|Year : 2021 | Volume
| Issue : 1 | Page : 45-51
Assessment of potential drug interactions among psychiatric inpatients receiving antipsychotic therapy of a secondary care hospital, United Arab Emirates
Haneen A. R. Aburamadan1, Sathvik Belagodu Sridhar1, Talaat Matar Tadross2
1 Department of Clinical Pharmacy and Pharmacology, RAK College of Pharmaceutical Sciences, RAK Medical and Health Sciences University, Ras Al-Khaimah, UAE
2 Psychiatry, RAK College of Medical Sciences, RAK Medical and Health Sciences University; Department of Psychiatry, Ibrahim Bin Hamad Obaidallah Hospital, Ras Al-Khaimah, UAE
|Date of Submission||09-Aug-2020|
|Date of Decision||03-Sep-2020|
|Date of Acceptance||13-Oct-2020|
|Date of Web Publication||09-Jan-2021|
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
The majority of the antipsychotic drugs are also known to interact with other co-administered drugs. Drug–drug interaction (DDI) reports among patients receiving antipsychotic medications are common. The study aims to identify the potential drug–drug, drug–tobacco, and drug–ethanol interactions associated with antipsychotics and significant predictors of potential DDIs (pDDIs). A prospective observational study was conducted among psychiatric inpatients receiving antipsychotic therapy and met the inclusion criteria that were reviewed for the presence of pDDIs using DRUGDEX-Micromedex database 2.0. The identified pDDIs were graded according to the severity and type of documentation. A total of 110 patients had a minimum of a single interaction, and the overall frequency of pDDIs reported was 64.7%. Of 158 pDDIs, 92 interactions (58.2%) were of major severity, while 66 interactions were of moderate severity (41.8%). Olanzapine with valproate (40 [25.3%]) was the most commonly documented pDDIs, followed by risperidone with valproate (20 [12.6%]). Olanzapine with tobacco (20 [69%]) was the most common drug–tobacco interaction. Simultaneously, olanzapine with ethanol was the most common potential drug and ethanol interaction (9 [50%]). Variables such as the number of drugs and polypharmacy statistically significantly predicted pDDIs (F[7, 162] = 8.155, P < 0.05, R2 = 0.26). Knowing the severity of different pDDIs will help clinicians and prescribers monitor patient safety through regular monitoring for interactions and adverse drug effects in future. The number of medications and polypharmacy was found to be the most significant predictor of pDDIs.
Keywords: Antipsychotics drug interactions, drug–drug interaction, drug–ethanol interaction, drug–tobacco interaction
|How to cite this article:|
Aburamadan HA, Sridhar SB, Tadross TM. Assessment of potential drug interactions among psychiatric inpatients receiving antipsychotic therapy of a secondary care hospital, United Arab Emirates. J Adv Pharm Technol Res 2021;12:45-51
|How to cite this URL:|
Aburamadan HA, Sridhar SB, Tadross TM. Assessment of potential drug interactions among psychiatric inpatients receiving antipsychotic therapy of a secondary care hospital, United Arab Emirates. J Adv Pharm Technol Res [serial online] 2021 [cited 2022 Jan 29];12:45-51. Available from: https://www.japtr.org/text.asp?2021/12/1/45/306556
| Introduction|| |
A drug–drug interaction (DDI) has occurred when the effects of one drug are changed after the concomitant administration of another drug, leading to synergistic, additive, or antagonistic effects. DDIs are known to be one of the common causes of increased hospital admissions, length of hospital stays, treatment cost, morbidity, and mortality., DDIs can be a crucial factor for the occurrence of adverse drug reactions (ADRs) and adverse drug events. Earlier studies have stated that 5% of the ADRs in hospital settings are because of DDIs, while DDIs are likely to be the contributing factor for about 3-26% of ADRs requiring hospitalization.
Potential DDIs (pDDIs) are those interactions, which can be predicted from the known pharmacological actions of the drugs and have the possibility to alter the effects of the co-administered drug., Conversely, all pDDIs may not necessarily contribute to clinically significant or actual DDIs. However, pDDIs may need closer monitoring. Various pharmacoepidemiological studies conducted in the different parts of the world in various study settings, study design, duration, and diverse population and with various DDI assessment tools have reported the prevalence rates of pDDIs, varying from 5% to 91%.,
Based on the underlying mechanism, DDIs are categorized as pharmacokinetic and pharmacodynamic interactions. Patients suffering from psychiatric illness are at risk for DDIs because they are highly likely to receive chronic treatment using several medications to manage the signs and symptoms or due to medical and psychiatric comorbidity or multiple prescribers may be required in the management.,
Most antipsychotics are extensively metabolized by the hepatic cytochrome P450 enzymes. CYP1A2, CYP2D6, and CYP3A4 isoenzymes are of particular importance to the metabolism of antipsychotics. Consequently, the frequency of CYP mediated DDIs is found to be high in psychiatric patients. Co-administration of inhibitors or inducers of these enzymes can lead to clinically significant adverse events or diminished clinical efficacy, respectively.,,
Pharmacodynamic interactions are the most common interactions encountered in clinical practice. Clinically, significant pharmacodynamic interactions may cause serious complications such as extrapyramidal symptoms (EPS), serotonin syndrome, QT prolongation, and seizure. DDIs associated with antipsychotic medications may cause decreased efficacy and/or poor tolerability affecting the clinical outcomes.
Demographic and treatment variables such as age, gender, primary diagnosis, number of medications received, or polypharmacy have shown a significant association with the occurrence of pDDIs., There are few studies assessing solely the nature of pDDIs in psychiatric settings.,, Tobacco smoking is common among patients receiving psychotropic medications, consequently reducing the plasma concentrations of the drugs, while enhanced central nervous system suppression resulting in impaired concentration, coordination, hypotension, and increased sedation is documented with alcohol and antipsychotics.
Studies documenting pharmacoepidemiology of pDDIs among psychiatric disorder patients of the UAE are scarce. The primary aim of the study was to identify the pDDI, drug–tobacco, and drug–ethanol interactions associated with antipsychotics. The study also aims at analyzing the frequency, types, severity, and documentation grades of pDDIs and to identify the significant predictors of pDDIs.
| Materials and Methods|| |
This was a prospective observational study carried out in an inpatient psychiatric setting. We initiated the study after the approval from the Institutional and Regional Research and Ethics Committee approval (8-2015-PG-P and RAKREC-Aug-2015-3). The duration of the study was 7 months. The required sample size was 169, with a confidence level of 95%, a margin of error of 5%, and the population proportion of 50%.
Psychiatric inpatients of either gender, aged >13 years, and hospitalized in the psychiatry ward over 24 h were included in the study. Furthermore, we included patients diagnosed with any psychiatric disorder and managed with at least one antipsychotic medication. The study investigators identified the cases by attending ward rounds on alternative days (3 days/week) at the study site along with the treating psychiatrist.
We collected the required data from the electronic medical records of patients and entered into a data collection form designed for the study. The pDDIs were identified using DRUGDEX-Micromedex database 2.0. The drugs, which are concomitantly received by the patients, were entered into the database for screening the presence of pDDIs. The database screens for pDDIs and above classifies pDDIs according to severity and documentation. The prescription-related polypharmacy was evaluated and categorized as minor, moderate, and major using Veehof et al. Scale.
SPSS version 24.0 (IBM, New York, USA) was used to analyze the data. Descriptive statistics were used to evaluate the data. We assessed comparisons between categorical variables using the Chi-square test. A Pearson correlation was done to estimate the relationship between continuous variables and its association with the number of pDDIs. The odds ratio (OR) was also calculated. Multiple regression analysis was carried out to detect the predictors of pDDIs. P < 0.05 was considered as statistically significant, further any value ≤0.01 was considered as highly significant.
| Results|| |
A total of 170 patients were included in the study and most of them were male (98 [57.6%]). The age of patients varied from 13 to 79 years, with a mean age of 34.8 ± 12.9 years. A total of 78 (45.9%) of the patients were UAE nationals and the remaining were expatriate population. The length of hospital stay as inpatients varied from 2 to 74 days, with a mean length of stay of 15.8 ± 12 days. A total of 52 (30.6%) patients had other comorbidities. Diabetes (18 [10.5%]) and hypertension (17 [10%]) were the most commonly documented comorbidities.
The majority of the study patients were nonsmokers (108 [63.5%]), also had no history of alcohol usage (141 [82.9%]). The mean number of medications received by the patients was 2.69 ± 1.09. The majority of the patients were categorized to have minor polypharmacy (114 [67.1%]), followed by moderate polypharmacy (34 [20%]), no polypharmacy (20 [11.80%]), and major polypharmacy (2 [1.2%)].
A total of 158 pDDIs were identified, quantified, and classified in 170 patients who got enrolled during the study period. Moreover, a total of 41 pairs of interacting drugs were recognized. A total of 110 patients had a minimum of a single interaction. The overall frequency of pDDIs among the study population was 64.7%. The predominance of pDDIs was documented in patients diagnosed with bipolar I disorder (29 [26.4%]), followed by schizophrenia (15 [13.6%]), schizoaffective disorder (14 [12.7%]), substance use disorder (10 [9.1%]), major depressive disorder 7 (6.4%), and alcohol use disorder (7 [6.4%]).
Olanzapine and valproate were the most commonly documented pairs of interacting drugs. The 10 most common pDDIs with their frequencies, severity, and documentation grade in the analyzed prescriptions are highlighted in [Table 1].
|Table 1: Most frequent potential drug- drug interactions associated with antipsychotics|
Click here to view
Among the pDDIs identified, 92 (58.2%) were major and 66 (41.3%) were of moderate severity. The documentation grade of the predominance of the pDDIs was of fair (73 [46.2%]), followed by good (45 [28.5%]) and excellent (40 [25.3%]).
Among the 62 patients who were usual smokers of tobacco cigarettes, 29 (46.7%) patients were exposed to the interaction between antipsychotics prescribed during hospitalization and tobacco. All of them were males. Olanzapine was involved in the largest number of interactions with tobacco smoking (20 [69%]) [Table 2].
Among the 29 patients who were usual or heavy drinkers of alcohol, 18 (10.6%) patients were exposed to the interaction between antipsychotics prescribed upon discharge and alcohol. All of them were males. Olanzapine was involved in the largest number of interactions with alcohol (9 [50%]) [Table 3].
A statistically significant but weak positive linear correlation between duration of hospital stay and a number of DDIs (r = 0.158, P = 0.039) and a strong, highly significant positive association was documented between the number of drugs taking and the number of DDIs (r = 0.514, P < 0.01). The variables which were positively correlated with the risk of occurrence of pDDIs were length of hospital stay (OR: 0.440, 95% confidence interval [CI]: 0.216–0.893, P = 0.021), number of drugs prescribed (OR: 3.266, 95% CI: 2.0–5.0, P < 0.01), and polypharmacy (OR: 0.0049, 95% CI: 0.0001–0.3045, P < 0.05) [Table 4] and [Table 5].
|Table 4: Association between demographic and treatment-related variables and number of drug- drug interactions|
Click here to view
We ran multiple regression to predict the total number of pDDIs. It revealed that only variables such as the number of drugs and polypharmacy statistically significantly predicted pDDIs (F (7, 162) = 8.155, P < 0.01, R2 = 0.261). Number of drugs and polypharmacy added statistically significantly to the prediction, P < 0.01, as presented in [Table 6].
|Table 6: Predictors of potential drug-drug interactions by multiple regression analysis|
Click here to view
| Discussion|| |
The overall frequency of pDDIs documented in our study was in accordance with the findings of Ismail et al. (64.8%) study. In contrast, other studies have reported a lower frequency (23%) and a higher frequency (77.9%) of pDDIs., This variation in the reported frequency could be due to the variance in the study designs, sample sizes, and consideration of classes of pDDIs (from minor to contraindicated). In our study, a higher number of pDDIs were identified in bipolar I disorder patients, whereas, in contrast, a study reported higher rates of pDDIs in patients with depression. This variation in the findings is attributed to the divergence in the type of study population included.
The most commonly identified pDDIs in our study were olanzapine and valproate sodium, followed by risperidone and valproate sodium. A similar type of study reported a combination of first-generation antipshychotic and second-generation antipsychotic (olanzapine and haloperidol) as the most common pDDI. DDIs between olanzapine and haloperidol are known to increase the risk of developing EPS. Other studies have documented most common pDDIs between haloperidol and trihexyphenidyl (72 [5.3%]), amitriptyline and fluoxetine (24.5%), and antipsychotics and beta-blockers. The difference in these findings could be due to divergences in the study setting, duration, and mainly the study population included.
The mainstream of the pDDIs identified in our study was of major severity type, whereas Balen et al. documented 34 serious and 20 moderate pDDIs. Another study reported 15.2% major and 84.6% moderate type of pDDIs. The documentation grade of a majority of the pDDIs in our study was the fair type, whereas Ismail et al. reported 38 (4.6%) excellent; 548 (66.4%) good; and 239 (29%) fair type of pDDIs.
It is crucial to note that regardless of the prescriber's knowledge about pDDIS of antipsychotics with other drugs, the benefit of these treatment regimens may outweigh the risks caused by DDIs, especially for patients with severe mental illnesses. No serious clinical outcome caused by pDDIs was detected in our study. Therefore, all the interactions documented were of possible nature according to recent clinical studies.
It is worthy to mention that patients on antipsychotic therapy who are regular tobacco users may need higher doses of antipsychotics than nonsmokers. This is because of the induction in the activity of human cytochromes P450 (CYP) 1A2 and 2B6, which metabolizes several antipsychotics, lowering their expected plasma concentrations., Conversely, upon smoking cessation, tobacco users may require a decrease in the dosage of antipsychotics. Among the 29 patients who were usual or heavy drinkers of alcohol, 18 (10.6%) patients were exposed to the interaction between antipsychotics prescribed upon discharge and alcohol. All of them were males. Consistently, Green et al. reported that 21% of patients with a history of alcohol abuse are less likely to respond to antipsychotics compared with people without the alcohol abuse disorder.
A number of medications prescribed and polypharmacy were the significant predictors of the occurrence of pDDIs in our study, since many psychiatric patients are expected to receive multiple medications due to the presence of some additional comorbidities along with their psychiatric illnesses. In accordance with our findings, Oesterhus et al. documented the number of medications as the most significant predictor of DDIs in patients with mild dementia, while other studies reported predictors such as prescribed medications, race and female sex, and patient's age.
The primary limitation of our study was it was a single center-based study with a limited sample size and short study duration. Hence, the findings of this study cannot be completely generalized. The frequency, severity, and documentation grades of pDDIs solely dependent on the single analyzing software, i.e., Micromedex. Studies have documented variation in the frequency and nature of pDDIs with different drug interaction analyzing softwares. In addition, a good number of patients included in the study were not receiving other medications apart from psychotropic medications.
| Conclusion|| |
The study necessitates the importance of continuous patient monitoring to identify the adverse events and careful selection of therapeutic alternatives if feasible. The pharmacist can contribute significantly in educating the patients or their family members regarding DDIs, polypharmacy, ADRs, and assessing the patient medication history. Further multicenter studies are required to substantiate the findings of our study.
We sincerely thank all the health-care staff of the psychiatry study setting and the director of the hospital for their kind support. The authors thank the President of RAK Medical and Health Sciences University, Ras Al Khaimah, and the Dean, RAK College of Pharmaceutical Sciences, for their support during the work period.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Wiggins BS, Saseen JJ, Page RL 2nd
, Reed BN, Sneed K, Kostis JB, et al
. Recommendations for management of clinically significant drug-drug interactions with statins and select agents used in patients with cardiovascular disease: A Scientific Statement From the American Heart Association. Circulation 2016;134:e468-95.
Lubinga SJ, Uwiduhaye E. Potential drug-drug interactions on in-patient medication prescriptions at Mbarara Regional Referral Hospital (MRRH) in western Uganda: Prevalence, clinical importance and associated factors. Afr Health Sci 2011;11:499-507.
Dumbreck S, Flynn A, Nairn M, Wilson M, Treweek S, Mercer SW, et al
. Drug-disease and drug-drug interactions: Systematic examination of recommendations in 12 UK national clinical guidelines. BMJ 2015;350:h949.
Glintborg B, Andersen SE, Dalhoff K. Drug-drug interactions among recently hospitalized patients-frequent but mostly clinically insignificant. Eur J Clin Pharmacol 2005;61:675-81.
Jimmy OD, Rani RH, Indira R, Ramjan S. Study of drug-drug interactions in the medication charts in medicine wards at a tertiary care hospital. Indian J Pharm Pract 2012;5:61-4.
Ferner RE, Aronson JK. Communicating information about drug safety. BMJ 2006;333:143-5.
Alvim MM, Silva LA, Leite IC, Silvério MS. Adverse events caused by potential drug-drug interactions in an intensive care unit of a teaching hospital. Rev Bras Ter Intensiva 2015;27:353-9.
Mistry M, Gor A, Ganguly B. Potential drug-drug interactions among prescribed drugs in paediatric outpatients department of a tertiary care teaching hospital. J Young Pharm 2017;9:371-5.
Riechelmann RP, Tannock IF, Wang L, Saad ED, Taback NA, Krzyzanowska MK. Potential drug interactions and duplicate prescriptions among cancer patients. J Natl Cancer Inst 2007;99:592-600.
Al-Qerem W, Jarrar Y, Al-Sheikh I, ElMaadani A. The prevalence of drug-drug interactions and polypharmacy among elderly patients in Jordan. Biomed Res Tokyo 2018:29;2561-9.
Snyder BD, Polasek TM, Doouge MP. Drug interactions: Principles and practice. Aust Prescr 2012;35:85-8.
English BA, Dortch M, Ereshefsky L, Jhee S. Clinically significant psychotropic drug-drug interactions in the primary care setting. Curr Psychiatry Rep 2012;14:376-90.
Ramadan MI, Werder SF, Preskorn SH. Drug-drug interactions: Avoid serious adverse events with mood stabilizers. Curr Psychiatry 2005;4:27-40.
Hefner G, Wolff J, Hahn M, Hiemke C, Toto S, Roll SC, et al
. Prevalence and sort of pharmacokinetic drug-drug interactions in hospitalized psychiatric patients. J Neural Transm (Vienna) 2020;127:1185-98.
Kennedy WK, Jann MW, Kutscher EC. Clinically significant drug interactions with atypical antipsychotics. CNS Drugs 2013;27:1021-48.
Spina E, Scordo MG, D'Arrigo C. Metabolic drug interactions with new psychotropic agents. Fundam Clin Pharmacol 2003;17:517-38.
Guo JJ, Wu J, Kelton CM, Jing Y, Fan H, Keck PE, et al
. Exposure to potentially dangerous drug-drug interactions involving antipsychotics. Psychiatr Serv 2012;63:1080-8.
Moura CS, Acurcio FA, Belo NO. Drug-drug interactions associated with length of stay and cost of hospitalization. J Pharm Pharm Sci 2009;12:266-72.
Wijesinghe R. A review of pharmacokinetic and pharmacodynamic interactions with antipsychotics. Ment Health Clin 2016;6:21-7.
Lucca JM, Ramesh M, Parthasarathi G, Raman R. An adverse drug interaction of haloperidol with levodopa. Indian J Psychol Med 2015;37:220-2.
] [Full text]
Kirilochev OO, Dorfman IP, Umerova AR, Bataeva SE. Potential drug-drug interactions in the psychiatric hospital: Frequency analysis. Res Results Pharmacol 2019;5:1-6.
Balen E, Giordani F, Cano MF, Zonzini FH, Klein KA, Vieira MH, et al
. Interações medicamentosas potenciais entre medicamentos psicotrópicos dispensados. J Bras Psiquiatr 2017;66:172-7.
Desai HD, Seabolt J, Jann MW. Smoking in patients receiving psychotropic medications: A pharmacokinetic perspective. CNS Drugs 2001;15:469-94.
Cheng C, Mithoowani F, Ungar T, Lee M. Interaction between psychotropic medications and alcohol: Perceptions among patients attending an adult mental health day hospital program. Can J Hosp Pharm 2018;71:7-13.
Veehof L, Stewart R, Haaijer-Ruskamp F, Jong BM. The development of polypharmacy. A longitudinal study. Fam Pract 2000;17:261-7.
Ismail M, Iqbal Z, Khattak MB, Javid A, Khan MI, Khan TM. Potential drug-drug interactions in psychiatric ward of a tertiary care hospital: Prevalence, levels and association with risk factors. Trop J Pharm Res 2012;11:289-96.
Gomberg RF. Interaction between olanzapine and haloperidol. J Clin Psychopharmacol 1999;19:272-3.
Siwek M, Woroń J, Gorostowicz A, Wordliczek J. Adverse effects of interactions between antipsychotics and medications used in the treatment of cardiovascular disorders. Pharmacol Rep 2020;72:350-9.
Jain T, Bhandari A, Ram V, Parakh M, Wal P, Nagappa AN. Drug interactions and adverse drug reactions in hospitalized psychiatric patients a critical element in providing safe medication use. German J Psychiatry 2011;14:26-34.
Sagud M, Mihaljević-Peles A, Mück-Seler D, Pivac N, Vuksan-Cusa B, Brataljenović T, et al
. Smoking and schizophrenia. Psychiatr Danub 2009;21:371-5.
Lucas C, Martin J. Smoking and drug interactions. Aust Prescr 2013;36:102-4.
Green AI, Tohen MF, Hamer RM, Strakowski SM, Lieberman JA, Glick I, et al
. First episode schizophrenia-related psychosis and substance use disorders: Acute response to olanzapine and haloperidol. Schizophr Res 2004;66:125-35.
Oesterhus R, Aarsland D, Soennesyn H, Rongve A, Selbaek G, Kjosavik SR. Potentially inappropriate medications and drug-drug interactions in home-dwelling people with mild dementia. Int J Geriatr Psychiatry 2017;32:183-92.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]