case of polypharmacy, DDIs. Others are problems with adherence due to, frailty, reduced dexterity and memory problems (Cresswell, Fernando, McKinstry & Sheikh, 2007). The main objective of the study was to identify potential DDIs in elderly outpatients, receiving five or more medications at Dr George Mukhari Academic Hospital. Methodology Study design The study was a retrospective cross-sectional cohort study in which files were reviewed at the Pharmacy Department of Dr George Mukhari Academic Hospital (DGMAH) in the Gauteng Province of South Africa. The hospital services approximately 1 700 000 people from surrounding sub-districts as well as referrals from other provinces and countries (Dr George Mukhari Academic Hospital, 2013). Data collection procedures The most recent prescriptions of 500 out-patients, 65 years and older, with five or more medications in September 2012 to March 2013, were assessed. All medication per patient were entered and analysed using the Lexi-Interact software® on a Personal Digital Assistant (PDA). The software categorised the
Investigating potential drug-drug interactions associated with polypharmacy 95 potential DDIs according to their risk, severity and reliability ratings to determine their clinical significance. The risk rating reflects both the level of urgency and the nature of actions necessary to respond to an action. The severity rating indicates the reported or possible magnitude of an interaction outcome and the reliability rating determines the amount of supporting documentation of the DDI. Data analysis A Microsoft Excel™ spreadsheet was used to analyse data categorically. The statistical analysis was descriptive in nature. The prevalence of the drug-drug interaction was summarised by percentage calculations. All statistical procedures were performed on SAS Release 9.2 running under Microsoft Windows for a personal computer. Reliability, validity and bias File numbers were recorded to serve as reference and proof of data collected. It was ensured that the Lexi-Interact software was updated on-line before imputing data to be analysed. The programme gave concise details on both predicted and documented interactions and had references. Results obtained were compared as comprehensively as possible to literature so that the results could be placed in a realistic context. Ethical considerations Data collection was conducted after approval had been received from the Medunsa Research Ethics Committee (MREC) (approval number MREC/H/160/2012: PG) as well as approved consent from the Management of DGMAH. The research was retrospective and only involved the recording of prescriptions and DDIs of prescriptions received at the Outpatient Department of the Pharmacy at DGMAH. Therefore no consent from patients was required. Results This study sample comprised 500 patients. The majority of the participants were women 357 (71.4 %). The ages of the patients ranged from 65 to 91 with a mean of 71.6 (±5.67). There were a total of 1045 diagnoses, most of them being chronic degenerative diseases. The most common diagnoses according to ICD10 (International Classification of Diseases version 10) classification in this study were those of the circulatory system 468 (44.8%), with hypertension being the most prevalent 430 (41%). Endocrine, nutritional and metabolic disorders were diagnosed in 291 (27.9%) patients. The diagnosis made most often in this group was type 2 diabetes mellitus (246; 23.5%). Diseases of the
96 Annor, Schellack and Gous musculoskeletal system and connective tissue were diagnosed in 139 (13%) patients with the most occurring being rheumatoid arthritis (74; 7.1%) (Tables 1 and 2). The first ten co-morbid conditions listed in decreasing order of frequency are shown in Table 2. Table 1: ICD-10 Organ system affected ICD-10 CODE
ORGAN SYSTEMS AFFECTED
Frequency
Percentage
I00-I99 E00-E89 M00-M99 J00-J99 R00-R99
Diseases of the circulatory system Endocrine, nutritional and metabolic disorders Diseases of the musculoskeletal system and connective tissue Diseases of the respiratory system Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified Diseases of the genitourinary system Diseases of the nervous system Diseases of the digestive system Neoplasms Diseases of the skin and subcutaneous tissue Factors influencing health status and contact with health services Diseases of the eye and adnexa Mental, behavioural and neurodevelopmental disorders Certain infectious and parasitic diseases Diseases of the blood and blood-forming organs and certain disorders Diseases of the ear and mastoid process Total
468 291 139 48 25
44.8% 27.9% 13% 4.6% 2.4%
18 15 14 7 5 5
1.8% 1.4% 1.3% 0.7% 0.5% 0.5%
3 3 2 1
0.3% 0.3% 0.2% 0.1%
1 1045
0.1% 99.9%
N00-N99 G00-G99 K00-K95 C00-D49 L00-L99 Z00-Z99 H00-H59 F01-F99 A00-B99 D50-D89 H60-H95
Table 2: Ten most frequent diagnoses made ICD-10 code
Diagnosis
Number
Percentage
I10 E11 M06.9 M19.9 J44.9 J45.9 M00.9 E05 R05 E78.5
Hypertension Type 2 diabetes mellitus Rheumatoid arthritis Osteoarthritis Chronic obstructive airways disease Asthma Arthritis Hyperthyroidism Cough Dyslipidemia
430 246 74 22 20 18 16 16 12 9
41% 23.5% 7.1% 2% 1.9% 1.7% 1.5% 1.5% 1.2% 0.9%
There were 3711 drugs prescribed in total, classifiable into 113 groups. A range of 5-13 drugs was prescribed per patient, the mean being 7.42(±1.86). The drugs were grouped according to the ATC (Anatomical Therapeutic Chemical) system of classification and summarised in Table 3. The ten most frequently prescribed medicines were perindopril (325; 8.8%), nifedipine (276; 7.4%), aspirin (240; 6.5%), indapamide (218; 5.9%), metformin (195; 5.3%), paracetamol (184; 5%), atorvastatin (135; 3.6%), simvastatin (130; 3.5%), furosemide (128; 3.5%) and omeprazole (121; 3.3%).
Investigating potential drug-drug interactions associated with polypharmacy 97 Table 3: ATC Classification Grouping of Medicines Groups Names
Group C Group A Group B Group N Group M Group R Group L Group H Group J Group P Group G Total
Cardiovascular system Alimentary tract and metabolism Blood and blood forming organs Nervous system Musculo-skeletal system Respiratory system Antineoplastic and immunomodulating agents Systemic hormonal preparations, excl. sex hormones and insulins Antiinfectives for systemic use Antiparasitic products, insecticides and repellents Genito urinary system and sex hormones
No of drugs
Percentage
1608 981 335 320 207 65 61 51
43.3 26.4 9 8.6 5.6 1.8 1.6 1.4
41 35 7 3711
1.1 0.9 0.2 99.9
A total of 5300 DDIs were identified, with an average of ten interactions per patient (range 0-44). Of the 500 patients, only 12 (2.4%) patients had no potential DDIs; 488 (97.6%) patients had at least one potential DDI. An explanation of the interaction categories used in the study is indicated in Table 4. Table 4: Drug –Drug Interaction Rating Scale (Lexi-Comp®, 2012) Risk
Severity
Explanation
Explanation
X
Contraindicated
The drugs are contraindicated for concurrent use
D
Major
C
Moderate
Avoid combination Consider therapy modification Monitor therapy
B
Minor
No action needed
A
Unknown
No known interaction
The interaction may be life-threatening and/or require medical intervention to minimise or prevent serious adverse events The interaction may result in exacerbation of the patient’s condition and/or require an alteration in therapy The interaction would have limited clinical effects. May include an increase in the frequency or severity of side-effects, but generally it would not require a major alteration in therapy Unknown
Out of the 5300 potential DDIs identified, nine (0.17%) were of risk rating X (Figure 1). All were major in severity (Figure 2), with good (22.2%; 2) and fair (77.8%; 7) documentation (Figure 3). A summary of the interacting drug pairs is indicated in Table 5.
98 Annor, Schellack and Gous Table 5: Interactions of Risk Rating X (Avoid Combination) Types of drug-drug interactions Carbamazepine/ tramadol Alpha-1 blockers/ alpha-1 blockers Highest risk QTc-prolonging agents/moderate QTc-prolonging agents
Number (n=5300) 5 2 2
In the category of DDIs with risk rating D, 321(6.06 %) interactions were recorded (Figure 1). Of these, 178 (55.45%) were of major severity and excellent to fair documentation (Figures 2 and 3). A summary of the ten most frequently interacting drug pairs is indicated in Table 6. Table 6: Interactions of Risk Rating D (Modify regimen) Types of drug-drug interactions
Number (n=5300)
ACE inhibitors/ antacids Methotrexate/ NSAIDS Salicylates/ NSAIDS (non selective) Calcium channel blockers (dihydropyridine)/ carbamazepine Loop diuretics/ NSAIDS Serotonin modulators/ serotonin modulators Chloroquine (antimalarial)/ antacids Allopurinol/ ACE inhibitors Simvastatin/ amlodipine Potassium sparing diuretics/potassium salts
72 58 38 21 21 9 7 7 6 5
Figure 1: Risk ratings of potential drug-drug interactions.
Investigating potential drug-drug interactions associated with polypharmacy 99
Figure 2: Severity ratings of potential drug-drug interactions.
Figure 3: Reliability ratings of potential drug-drug interactions.
The majority of the interactions in this study were that for risk rating C. A total of 4077 (76.92%) were recorded for this category. Most of them were of moderate severity 3775 (92.57%) with good documentation 2108 (51.69%) (Figures 1- 3). A summary of the ten most frequently interacting drug pairs is illustrated in Table 7.
100 Annor, Schellack and Gous Table 7: Interactions of Risk Rating C (Monitor Therapy) (Lexi-Comp®, 2012-2013) Potential drug-drug interactions Hypotensive agents/ hypotensive agents
Number N=5300 775(14.6%)
Antihypertensives/ antihypertensives
774(14%)
Antidiabetic agents/ thiazide diuretics ACE inhibitors/ thiazide diuretics
342(6.5%)
ACE inhibitors/ salicylates
192(3.6%)
Hypoglycemic agents/ salicylates
171(3.2%)
Thiazide diuretics/ NSAIDS
107(2%)
ACE inhibitors/ loop diuretics
94(1.7%)
NSAIDS/ ACE inhibitors
92(1.7%)
Hypoglycemic hypoglycemic agents
agents/
233(4.4%)
75(1.4%)
Description of interaction Hypotensive agents may enhance the adverse/toxic effect of other hypotensive agents Antihypertensives may enhance the hypotensive effects of other antihypertensives Thiazide diuretics may diminish the therapeutic effect of antidiabetic agents Thiazide diuretics may enhance the hypotensive effect of ACE inhibitors. Specifically, postural hypotension at initiation of ACE-inhibitor therapy. Thiazide diuretics may also enhance the nephrotoxic effects of ACE-inhibitors. Salicylates may diminish the antihypertensive and pharmacodynamic effects desired for congestive cardiac failure. Low doses of aspirin (100 mg) appear to cause no problems Salicylates may enhance the hypoglycaemic effect of hypoglycaemic agents. This is dosedependent (3 g or more of salicylates per day) NSAIDs may diminish the therapeutic effect of thiazide diuretics Loop diuretics may enhance the hypotensive effect of ACE-inhibitors. Specifically, postural hypotension at initiation of ACEinhibitor therapy. Loop diuretics may also enhance the nephrotoxic effects of ACEinhibitors ACE-inhibitors may enhance the adverse/toxic effects of NSAIDs, specifically a decrease in renal function. NSAIDS may also diminish the antihypertensive effect of ACE-inhibitors Hypoglycaemic agents may enhance the adverse/toxic effects of other hypoglycaemic agents
Discussion The study enrolled 500 geriatric patients, 65 years and older, majority of whom were female. Globally and in South Africa the majority of older persons, especially those seeking medical care, are women (Powell, 2011; Bacic-Vrca et al., 2010; Van Staden & Weich, 2007).
Investigating potential drug-drug interactions associated with polypharmacy 101 The ten co-morbid conditions that were most frequently diagnosed were hypertension, type 2 diabetes mellitus, rheumatoid arthritis, osteoarthritis, chronic obstructive airways disease, asthma, arthritis, hyperthyroidism, cough and dyslipidemia. These are also the most prevailing disease conditions in South Africa (Van Staden et al., 2007). Contrary to this, a study in Switzerland ranked endocrine, nutritional and metabolic disorders as 9th (3.4%) and diseases of the musculoskeletal system and connective tissue as 6th (4.8%) (Egger, Drewe & Schleinger, 2003). The incidence of diseases varies based on the geographical location of that population. The average number of medicines prescribed per patient in this study was 7.42(± 1.86) and can be classified as polypharmacy. Polypharmacy increases the likelihood of patients being exposed to medicines that have recognised DDIs or drug-disease interactions and adverse drug reactions. Medicines for diseases of the cardiovascular system were the most prescribed of which the most potential DDIs were recorded. The high prevalence of potential DDIs can be attributed to the age group of the study population. Due to the physiologic decline of one or more organ systems, they are pre-disposed to co-morbid, chronic conditions. As a result, they have to be managed with multiple medications. The greater the number of medications, the higher the number of DDIs the patient might be exposed to. In a related study in Taiwan, which involved all age groups, the prevalence of potential DDIs was only 25.6% (Lin et al., 2011). However the prevalence of potential DDIs was 90% in another study in Croatia which was also limited to elderly people (BacicVrca et al, 2010). This correlates well with the findings of this study. Tramadol and carbamazepine concurrent use and agents that effect QTcprolongations e.g. chloroquine, citalopram and haloperidol were examples of potential DDIs of risk rating X. According to the field information for LexiInteract®, data on DDIs of risk rating X demonstrate that the specified agents may interact with each other in a clinically significant manner. The risks outweigh the benefits of concomitant use and are therefore contraindicated (Lexi-Comp®, 2012; Sanoski, Schoen & Bauman, 2008). For DDIs of risk rating D, patient-specific assessment is required to determine whether the benefits of concomitant therapy outweigh the risks. Specific actions were to be taken to optimise therapy whilst minimising toxicity. Actions to be taken include aggressive monitoring, empiric dosage changes or choosing alternative agents (Lexi-Comp®, 2012). Examples included concomitant prescription of perindopril or enalapril for hypertension, prednisolone, chloroquine, a nonsteroidal anti-inflammatory drug for arthritis, and an antacid for prophylaxis against peptic ulcer disease. Co-administration of antacids may have the potential to cause therapeutic failure due to reduced oral bioavailability
102 Annor, Schellack and Gous of these drugs. It must be given two to four hours before or after the above mentioned drugs (Srinivas, 2009). The potential DDIs with risk rating C constituted majority of the potential DDIs recorded. However, the benefits of concomitant use of these two medicines usually outweigh the risks (Lexi-Comp®, 2012). An example is the deliberate coprescription of antihypertensive medicines and diuretics in order to manage hypertension which is not possible with the use of either medicine alone. Literature states that 75% of patients will require combination therapy to achieve contemporary blood pressure targets (Baxter, 2008; Gradman, Basile, Carter & Bakris, 2010). It is however essential for the pharmacist, to be aware of these interactions and the effects they produce. This will assist in monitoring for efficacy and toxicity using clinical signs and laboratory information. Dosage and dosing adjustments can be made when necessary to achieve optimum therapeutic outcomes. In a few cases however, a change of one of the medicines will be the best option Lexi-Comp®, 2013). In a study conducted in a primary health care setting in South Africa, the prevalence of prescriptions containing at least one contraindicated combination of medications was 0.5%, severe potential interaction were 5.25% and moderate potential interactions constituted 42% of the prescriptions. Advanced age and polypharmacy were identified to be associated with potential drug-drug interactions (Kapp, Klop & Jenkins, 2013). This correlated well with the findings of the current study. The results of this study clearly indicate the need to monitor for potential DDIs in elderly patients, who have co-morbid conditions, and are on multiple medications. This will help in the identification of potentially clinically significant interactions in order to avoid adverse drug events. It should involve the active participation of the clinical pharmacist in patient care. Pharmacotherapeutic expertise of the clinical pharmacist is essential in knowing which patients are at risk, based on their disease profile and the medicines which have been prescribed. These potential DDIs can be prevented or monitored for, if a clinical pharmacist is able to identify them on time, using a quick, reliable and regularly updated computerised drug-drug interactions checker. Limitations Files were evaluated retrospectively, and the possibility exists that interactions from over-the-counter drugs and herbal medications not written on the prescription could have been missed. A follow-up prospective study could be conducted to identify interactions that include these medicines.
Investigating potential drug-drug interactions associated with polypharmacy 103 A limitation of the DDI software is that it does not include dosages and there is a possibility that the occurrence and severity of certain interactions will be dependent on drug dosage. Also, it only checks for interactions between a pair of medicines. Therefore, there is a possibility that a potential adverse event may not have been identified because it may have been caused by the presence of a third drug on the patient’s prescription. Conclusion Reliable, regularly updated clinical decision support systems and information technology are necessary in the pharmacy to help identify, monitor and prevent dangerous drug combinations being given to patients. The pharmacist should not take an action solely on a DDI alert but also take into consideration, the full clinical and drug profile of the patient.The accuracy, ease of use and comprehensiveness of the Lexi-Interact software® as well as its accessibility on a desktop computer or mobile device/PDA, makes it a reasonable resource guide for the pharmacist. Acknowledgements The authors would like to acknowledge Prof Herman Schoeman for the statistical analysis, Nikki Williamson for editorial assistance, the Department of Pharmacy, Medunsa Campus and the Department of Pharmacy, Dr DGMAH for financial and logistical support. References Bacic-Vrca, V., Marusic, S., Erdeljic, V., Falamic, S., Gojo-Tomic, N. & Rahelic, D. (2010). The incidence of potential drug-drug interactions in elderly patients with arterial hypertension. Pharmacy World and Science, 32, 815-821. Baxter, K. (Ed.) (2008). Stockley’s Drug Interactions (8th ed.). London, United Kingdom: Pharmaceutical Press. Cresswell, K.M., Fernando, B., McKinstry, B. & Sheikh, A. (2007). Adverse drug events in the elderly. British Medical Bulletin, 83, 259-274. Dr George Mukhari Academic Hospital (2013). Extracts from the Operational Plan of Dr George Mukhari Academic Hospital Operational Plan for 2013/2014. Ga-rankuwa, South Africa. Dr George Mukhari Academic Hospital. Egger, S.E., Drewe, J. & Schleinger, R.G. (2003). Potential drug-drug interactions in the drug of medical patients at hospital discharge. European Journal of Clinical Pharmacology, 58(11), 773-778. Gradman, A.H., Basile, J.N., Carter, B.L. & Bakris, G.L. (2010). Combination therapy in hypertension. Journal of the American Society of Hypertension, 4(1), 42-50.
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