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 Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 19  |  Issue : 1  |  Page : 29-35

Incidence and predictors of adverse drug reactions caused by drug-drug interactions in psychiatric patients: An empirical study


1 Department of Pharmacy Practice, JSS College of Pharmacy, JSS University, Mysore, Karnataka, India
2 Department of Psychiatry, JSS Hospital, Mysore, Karnataka, India

Date of Web Publication17-Dec-2015

Correspondence Address:
Jisha M Lucca
Department of Pharmacy Practice, JSS College of Pharmacy, JSS University, SS Nagara, Mysore - 570 015, Karnataka
India
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DOI: 10.4103/1119-0388.172059

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  Abstract 

Introduction: Potential drug-drug interactions (pDDIs) are very frequent in psychiatric practice and result in poor tolerability or reduced efficacy, or both, which can negatively impact patient outcomes. Clinically significant drug-drug interactions (DDIs) are the ones that can result in changes in the therapeutic effect of one of the two drugs, that is, adverse drug reactions (ADRs). The aim of this study was to identify the incidence and pattern of ADRs occurring as results of DDIs in patients with mental illness. Materials and Methods: This empirical study reviewed all the patients with a psychiatric diagnosis as per the Tenth Revision of the International Classification of Diseases (ICD-10) and received at least two medications. DDIs were identified using drug interaction software/databases, standard textbooks, and references. Result: A total of 122 ADRs were identified from 97 patients. The majority [n = 98 (68.5%)] of the DDIs involved pharmacodynamic interactions. Risperidone (41 occurrences) exhibited the greatest propensity to interact with other medications, and the most commonly observed ADR was extrapyramidal symptoms (EPS) (n = 33). More than half of the ADRs were “possible” in their causal relationship. Conclusion: The incidence of ADRs caused by DDIs in this study was 12%. Pharmacodynamic interactions accounted for the majority (68.5%) of ADRs. ADRs are an important cause of the increased burden of disease and unnecessary health-care expenditure. Intense monitoring of psychiatric patients for any DDI, and early detection and prevention of the same may result in improved therapeutic outcomes and decreased unnecessary health-care expenditure.

Keywords: Adverse drug reaction, drug-drug interactions, psychiatric patients


How to cite this article:
Lucca JM, Ramesh M, Ram D, Kishor M. Incidence and predictors of adverse drug reactions caused by drug-drug interactions in psychiatric patients: An empirical study. Trop J Med Res 2016;19:29-35

How to cite this URL:
Lucca JM, Ramesh M, Ram D, Kishor M. Incidence and predictors of adverse drug reactions caused by drug-drug interactions in psychiatric patients: An empirical study. Trop J Med Res [serial online] 2016 [cited 2019 Oct 17];19:29-35. Available from: http://www.tjmrjournal.org/text.asp?2016/19/1/29/172059


  Introduction Top


Over the last two decades, the array of available psychopharmacologic agents has expanded tremendously. In clinical practice, prescribed drug combinations are often uncontrolled experiments, with unknown potential for toxic effects.[1],[2] A drug interaction, defined as the modification of the action of one drug by another, can be beneficial or harmful, or it can have no significant effect.[3] More than 100,000 possible detrimental drug-drug interactions (DDIs) have been documented in the medical literature and pharmaceutical company data.[1] Individuals with psychiatric illnesses are at particular risk for DDIs because of the practice of symptom-based prescribing, multiple prescribers, medical comorbidity, and psychiatric comorbidity. DDIs contribute majorly to hospital admissions, treatment failures, avoidable medical complications, and subsequent health-care costs.[1],[2]

Drug interactions play a vital role in the incidence of adverse drug reactions (ADRs) in the community and in hospitals. Reducing the incidence of ADRs is a critical element of safe medication usage for hospitalized patients.[4] According to the published literature, the prevalence of potential DDIs (pDDIs) resulting in ADRs in different group of patients has been estimated in the range of 1.3 to 60%.[5],[6],[7],[8],[9] It has been estimated that 26% of ADRs requiring hospital admissions may be due to DDIs.[10] The frequency of pDDIs in psychiatric departments varies between 38.7 and 64.82.[5],[11],[12] Psychotropic medications are the reason for 50% of the ADRs occurring in hospitalized psychiatric patients, many of which can be attributed to DDIs.[9]

Very few studies have addressed the issue of DDIs in psychiatric wards in comparison to other clinical specialty wards. Studies on psychiatric patients aimed at determining the occurrence of ADRs as a result of drug interactions in a clinical setting are limited. A comprehensive assessment of the identification of ADRs due to DDIs selectively in psychiatric patients is rare in Indian settings. Thus we employ a mechanistic approach to prospectively investigate the incidence and predictors of ADRs caused by DDIs in the psychiatric patient population.


  Materials and Methods Top


Design and settings

This prospective observation study was carried out at the Psychiatry Department of JSS Hospital, Mysore over a period of 2 years from April 2012 to March 2014. The study protocol was reviewed and approved by the Institutional Ethics Committee of JSS College of Pharmacy and in addition, administrative approval was obtained from the JSS Hospital authorities prior to the commencement of the study. All the study patients provided informed consent prior to enrolment into the study.

Study patients

All the patients with a psychiatric diagnosis as per the Tenth Revision of the International Classification of Diseases (ICD-10) and receiving at least two medications (one of which was from the psychotropic class) were included in the study. Patients treated simultaneously with alternative systems of medicines, patients with illegible prescriptions, and patients who were unable to provide details of required medication were excluded. Topical agents and drugs which were not included in any of the three reference resources [13],[14],[15] were not checked for interactions.

Independent clinical panel

An independent clinical panel consisting of two consultant psychiatrists, a medical doctor, a clinical pharmacist, and a postgraduate medical student was constituted. The five-member panel reviewed and assessed the clinical significance of pDDIs identified by the study pharmacist on a weekly basis. The assessment was based on a review of the individual case, and all the information pertaining to the ADR and the pDDI. The decision by the panel was made on the basis of the members' knowledge, expertise, and consulting the appropriate literature.

Data collection procedure

All the patients admitted to the psychiatric ward were intensively monitored through daily participation in ward rounds from the day of admission to the day of discharge, whereas the outpatients were randomly reviewed on their visits to the outpatient department (OPD), in order to detect any symptoms that might be related to an ADR. Any reaction noted by the study pharmacist was brought to the notice of the concerned psychiatrist. Any adverse outcome was labeled as an adverse event only after discussion with the consultant. In the case of any difference of opinion with respect to a specific reaction, the treating psychiatrist's opinion was considered as final.

Once the ADR was identified, the patients and/or caregivers were interviewed regarding their medication use history, including the list of prescription drugs and self-medication up to 3 months prior to the visit/admission. All the information pertaining to the medication history, such as start date and end date of therapy, and names of the medications, was also documented based on the available information resources. Also, questions such as “Did you suffer from any new illness during this period?” or “Did you really take this pill?” (documented in the chart/present with the patient) were added to clarify the patient's information and to address the potential recall bias. In addition, physical verification of all the medications was done to check the consistency of the self-reported information. After identifying all the drugs administered concomitantly, potential drug-drug interactions (pDDIs) were identified using the online versions of computerized interaction detection systems such as Micromedex ®, Medscape ®, and Drugs ®, and the textbook Stockley's Drug Interactions. In the subsequent steps, pDDIs that resulted in ADRs were further investigated. by the independent clinical panel. A reported ADR was considered only when the suspected drug was involved in the DDI and the ADR corresponded to the description of the interaction effect described in drug interaction programs or the textbook. The drugs involved in the interaction were categorized as index drug (the object drug whose effect resulted in an ADR) and the interacting agent (the perpetrator drug that altered the pharmacokinetics or pharmacodynamics of the index drug, which resulted in an ADR.

The causal relationship between a pDDI and an event was assessed by using the Drug Interaction Probability Scale (DIPS).[16] Based on this scale, each of the ADIs was classified into any one of the categories as “highly probable,” “probable,” “possible,” and “doubtful.” The onset of DDIs was classified into either rapid or delayed, based on the criteria as follows:

Rapid - The effect of interaction will occur within 24 h of administration.

Delayed - The effect will occur if the interacting combination is administered for more than 24 h, that is, days to weeks.

Statistical analysis

The overall prevalence of ADRs resulting from DDIs was determined by the ratio of the total number of patients who experienced ADRs to the total number of patients with pDDI. Prevalence of ADRs based on specific characteristics was calculated by considering the number of patients who experienced ADRs with a specific characteristic in relation to the total number of study patients with pDDIs associated with that particular characteristic.

Risk factors for ADRs caused by DDIs were determined at a P < 0.05 by using multivariate regression analysis. The variables tested for identification of the predictors included age, gender, comorbid medical condition, and total number of drugs prescribed All the calculations were performed by using the Statistical Package for Social Sciences (SPSS) V21.0 software.


  Results Top


Of the 990 reported ADRs, 149 ADRs were predicted to result from pDDIs and were referred to the independent clinical panel. The panel rejected 27 ADRs, and only 122 ADRs were considered for further assessments.

A total of 2587 pDDIs were identified from 807 patients. Of the total pDDIs, 41 pDDIs were “contraindicated,” 580 were “major,” 1065 were “moderate,” and 901 were “minor” in their severity. The overall incidence of DDI was 55.2%. Of the total DDIs, 143 (5.5%) pDDIs led to 122 ADRs in 97 patients. Of the 122 identified ADRs, 105 were caused by one DDI, 10 were caused by two DDIs, four were caused by three DDIs, and three were caused by four DDIs. In six cases, one DDI resulted in more than one ADR.

ADRs were more prevalent in male [n = 57 (14.7%)] patients taking more than five drugs [n = 56 (17.6%)] and in patients with comorbid medical conditions [n = 27 (15.6%)]. Incidence of ADRs based on patient characteristics is listed in [Table 1].
Table 1: Incidence of adverse drug interaction based on patient characteristics

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Of the 143 pDDIs that led to ADRs, 27 (18.9%) DDIs were pharmacokinetic and 98 (68.5%) were pharmacodynamic interactions. The summary of the types of interactions leading to ADRs is shown in [Figure 1].
Figure 1: Types of interactions leading to ADRs

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Drugs that exhibited the greatest propensity to interact with other medications included risperidone (41 occurrences), quetiapine (30 occurrences), and olanzapine (27 occurrences). The most frequently reported drug-drug combination is risperidone with quetiapine (n = 7), followed by olanzapine with haloperidol (n = 7). Extrapyramidal symptoms (EPS) (n = 33) were the most commonly observed ADR followed by sedation (n = 20) and tremors (n = 19). The summary of the drug combinations involved in interaction-led ADRs is shown in [Table 2],[Table 3],[Table 4].
Table 2: Drugs combinations involved in pharmacodynamic interactions

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Table 3: Drug combinations involved in pharmacokinetic interactions

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Table 4: Drug combinations involved in other interactions

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Of the 122 ADRs that resulted from DDIs, 29 (23.7%) were rapid in their onset. A majority [n = 49, (40.1%)] of DDIs were moderate in their severity. The causal relationship between the pDDIs and ADRs were found to be “probable” in 79 (64.7%) interactions, “possible” in 39 (31.9%) interactions, and “doubtful” in four (3.2%) interactions. The details of onset and the causality assessment of DDIs are presented in [Figure 2].
Figure 2: Onset and causality assessment of DDIs

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Multivariate logistic regression analysis of the demographic characteristics of patients who experienced ADRs [n = 99] with the total patients of DDIs [n = 807] indicated an increase in the odds ratio (OR) from 3.481 [(1.577-7.682) P = 0.002] in patients with 2-3 medications to 7.024 [(2.745-17.976) P = 000] in patients with >5 medications. The details of risk factors for the development of ADRs resulting from DDIs are presented in [Table 5].
Table 5: Risk factors for development of ADR

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  Discussion Top


This is probably the first study from India analyzing the incidence and predictors of ADRs caused by DDIs in patients with mental illness. The incidence of pDDIs in the psychiatric department was 55.2%. Our study supported the findings of previous studies conducted on the importance of DDIs (28.7-64.8) in the psychiatric specialty.[4],[14],[17] In our study, the incidence of ADRs with respect to the study population (patients with DDIs) was 12%, which is much higher than the findings of Neto et al.,[18] and Skvrce et al.,[6] where the incidence of DDI-related ADRs was found to be 6.5% and 7.8%, respectively. This disparity may be due to the diversity of the study population, as these studies are conducted within the general population.

As the prime consideration, 149 ADRs were identified as a result of DDIs and referred to the clinical panel but, on the resultant assessment, only 122 were recognized as being directly drug interaction-related, suggesting that a two-stage evaluation by a clinical expert team is crucial for determining ADRs objectively. The clinical pharmacist performed the analysis prospectively, while the clinical panel performed the analysis in retrospectively using the data collected by the study pharmacist.

In the present study, pharmacodynamic interactions accounted for 68.5% of the total DDIs that led to ADRs. It is well documented that pharmacodynamic interactions are the most commonly encountered type of DDI in clinical practice.[3] A higher rate of DDIs with pharmacodynamic mechanism was observed in the Pawar et al. study also,[19] while a contradictory report was obtained from the Mahesh et al.[17] study conducted at the same study site where a higher number was reported with pharmacokinetic reactions. This disparity is because of differences in the study methodology: In the Mahesh et al. study they assessed only the pDDI. The majority of the pharmacodynamic interactions that led to ADRs were due to synergistic actions. This may be because of the concurrent use of drugs, which may have augmented actions at a receptor or from different mechanisms in the same tissue. For example, clonazepam induces sleep and sedation by stimulating gamma-aminobutyric acid (GABA) receptors, while quetiapine, probably by blocking the histamine receptor however when these two agents are combine administered, patient may be at an increased risk of developing sedation. Pharmacokinetic interactions that accounted for 18.9% include excretion (n = 9), metabolism (n = 15), and distribution (n = 3). In our study, we did not observe a drug interaction as a result of absorption; generally, in medical or psychiatric practice, significant clinical effects caused by changes in drug absorption are hardly ever seen.[4],[20]

The antipsychotic (olanzapine, quetiapine), mood-stabilizer (lithium), and antidepressant (amitriptyline) classes of drugs combinations were implicated in the greatest number of ADRs among psychotropic drug prescriptions. One of the reasons could be that these drug combinations are frequently administered to treat many psychiatric disorders such as schizophrenia, bipolar affective disorders, and depressive disorders in our study settings. Other studies that have assessed pDDIs due to nonpsychotropic and psychotropic drugs have found the association of similar psychotropic medications.[11],[17]

Another important concern with drug interactions is timing: The onset of clinically significant DDIs was rapid in 23.7% of the cases and delayed in 76.2%. This is in accordance with other studies.[17],[19] The pDDIs that are likely to cause clinically significant effects within 24 h, were considered as the rapid type and those that are likely to show their consequences after a few days or 1 week were considered as the “delayed type."[17] The causality of DDIs leading to ADRs as assessed by using the DIPS showed that more than half of the interactions were “probable.” This finding reveals that almost half of the interactions had a good causal association with the occurred event.

Our findings concerned the strong association of ADRs with the increased number of drugs and are consistent with other studies.[6],[20],[21] It is obvious that the use of multiple medications owing to the presence of multiple conditions increases the risk of patients developing DDI-related ADRs. The study conducted by Neta et al.[18] revealed that patients who presented six or more diseases and those who took five or more drugs had a significantly higher risk of DDI-related ADRs. Although the findings of the number of diseases and the number of drugs as predictors in the Neto et al. study differed from our study findings, the finding of gender as a predictor was similar to our study where it was reported that there was no association between either of gender and the risk of ADRs. Of the actual DDIs in our study, 99% were caused by nervous system medications, which shows a linear association with study conducted by Scarce et al.[6] Variables such as, polymedication, and state of disease increase the probability of an interaction; however, we did not find any significant age difference between patients with DDI-related ADRs.

There is no difference between age group and the development of DDI-related ADRs. One of the reasons could be attributed to psychiatrists considering the special requirements of elderly and pediatric patients and monitoring them more intensively, prescribing lower dosages, and/or avoiding high-risk drugs and dangerous combinations, thus reducing the risks of ADRs in these patients. Our findings on the strong association of DDIs with increased number of drugs and the insignificant association with gender are consistent with other studies.[22],[23],[24]

There were a few limitations with this study: We did not use an objective method such as therapeutic drug monitoring to confirm the accuracy of reported ADRs, and new drugs such as blonanserin, agomelatine, and iloperidone were not checked for their interaction profiles.


  Conclusion Top


The incidence of ADRs caused by DDIs in this study was 12%. Pharmacodynamic interactions accounted for the majority of ADRs. The study provides additional evidence about ADRs associated with DDIs relevant for psychiatric practice. Intense monitoring of any pDDI and early detection and prevention of the same may result in improved therapeutic outcomes and decreased unnecessary health-care expenditure.


  Acknowledgments Top


We thank JSS University, the Principal of JSS College of Pharmacy Mysore, and all the staff of the Mental Health Department of JSS Hospital, Mysore for their support and encouragement.

 
  References Top

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    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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