|Year : 2019 | Volume
| Issue : 1 | Page : 20-24
Assessment of potential drug–Drug interactions in hospitalized cardiac patients of a secondary care hospital in the United Arab Emirates
Muhammad Zeeshan Khan1, Sathvik Belagodu Sridhar1, Pradeep Kumar Gupta2
1 Department of Clinical Pharmacy and Pharmacology, RAK College of Pharmaceutical Sciences, RAK Medical and Health Sciences University, Ras Al-Khaimah, UAE
2 Ibrahim Bin Hamad Obaidallah Hospital, Ras Al-Khaimah, UAE
|Date of Web Publication||28-Feb-2019|
Dr. Sathvik Belagodu Sridhar
Department of Clinical Pharmacy and Pharmacology, RAK College of Pharmaceutical Sciences, RAK Medical and Health Sciences University, Ras Al-Khaimah
Source of Support: None, Conflict of Interest: None
Objective: To identify the types, severity, and documentation grades of potential drug–drug interactions (pDDIs) and to identify the predictors of pDDIs among hospitalized cardiac patients. Methods: This was a cross-sectional study. All the patients who were admitted for >24 h in a cardiology ward of a general hospital of the United Arab Emirates and prescribed with cardiac medications were included. The occurrence of any pDDI between cardiac medications and other coprescribed medications was identified using Micromedex database 2.0® and graded and documented based on the severity and documentation. Findings: A total of 842 pDDIs were identified in 155 patients. The overall relevant frequency for the occurrence of pDDIs was found to be 87.74%. A total of 79 pairs of pDDIs were identified. Among identified pDDIs, 41.33% and 56.65% were major and moderate severity type, respectively, whereas 12.32% were excellent and 36.81% were good documentation grade. The majority of pDDIs were between aspirin-bisoprolol (11.64%). Patients taking more than seven drugs (odds ratio [OR] = 9.90; 95% confidence interval [CI]: 2.28–42.99), polypharmacy (OR = 3.86; 95% CI: 0.93–16.08), and number of medical conditions (OR 0.25; 95% CI: 0.09–0.68) were significant predictors of pDDIs. Conclusion: The study fosters the importance of regular and close monitoring for pDDIs among cardiac patients. Thus, multicenter interventional studies are required to determine the exact nature and types of pDDIs in the local population.
Keywords: Adverse drug reactions, cardiology, hospitalized patients, potential drug–drug interactions
|How to cite this article:|
Khan MZ, Sridhar SB, Gupta PK. Assessment of potential drug–Drug interactions in hospitalized cardiac patients of a secondary care hospital in the United Arab Emirates. J Res Pharm Pract 2019;8:20-4
|How to cite this URL:|
Khan MZ, Sridhar SB, Gupta PK. Assessment of potential drug–Drug interactions in hospitalized cardiac patients of a secondary care hospital in the United Arab Emirates. J Res Pharm Pract [serial online] 2019 [cited 2020 Oct 28];8:20-4. Available from: https://www.jrpp.net/text.asp?2019/8/1/20/253213
| Introduction|| |
Drug–drug interactions (DDIs) appear to be the most frequently encountered challenge that may alter overall therapeutic response and may result in increased hospital stay and health care cost. Studies suggest that cardiovascular disease (CVD) patients more repeatedly encounter DDIs than patients with other disease states, probably due to associated risk factors such as age, polypharmacy, and pharmacokinetic and pharmacodynamic profile of the drugs.
The literature states that up to 1% of hospitals' admissions and 16% of admissions due to adverse drug reactions (ADRs) are owing to DDIs. The reported prevalence rate of potential DDIs (pDDIs) in CVD patients is found to be 65%–99.2%.,,,, Studies have reported a higher prevalence of DDIs in CVD patients than other disease group population.
Not much data are available in the Gulf Cooperation Council countries regarding the incidence or prevalence of pDDIs among cardiac patients. There are few published data regarding the incidence or prevalence and pattern of DDIs among United Arab Emirates (UAE) CVD patients. Hence, this study aims to find out the frequency, types, severity, documentation grades, and predictors of pDDIs among hospitalized cardiac patients.
| Methods|| |
This was a cross-sectional clinical study conducted at a cardiac inpatient setting of a secondary care ministry hospital from January 2017 to May 2017. One hundred fifty-five patients were included using a convenience sampling technique. Patients of >18 years of age of both genders, diagnosed with any CVD, and admitted under direct cardiac care for a minimum of 1 day and prescribed with a minimum of two medications were included. However, patients referred to the cardiology department for the assessment and visited as outpatients were excluded.
The cases were identified by the principal investigator by attending clinical ward rounds at the study site. All the required information such as demographic parameters, number of drugs prescribed, length of hospital stay, primary diagnosis, and the number of comorbidities and laboratory investigations was collected from the medical records and entered into a data collection form.
The prescriptions of in-patients were reviewed for the presence of pDDIs when two or more drugs were prescribed by entering the prescribed drugs in the Micromedex® 2.0 database. The drug interactions tool (DRUG-REAX®) of this database helps to check for interacting drugs, their effects, and clinical significance (Truven Health Analytics Inc., Michigan, USA). The identified pDDIs were classified according to the severity and documentation grade system of the database. Drug interaction probability scale (DIPS) was used to assess the probability of pDDIs.
All ADRs noted by treating cardiologist/s, reported by patients, and occurred during the hospital stay as a consequence of pDDIs were documented. A reported ADR was included and documented only when the suspected drug was involved in the pDDIs and the ADR corresponds to the description of the interaction effect cited in Micromedex®. Naranjo and WHO probability scale were used to assess the causality assessment of documented ADRs., Hartwig et al. scale was implemented to determine the severity. However, Modified Schumock and Thornton Scale and predictability criteria were used to assess the preventability and predictability of ADRs.
The data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 24.0 (IBM, Armonk, New York, USA). Multivariate logistic regression analysis was done to assess the predictors of pDDIs. Chi-square test was used to assess the association between categorical variables and pDDIs. Odds ratio (OR) with 95% confidence interval (95% CI) was performed. Pearson correlation test was done to assess the relationship between the number of DDIs and continuous demographic variables (age, number of comorbidities, number of medication, and duration of hospital stay). A P < 0.05 and P < 0.01 were considered as statistically significant and highly significant, respectively.
Approval for the conduct of the study was obtained from the institutional and regional research and ethics committee (MOHP/RAK/SUBC/NO-372016-PG-P).
| Results|| |
A total of 155 patients were recruited and screened for the pDDIs during the study period, among which majority (128 [82.6%]) were male. The mean age of the study patients was found to be 58.8 ± 14.3 years. Most of the study patients were expatriates (120 [77.4%]) followed by UAE nationals (35 [22.6%]). The average length of hospital stay of the patients screened was found to be 4.8 ± 2.7 days. The mean number of drugs administered was 9.1 ± 2.8 per patient.
The overall relevant frequency for pDDIs among the study patients was found to be 87.74%. A total of 842 pDDIs and 79 pairs of interacting drugs were identified among 155 patients. A total of 136 (87.7%) of the patients had a minimum of single DDI, irrespective of severity. Severity, documentation grades, mechanism, onset, and probability assessment of pDDIs are shown in [Table 1].
|Table 1: Severity, documentation grades, mechanism, onset, and probability assessment of the potential drug-drug interactions in the studied patients|
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A total of 79 different pairs of interacting drugs associated with the use of cardiovascular medicines were detected. Aspirin with bisoprolol (98 [11.64%]) followed by aspirin with clopidogrel (95 [11.28%]) was the most commonly documented pDDIs. The most common 15 pairs of pDDIs are mentioned in [Table 2].
|Table 2: Most common pair of potential drug-drug interactions in the cardiology department's patients|
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A total of 12 ADRs were reported among 155 patients as a possible outcome of pDDIs. Thus, the relevant frequency of suspected ADRs, as a possible outcome of pDDI, was found to be 7.74%. Majority (n = 11) were males. Higher number (n = 9) of ADRs documented in expatriates. Bleeding was the most commonly suspected ADR (n = 6) followed by hyperkalemia (n = 3), bradycardia (n = 1), hypokalemia (n = 1), and raised prothrombin time (n = 1). Tenecteplase was the most commonly involved drug in ADR (6 [50%]) followed by perindopril (n = 2) and enoxaparin, losartan, furosemide, and diltiazem (n = 1) each. In the majority of cases (n = 7), withdrawal of suspected medication was done, while (n = 5) patients were managed by the alteration in the prescribed dose of medication. No treatment was given to eight patients except drug withdrawal, and the remaining (2 [16.66%]) patients were treated symptomatically, while two patients were treated with specific treatments. Majority of the suspected ADRs (n = 10) were recovered.
Naranjo causality assessment of ADRs reveals that majority of the suspected ADRs were probable (n = 7) followed by possible type (n = 5). While a majority of the suspected ADRs were possible (n = 5) in nature followed by probable type (n = 7) by WHO probability assessment. Majority of the suspected ADRs were mild in nature (7 [58%]) followed by moderate (n = 2) and severe (n = 3). Seven ADRs were of the predictable type and (n = 5) were not predictable. Majority of the suspected ADRs (n = 8) were of probably preventable type followed by definitely preventable (n = 2) and not preventable type (n = 2).
A significant (P < 0.05) association was documented between nationality (χ2 = 4.722; P = 0.041), length of hospital stay (χ2 = 6.126; P = 0.021), number of medical conditions diagnosed (χ2 = 10.379; P = 0.015), and occurrence of pDDIs. However, a highly significant (P < 0.01) association was documented for number of drugs prescribed (χ2 = 35.18; P < 0.01) and polypharmacy (χ2 = 32.06; P < 0.01). It was noted that patients taking seven or more drugs (P = 0.002; OR 9.90; 95% CI = 2.28–42.99), polypharmacy (P = 0.063; OR = 3.86; 95% CI = 0.93–16.08), and number of medical conditions (P = 0.006; OR 0.25; 95% CI = 0.09–0.68) were significant predictors of pDDIs. A statistically highly significant (P < 0.01) positive linear correlation was observed between number of drugs prescribed and total number of pDDIs (r = 0.547; P < 0.01).
| Discussion|| |
The higher occurrence of pDDIs in males in our study is consistent with previously published reports.,, The average number of drugs prescribed/patient in our study was also similar to other studies., The similarities in observations could be due to similarities in the prescribing pattern of the medications in CVD patients. There was a variance in the reported number (higher and lesser) of pDDIs documented in other studies compared to our study.,, The difference in the number of pDDIs and interacting pairs of a drug can be because of the number of drugs, classes of drug, and drugs prescribed for other comorbidities. CVD patients often have multiple comorbidities, for which they use many drugs other than cardiac medication giving rise to more pDDIs.
Majority of the pDDIs were of moderate severity in our study. This observation of ours is consistent with a study conducted by Murtaza et al., which reported a higher number of moderate (55%) followed by major severity (45%) pDDIS. The slight difference in severity grades of pDDIs is not validated and can be possible due to the difference in resources/database used for identification of pDDI.
As far as documentation grades are concerned, half of the pDDIs were fair followed by good and excellent. Ismail et al. reported most interaction to be good and fair in documentation similar to our study. However, Shakeel et al. reported slight different documentation grades; almost half were good followed by fair, then excellent documentation. The difference in the documentation grade reporting could be due to the difference in the type of drugs prescribes for patients in different study settings, availability, and affordability of medications, local and international treatment guidelines, and prescribers' preference based on their experience.
The incidence rate of pDDIs in the current study falls within this reported range of other studies.,,,,, A wide variation of the occurrence of pDDIs among cardiac patients may be because of various factors such as age, polypharmacy, and comorbidities which affects the potential of DDIs.
A majority (58.3%) of the interactions were pharmacokinetic in nature as reported by Sharma et. al. This was in contrast to the findings of our study, which documented a higher number of pharmacodynamic interactions. The onset of pDDIs in our study was found to be unknown for most. Similar results were reported by Shakeel et al. The possibility of a high number of unknown onset can be due to underreporting of pDDIs or not observed or noticed promptly.
Age, gender, length of stay, number of drugs received/polypharmacy, comorbidities, and other patient characteristics are known to be as the potential predictors of DDIs. Our findings are almost in accordance with the findings of Crucial-Souza and Thomson, which documented some medications and comorbidities as the significant predictor of DDIs in patients. Moreover, Jain et al. identified age and polypharmacy having a positive correlation with the drug interaction. Polypharmacy, length of stay, and concurrent illness are the most crucial determinants of pDDIs. Predictors of pDDIs. Our findings are similar to Murtaza et al., which revealed age, length of stay, and number of drug prescribed to be positive predictors of pDDIs. However, in Sharma et al.'s study, length of hospital stay, number of medications, and concurrent illness were associated risk factors. It is well-known fact that more extended length of hospital stay and concurrent illness contributes to increased exposure to drugs, which might lead to higher prevalence of pDDIs.
The reason for a low occurrence of ADRs in our study could be possibly due to the short length of stay of patients as our study was conducted in a secondary care hospital. Bleeding, bradycardia, and abnormal serum potassium levels were the most commonly documented ADR in our study. Almost similar types of clinical outcome of pDDI have been documented in a study conducted by Kovačević et al. However, in their study, the most common most common potential clinical outcome was cardiovascular related.
The strengths of our study are that the pharmacist was involved in the monitoring of pDDIs. Further, we used the DIPS for assessing the probability of pDDIs, which is not documented in many previous studies and the study also documented that ADRs occurred as a possible consequence pDDIs. The main limitations of our study were short study duration, small sample size, noninternational, and conducted in a single center.
In conclusion, the study fosters the possible role of the pharmacist in regular monitoring of cardiac patients receiving different medications. Multicenter interventional studies are required to assess the effect of interventions in preventing and managing pDDIs, which further improves patient outcomes.
| Authors' Contribution|| |
Muhammad Zeeshan Khan, Sathvik Belagodu Sridhar and Pradeep Kumar Gupta contributed in designing, conducting the study, acquisition of data, analysis, and interpretation of the data. All authors read and approved the manuscript.
Our sincere thanks to Dr. Padma, Dean RAK College of Pharmaceutical Sciences, for all the support. Our heartfelt thanks to Dr. Gurumadhva Rao, President of RAK Medical and Health Sciences University, for all the support and encouragement.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2]