Skip to main content
NCBI home page
Saudi Pharmaceutical Journal : SPJ logoLink to Saudi Pharmaceutical Journal : SPJ
. 2022 Dec 15;31(2):207–213. doi: 10.1016/j.jsps.2022.12.004

Potential drug-drug interactions in outpatients with depression of a psychiatry department

Yang Chen 1, Lijun Ding 1,⁎
PMCID: PMC10023543  PMID: 36942274

Abstract

Objective

This study aims to explore the prevalence and associated risk factors for potential drug-drug interactions (pDDIs) in prescriptions among outpatients with depression, and report the widespread relevant drug interactions.

Methods

The cross-sectional retrospective study was conducted on outpatients in a psychiatric hospital. We included prescriptions of outpatients with a principal diagnosis of depression from April 1st to June 30th in 2021. The patients were ≥ 18 years old and treated with two or more drugs including at least one psychotropic drug. pDDIs were detected and identified mainly using Medscape’s drug interactions checker. Gender, the number of concomitant drugs, age and diagnosis were analysed as potential risk factors for the occurrence of pDDIs by logistic regression.

Results

A total of 13,617 prescriptions were included in the present analysis, and 4222 prescriptions (31.0%) were at risk of 8557 pDDIs. The risk of pDDIs in patients who were prescribed 4–6 drugs (OR: 3.49, 95% CI: 3.11–3.91, p < 0.001) or 7 or more drugs simultaneously (OR: 7.86, 95% CI: 1.58–39.04, p < 0.05) increased compared with patients prescribed 2–3 drugs. Patients with recurrent depressive disorders (OR: 1.18, 95% CI: 1.02–1.36, p < 0.05) had an increased risk of pDDIs compared with patients with depressive episodes. In terms of severity of pDDIs identified by Medscape’s drug interactions checker, 0.7%, 16.4%, 77.5% and 5.4% of pDDIs were classified as contraindicated, serious, monitor closely and minor, respectively. The most common pDDI was escitalopram + quetiapine (374 prescriptions), which was classified as serious and monitor closely due to different mechanisms of interaction. Increased central nervous system (CNS)-depressant effect was the most frequent potential clinical adverse outcome of the identified pDDIs.

Conclusions

pDDIs in outpatients with depression were prevalent in this retrospective study. The number of concomitant drugs and severity of the disease were important risk factors for pDDIs. The pDDIs of the category monitor closely were the most common, and the CNS-depressant effect was the most frequent potential clinical adverse outcome.

Keywords: Drug-drug interactions, Potential drug-drug interactions, Polypharmacy, Depression, Psychotropic drugs, Outpatients

1. Introduction

A drug-drug interaction (DDI) is defined as an alteration in a clinically meaningful way of the effect of a drug as a result of coadministration of another drug. Potential drug-drug interactions (pDDIs) are defined as possible DDIs that theoretically occur during the concurrent use of two or more drugs, regardless of whether harm actually ensues (Hines et al., 2012). Multiple studies have shown that pDDIs are common in patients, the prevalence of which varies from 16% to 96% in different studies (Ismail et al., 2018, Mistry et al., 2017, Nusair et al., 2020). And pDDIs can potentially lead to adverse drug events (Day et al., 2017, Leone et al., 2010, Marengoni and Onder, 2015). Significant DDIs can result in increasing the risk of treatment failure or even death (Rekic et al., 2017). It is reported that DDIs induced approximately 22% of drug withdrawals and adverse drug reactions-related hospital admissions (Dechanont et al., 2014, Huang et al., 2008). Therefore, detecting and controlling pDDIs is of critical importance for patients.

pDDIs are of special interest in patients with depression, who are often treated with numerous concurrent medications (Ereshefsky et al., 2005). Depression is one of the most common psychiatric disorders (American Psychiatric Association, 2013). Globally, 322 million people were estimated to suffer from depression in 2015, equivalent to 4.4% of the world’s population (World Health Organization, 2017). Pharmacotherapy is the main treatment strategy for depression (Guideline Development Panel for the Treatment of Depressive Disorders, 2021, Kok and Reynolds, 2017, Wang et al., 2019). Multiple antidepressants of different types and mechanisms are often used simultaneously for the treatment of depression, especially treatment-resistant depression (Wang et al., 2019). Some non-antidepressants are also used at the same time for the treatment, such as lithium, antiepileptics and atypical antipsychotics (Chen, 2019). In addition, since psychiatric and physical conditions often coexist, patients with depressive disorders are likely to take non-psychotropic drugs concurrently (Low et al., 2018). The combination of multiple antidepressants and the combination with other drugs bear the risk of pDDIs.

Previous studies indicated that the pDDIs related to psychotropic drugs are common in patients, the prevalence of which varies from 28% to 76.5% (Lai et al., 2019, Yalcin et al., 2021). The pDDIs in patients with depression can lead to serious clinical adverse effects, including decreased effectiveness, central nervous system (CNS) depression, neurotoxicity, QT-interval prolongation which is associated with an increased risk of a rare but potentially fatal form of cardiac arrhythmia called “torsade de pointes” (TdP), and serotonin syndrome which is a rare but serious and potentially fatal condition caused by over stimulation of postsynaptic serotonin receptors (Khan et al., 2019, Nguyen et al., 2020). In clinical practice, pDDIs may have occurred and produced adverse outcomes but without being identified due to a lack of attention. Therefore, it is of great significance to identify pDDIs to avoid the relevant risk and improve clinical medication safety.

Although several studies have evaluated the risk of psychotropic drugs-related pDDIs extensively, few studies have been conducted to investigate the epidemiology of pDDIs among patients with depression specially. Therefore, the aim of this study was to explore the prevalence and associated risk factors for pDDIs in prescriptions among outpatients with depression, and report the widespread relevant drug interactions.

2. Methods

2.1. Study design and data source

The cross-sectional retrospective study was conducted on outpatients at Xiamen Xianyue Hospital, which is a tertiary A-level psychiatric hospital in China. A warning system for drug interactions called “ipharmacare” (Hangzhou Yiyao Information Technology Co., ltd., China) existed in the outpatient information system of this hospital.

We included prescriptions of outpatients with a principal diagnosis of depression from April 1st to June 30th in 2021. If outpatients were principally diagnosed with depression and secondary to any other disease in this hospital, their prescriptions would be collected. The theoretical sample size was calculated based on the standards that the minimum sample size is 20 times the number of predictor variables, that there are at least 50 prescriptions in the case or control group in binary logistic regression, and that there is an expected percentage of 16% of patients with pDDIs in the case group.

The patients were ≥ 18 years old and treated with two or more drugs including at least one psychotropic drug. We excluded prescriptions containing herbal medicines due to rare knowledge of drug-herbal interactions and the inherent high variability in herbal product composition (Brantley et al., 2014). The data we collected included the age and gender of patients, their principal diagnoses and the list of medications prescribed concurrently.

2.2. Screening of prescriptions for pDDIs

Compound preparations which contained two or more pharmacologically active ingredients were analysed individually according to each ingredient. If a patient was taking the same drug in more than one prescription, the pDDI was counted only once.

pDDIs, their severity, mechanisms of interaction, potential clinical outcomes and proposed pharmaceutical interventions were searched and collected using Medscape’s drug interactions checker. Medscape is an internationally well-recognized tool for DDI checking and its performance to detect clinically relevant DDIs has been assessed (Hecht et al., 2015, Marcath et al., 2018, Pirnejad et al., 2019). The knowledge base of this checker contains updated information through the United States Food and Drug Administration announcements, systematic reviews of major medical and pharmacology journals, and practice guidelines (Tesfaye and Nedi, 2017). The severity of pDDIs in Medscape was rated as contraindicated, serious, monitor closely and minor. The mechanisms of pDDIs were classified as pharmacokinetic (PK), pharmacodynamic (PD), unspecified (vague) or unknown. When the drugs were not included in Medscape, we referred to Drugs.com, which is an online DDI screening tool with high sensitivity (Marcath et al., 2018), and pharmacology textbooks.

2.3. Statistical analysis

Categories for the number of concomitant drugs in each prescription were defined as 2–3, 4–6, and 7 or more. Categories for age were as follows: youth (18–39 years), middle age (40–64 years) and elderly (65 years or older). The diagnosis of depression was divided into depressive episodes (F32) and recurrent depressive disorders (F33) based on the International Classification of Diseases-10th revision (ICD-10), which was currently applicable in this hospital. Similar categorizations were used previously in other studies (Ismail et al., 2018, Song and Oh, 2020, Wolff et al., 2021).

Categorical variables were presented as percentages and continuous variables as means ± standard deviations. Descriptive statistics were applied to analyse the demographics of patients, number of prescriptions and concomitant drugs, depression type, and severity and type of pDDIs. Then, chi-square test as a univariable analysis and logistic regression as a multivariable analysis were performed by a stepwise selection approach to identify the potential risk factors for pDDIs. In the univariable model, the predictor variables included gender, number of concomitant drugs, age and diagnosis. The multivariable model included variables with univariable p values < 0.10, and variables considered to be possible risk factors for pDDIs in clinical practice to adjust for potential confounders. Logistic regression was performed to calculate odds ratios (ORs) and their 95% confidence intervals (95% CIs) based on the characteristics of patients. A p value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS statistical software (version 20.0, IBM).

2.4. Ethics approval

Ethics approval was obtained from the Medical Ethical Committee of Xiamen Xianyue Hospital (2022-KY-08). As the study was conducted using prescription records and the patients were not subjected to any intervention, informed consent was not applicable.

3. Results

3.1. Demographic characteristics

Finally, a total of 13,617 prescriptions including 6445 outpatients were included in this analysis. The mean age of the patients was 42.7 ± 17.7 years. Of the patients of all prescriptions, 36.3% were male and 63.7% were female; the number of concomitant drugs was measured as 2–3 (90.0%), 4–6 (9.9%) and 7 or more (0.1%); 47.8%, 38.7% and 13.5% were in the youth, middle age and elderly groups, respectively; and 93.1% and 6.9% were diagnosed with depressive episodes and recurrent depressive disorders, respectively (Table 1).

Table 1.

Characteristics of the study subjects.

Characteristics Number of prescriptions, n (%) Number of prescriptions with pDDIsa and prevalence of pDDIs, n (%)
Gender
Male 4945 (36.3) 1547 (31.3)
Female 8672 (63.7) 2675 (30.8)
Number of concomitant drugs
2–3 12,252 (90.0) 3430 (28.0)
4–6 1357 (9.9) 786 (57.9)
≥ 7 8 (0.1) 6 (75.0)
Age (years)
Youth (18–39) 6503 (47.8) 2051 (31.5)
Middle age (40–64) 5276 (38.7) 1638 (31.0)
Elderly (≥65) 1838 (13.5) 533 (29.0)
Principal diagnosis
F32b 12,673 (93.1) 3861 (30.5)
F33c 944 (6.9) 361 (38.2)
Total 13,617 4222 (31.0)
Open in a new tab
a

pDDIs: potential drug-drug interactions.

b

F32: depressive episode; cF33: recurrent depressive disorder (the International Classification of Diseases-10th revision).

3.2. Prevalence of pDDIs

A total of 4222 prescriptions (31.0%) were identified with 8557 pDDIs. The prevalence of pDDIs reached 31.3% in males and 30.8% in females; the number of concomitant drugs was measured at 2–3 (28.0%), 4–6 (57.9%) and 7 or more (75.0%); 31.5%, 31.0% and 29.0% were in the youth, middle age and elderly groups, respectively; and 30.5% and 38.2% were in depressive episodes and recurrent depressive disorders groups (Table 1).

3.3. Factors associated with pDDIs

The chi-square test showed that the prevalence of pDDIs within the group based on the number of concomitant drugs (χ2 = 477.12, p < 0.001) or diagnosis (χ2 = 24.83, p < 0.001) was significantly different, but the prevalence of pDDIs within the group of gender (χ2 = 0.28, p = 0.595) or age (χ2 = 4.33, p = 0.115) was not significantly different (Table 2).

Table 2.

Association between patient’s characteristics and the risk of potential drug-drug interactions.

Characteristics Chi-square test
 Binary logistic regression
Value p value OR (95% CI) p value
Gender 0.28 0.595 –
Male
Female
Number of concomitant drugs 477.12 < 0.001
2–3 Reference
4–6 3.49 (3.11–3.91) < 0.001
≥ 7 7.86 (1.58–39.04) < 0.05
Age (years) 4.33 0.115 –
Youth (18–39)
Middle age (40–64)
Elderly (≥65)
Principal diagnosis 24.83 < 0.001
F32a Reference
F33b 1.18 (1.02–1.36) < 0.05
Open in a new tab

A p value < 0.05 was considered statistically significant.

a

F32: depressive episode; bF33: recurrent depressive disorder (the International Classification of Diseases-10th revision).

The binary logistic regression showed that the number of concomitant drugs and diagnosis were independently associated with the occurrence of pDDIs. The risk of pDDIs in patients who were prescribed 4–6 drugs (OR: 3.49, 95% CI: 3.11–3.91, p < 0.001) or 7 or more drugs simultaneously (OR: 7.86, 95% CI: 1.58–39.04, p < 0.05) increased compared with patients prescribed 2–3 drugs. Patients with recurrent depressive disorders (OR: 1.18, 95% CI: 1.02–1.36, p < 0.05) had an increased risk of pDDIs compared with patients with depressive episodes (Table 2).

The ordinal multiclass logistic regression showed that the number of concomitant drugs was related to the severity of depression. Patients with recurrent depressive disorders (OR: 3.10, 95% CI: 2.64–3.64, p < 0.05) were found to show a greater likelihood of receiving a combination of multiple drugs in comparison with patients with depressive episodes (not shown in Table 2).

3.4. Severity, mechanisms, potential clinical outcomes and proposed pharmaceutical interventions of pDDIs

The severity of pDDIs fell under the categories contraindicated, serious, monitor closely and minor in proportions of 0.7%, 16.4%, 77.5% and 5.4%, respectively (Fig. 1A). A majority of mechanisms of pDDIs were classified as PD (78.9%), followed by PK (7.5%) (Fig. 1B). Affecting sedation (31.9%), QTc intervals (23.0%), serotonin levels (12.8%) and cytochrome P450 enzyme (CYP450) metabolism (6.8%), with the two most significant being CYP2D6 and CYP3A4, were several main mechanisms. Correspondingly, increased central nervous system (CNS)-depressant effect was the most frequent potential clinical adverse outcome of the identified pDDIs, followed by QT interval prolongation and an increased risk of serotonin syndrome.

Fig. 1.

Fig. 1

Open in a new tab

Severity (A) and type of mechanisms (B) of potential drug-drug interactions.

The most common interaction pair among all pDDIs was escitalopram + quetiapine (n = 374), followed by lorazepam + quetiapine (n = 335) and escitalopram + olanzapine (n = 312). In the category contraindicated, the most common drug pair of pDDIs was amisulpride + olanzapine (n = 21), and the majority of proposed pharmacological intervention was the prohibition of drug combinations. In the category serious, the majority of pDDIs were related to escitalopram (42.9%), and the most frequent drug pair of pDDIs was escitalopram + quetiapine (n = 374). The predominant mechanism was an increased QTc interval (52.3%). The vast majority of proposed pharmacological interventions were “avoid or use alternate drug”. In the category monitor closely, the majority of pDDIs were related to quetiapine (28.0%), and the most frequent interaction was escitalopram + quetiapine (n = 374). The predominant mechanism was increased sedation (41.2%). The majority of proposed pharmacological interventions were “use caution/ monitor/ monitor closely” (Table 3).

Table 3.

Severity, mechanism, potential clinical outcome and proposed pharmaceutical intervention of potential drug-drug interaction pairs for the top 5 of category contraindicated, top 10 of category serious and top 10 of category monitor closely.

Interacting pair Number Mechanism of interaction Potential clinical outcome Proposed pharmaceutical intervention
Contraindicated
Amisulpride + olanzapine 21 PDa Either increases toxicity of the other by other. Increased risk of neuroleptic malignant syndrome Contraindicated
Clonazepam + flupentixol 12 PD Combined use of benzodiazepines with antipsychotics can lead to increased side effects. Respiratory depression, hypotension, ataxia, arrhythmia, cardiac arrest or death Monitor clinical condition and use caution
Flupentixol + lorazepam 12 PD Combined use of benzodiazepines with antipsychotics can lead to increased side effects. Respiratory depression, hypotension, ataxia, arrhythmia, cardiac arrest or death Monitor clinical condition and use caution
Amisulpride + quetiapine 10 PD Either increases toxicity of the other by other. Increased risk of neuroleptic malignant syndrome Contraindicated
Estazolam + flupentixol 2 PD Combined use of benzodiazepines with antipsychotics can lead to increased side effects. Respiratory depression, hypotension, ataxia, arrhythmia, cardiac arrest or death Monitor clinical condition and use caution
Serious
Escitalopram + quetiapine 374 PD Escitalopram increases toxicity of quetiapine by QTc interval. QT interval prolongation Avoid or use alternate drug
Escitalopram + trazodone 67 PD Both increase serotonin levels. Increased risk of serotonin syndrome Avoid or use alternate drug
Amisulpride + escitalopram 58 PD Both increase QTc interval. QT interval prolongation Avoid or use alternate drug
Amisulpride + venlafaxine 56 PD Both increase QTc interval. QT interval prolongation Avoid or use alternate drug
Trazodone + venlafaxine 54 PD Both increase serotonin levels. Increased risk of serotonin syndrome Avoid or use alternate drug
Aripiprazole + fluoxetine 39 PD Both increase QTc interval. QT interval prolongation Avoid or use alternate drug
Aripiprazole + fluoxetine 39 PKb Fluoxetine will increase the level or effect of aripiprazole by affecting hepatic enzyme CYP2D6 metabolism. Aripiprazole toxicity Avoid or use alternate drug
Duloxetine + trazodone 35 PD Both increase serotonin levels. Increased risk of serotonin syndrome Avoid or use alternate drug
Aripiprazole + paroxetine 32 PK Paroxetine will increase the level or effect of aripiprazole by affecting hepatic enzyme CYP2D6 metabolism. Aripiprazole toxicity Avoid or use alternate drug
Escitalopram + venlafaxine 29 PD Both increase serotonin levels. Increased risk of serotonin syndrome Avoid or use alternate drug
Monitor Closely
Escitalopram + quetiapine 374 PD Either increases toxicity of the other by QTc interval. QT interval prolongation Use caution/monitor
Lorazepam + quetiapine 335 PD Both increase sedation. Increased CNSd effects Use caution/monitor
Escitalopram + olanzapine 312 PD Escitalopram increases toxicity of olanzapine by QTc interval. QT interval prolongation Use caution/monitor
Clonazepam + quetiapine 289 PD Both increase sedation. Increased CNS- and/or respiratory-depressant effects; increased sedation and impairment of attention, judgment, thinking, and psychomotor skills Use caution/monitor
Clonazepam + olanzapine 270 PD Both increase sedation. Increased CNS- and/or cardiorespiratory-depressant effects Use caution/monitor
Lorazepam + olanzapine 266 PD Both increase sedation. Increased CNS- and/or cardiorespiratory-depressant effects Use caution/monitor
Escitalopram + mirtazapine 218 PD Both increase serotonin levels. Increased risk of serotonin syndrome Modify therapy/monitor closely
Lorazepam + mirtazapine 214 PD Both increase sedation. Increased CNS- and/or respiratory-depressant effects; increased sedation and impairment of attention, judgment, thinking, and psychomotor skills Use caution/monitor
Clonazepam + mirtazapine 211 PD Both increase sedation. Increased CNS- and/or respiratory-depressant effects; increased sedation and impairment of attention, judgment, thinking, and psychomotor skills Use caution/monitor
Olanzapine + venlafaxine 166 USc Serotonin modulators may enhance dopamine blockade, possibly increasing the risk for neuroleptic malignant syndrome. Antipsychotics may enhance serotonergic effect of serotonin modulators, which may result in serotonin syndrome. Uncertain Use caution/monitor
Open in a new tab
a

PD: pharmacodynamic; bPK: pharmacokinetic; cUS: unspecified; dCNS: central nervous system.

4. Discussion

There are scarce published studies that provide directly comparable results. Previous studies are only comparable to a limited extent. Our study revealed a relatively high prevalence of pDDIs (31.0%) in outpatients with depression. This result is similar to previous studies. For example, Lai et al. retrospectively described 28 % of the children with a diagnosis of major depressive disorder had a major or moderate pDDI in the U. S. outpatient settings (Lai et al., 2019). This value is slightly higher than that in our study, which could be due to their focus on only major or moderate pDDIs. Another study based on a nationwide database in Slovenia found 25.1% of prescriptions existed pDDIs (Jazbar et al., 2018). But it should be noted that in Medscape’s drug interactions checker our study used, a pDDI can be classified as different severity levels at the same time when the underlying drug combinations have different effects, and be multiple counted, which could have increased the probability of detecting pDDIs. Contrary to our results, an analysis of elderly psychiatry outpatients revealed that 70.7% of patients were prescribed interacting agents with the capacity to produce the extension of the QT interval (Das et al., 2021). The discrepancies among the prevalence of pDDIs may be attributable to inconsistencies in study populations, designs, drug prescribing patterns and DDI screening tools.

In contrast to general hospitals where the visits from males and females presented an almost equal distribution, we found that the number of females was much higher than that of males among all visits of outpatients with depression in this psychiatric hospital. There may be two reasons for this discrepancy. First, a female preponderance of depression is universal and substantial (Parker and Brotchie, 2010, Salk et al., 2017). Second, women are more likely to seek help for depression than men (World Health Organization. Regional Office for the Eastern, 2019).

Consistent with previous studies (Jankovic et al., 2018, Johnell and Klarin, 2007, Morales-Ríos et al., 2018, Wolff et al., 2021, Zhdanava et al., 2021), our findings also showed that the number of concomitant drugs and severity of disease were the risk factors for pDDIs. Especially, a retrospective study examining both paediatric and adult patients showed that the trajectory of the average number of DDIs resembled a linear trend when considering all possible DDIs in a regimen (Butkiewicz et al., 2016). Therefore, as expected, particular concern should be given to polypharmacy. Additionally, a study integrating eight psychiatric hospitals drew a consistent conclusion in finding that cases with recurrent depressive disorders were more likely to receive a combination of multiple antidepressant drugs (Wolff, Reissner, et al., 2021).

Contrary to some previous studies, we did not find any association between gender or age and the occurrence of pDDIs. These discrepancies could be attributed to different designs, study population types and age stratifications (Jazbar et al., 2018, Johnell and Klarin, 2007, Ren et al., 2020, Song and Oh, 2020). For the factor of age, it is well known that older adult patients are more likely to take more combination drugs due to the common co-prevalence of psychiatric and physical conditions (Goldberg et al., 2009, Kessler et al., 2010). In general hospitals, the number of prescribed medications usually increases with age, leading to a gradual increase in the prevalence of pDDIs (Butkiewicz et al., 2016, Offerhaus, 1997). However, our study was only based on data obtained from the hospital information management system at the study site, where outpatients are less likely to be prescribed for the treatment of physical illnesses. This may be another reason for the inconsistent results.

In this study, most pDDIs were classified as category monitor closely, followed by category serious, which is in line with previous studies (Ismail et al., 2018, Jazbar et al., 2018). These findings reinforce the need for patient therapy monitoring through proper follow-up for any adverse events due to the concomitant administration of multiple drugs.

Our analysis showed that the most frequent mechanism of interaction was PD, which was similar to the study about psychotropic drugs conducted by Wolff et al. (Wolff, Reissner, et al., 2021). Based on other similar studies (Ramos-Esquivel et al., 2017, Song and Oh, 2020), we believe that the category of mechanisms of pDDIs may be related to the drug prescribing pattern and type of drugs and diseases studied.

In accordance with the previous study (Yalcin et al., 2021), we found that the most common category of interacting pairs was that of antidepressants used in combination with atypical antipsychotics, and escitalopram + quetiapine was prioritized. For the treatment of major depressive disorder, atypical antipsychotics, such as quetiapine, olanzapine, aripiprazole and risperidone, are often used to augment antidepressants, but which are associated with an increased risk of discontinuation. Nelson and Papakostas reported that the rate of discontinuation of patients with major depression disorder due to adverse events was significantly higher for the atypical antipsychotics group than the placebo group. Additionally, during continuing treatment, there may be other secondary effects that in the aggregate affect tolerability and patient acceptance. The atypical antipsychotics agents also are associated with a variety of relatively serious adverse effects, such as metabolic syndrome, extrapyramidal symptoms, and rare but serious symptoms such as tardive dyskinesia and neuroleptic malignant syndrome (Nelson and Papakostas, 2009).

Similar to previous studies (Nguyen et al., 2020, Sandson et al., 2005, Wolff et al., 2021), we reported that the main potential clinical adverse consequences derived from pDDIs were enhanced CNS depression, QT interval prolongation and increased risk of serotonin syndrome. For example, Hefner et al. conducted a cross-sectional study for hospitalized psychiatric patients in ten psychiatric hospitals in Germany and found that 50% of cases were detected as having a known or possible risk for TdP (Hefner et al., 2021). Das et al. carried out a prospective cross-sectional study in a psychiatry outpatient department in India and reported that 47.9% of outpatients were identified as receiving interacting medications with the ability to induce TdP (Das et al., 2019). On the other hand, the combination of either selective serotonin reuptake inhibitor, serotonin-norepinephrine reuptake inhibitor or any of miscellaneous agents could precipitate serotonin syndrome (Volpi-Abadie et al., 2013).

There are some limitations to this study. First, the drug interaction checker provides only a “potential” estimate of DDI occurrence, and further research can be conducted to evaluate the clinical relevance of pDDI. Second, our study was done mainly using only one DDI database, but different databases might interpret the pDDI and its clinical severity in different ways (Castaldelli-Maia et al., 2021, Jankovic et al., 2018, Liu et al., 2017, Marcath et al., 2018). To improve the accuracy of pDDI evaluation, the use of two or more DDI screening tools is suggested. Last, we did not determine the comprehensive medications of outpatients used simultaneously, which might have led to an underestimated prevalence of pDDIs.

5. Conclusions

A substantial prevalence (31.0%) of pDDIs has been observed in outpatients with depression in this retrospective study. The number of concomitant drugs and severity of the disease were important risk factors for pDDIs. The pDDIs of the category monitor closely were the most common, and the CNS-depressant effect was the most frequent potential clinical adverse outcome. It is necessary to detect and prevent relevant pDDIs, which will contribute to patient safety.

Funding

This work was supported by a scientific research project of Xiamen Xianyue Hospital (Grant No: 2021-XYYB06).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Peer review under responsibility of King Saud University.

Contributor Information

Yang Chen, Email: chymina903@163.com.

Lijun Ding, Email: dr.juneding@foxmail.com.

References

  1. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM) (5th ed.).
  2. Brantley S.J., Argikar A.A., Lin Y.S., Nagar S., Paine M.F. Herb-drug interactions: challenges and opportunities for improved predictions. Drug Metab. Dispos. 2014;42(3):301–317. doi: 10.1124/dmd.113.055236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Butkiewicz M., Restrepo N.A., Haines J.L., Crawford D.C. Drug-drug interaction profiles of medication regimens extracted from a de-identified electronic medical records system. AMIA Jt Summits Transl. Sci. Proc. 2016;2016:33–40. https://www.ncbi.nlm.nih.gov/pubmed/27570646 Retrieved from. [PMC free article] [PubMed] [Google Scholar]
  4. Castaldelli-Maia J.M., Hofmann C., Chagas A.C.P., Liprandi A.S., Alcocer A., Andrade L.H., Wielgosz A. Major cardiac-psychiatric drug-drug interactions: a systematic review of the consistency of drug databases. Cardiovasc. Drugs Ther. 2021;35(3):441–454. doi: 10.1007/s10557-020-06979-x. [DOI] [PubMed] [Google Scholar]
  5. Chen P. Optimized treatment strategy for depressive disorder. Adv. Exp. Med. Biol. 2019;1180:201–217. doi: 10.1007/978-981-32-9271-0_11. [DOI] [PubMed] [Google Scholar]
  6. Das, B., Ramasubbu, S. K., Agnihotri, A., Kumar, B., & Rawat, V. S. (2021). Leading 20 drug-drug interactions, polypharmacy, and analysis of the nature of risk factors due to QT interval prolonging drug use and potentially inappropriate psychotropic use in elderly psychiatry outpatients. Ther Adv Cardiovasc Dis, 15, 17539447211058892. doi:10.1177/17539447211058892. [DOI] [PMC free article] [PubMed]
  7. Das B., Rawat V.S., Ramasubbu S.K., Kumar B. Frequency, characteristics and nature of risk factors associated with use of QT interval prolonging medications and related drug-drug interactions in a cohort of psychiatry patients. Therapie. 2019;74(6):599–609. doi: 10.1016/j.therap.2019.03.008. [DOI] [PubMed] [Google Scholar]
  8. Day R.O., Snowden L., McLachlan A.J. Life-threatening drug interactions: what the physician needs to know. Intern. Med. J. 2017;47(5):501–512. doi: 10.1111/imj.13404. [DOI] [PubMed] [Google Scholar]
  9. Dechanont S., Maphanta S., Butthum B., Kongkaew C. Hospital admissions/visits associated with drug-drug interactions: a systematic review and meta-analysis. Pharmacoepidemiol. Drug Saf. 2014;23(5):489–497. doi: 10.1002/pds.3592. [DOI] [PubMed] [Google Scholar]
  10. Ereshefsky L., Jhee S., Grothe D. Antidepressant drug-drug interaction profile update. Drugs R D. 2005;6(6):323–336. doi: 10.2165/00126839-200506060-00002. [DOI] [PubMed] [Google Scholar]
  11. Goldberg D., Pilling S., Andrews N., Bird V., Creed F., Dowrick C., Hindle J. Depression in adults with a chronic physical health problem: recognition and management. NICE Clin. Guideline. 2009 Retrieved from London: (accessed. [Google Scholar]
  12. Guideline Development Panel for the Treatment of Depressive Disorders. (2021). Summary of the clinical practice guideline for the treatment of depression across three age cohorts. Am Psychol. doi:10.1037/amp0000904 [DOI] [PubMed]
  13. Hecht T., Bundscherer A.C., Lassen C.L., Lindenberg N., Graf B.M., Ittner K.P., Wiese C.H. The expenditure of computer-related worktime using clinical decision support systems in chronic pain therapy. BMC Anesthesiol. 2015;15:113. doi: 10.1186/s12871-015-0094-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hefner G., Hahn M., Hiemke C., Toto S., Wolff J., Roll S.C., Klimke A. Pharmacodynamic Drug-Drug interactions of QT-prolonging drugs in hospitalized psychiatric patients. J. Neural Transm. (Vienna) 2021;128(2):243–252. doi: 10.1007/s00702-020-02291-y. [DOI] [PubMed] [Google Scholar]
  15. Hines L.E., Malone D.C., Murphy J.E. Recommendations for generating, evaluating, and implementing drug-drug interaction evidence. Pharmacotherapy. 2012;32(4):304–313. doi: 10.1002/j.1875-9114.2012.01024.x. [DOI] [PubMed] [Google Scholar]
  16. Huang, S. M., Strong, J. M., Zhang, L., Reynolds, K. S., Nallani, S., Temple, R., . . . Lesko, L. J. (2008). New era in drug interaction evaluation: US Food and Drug Administration update on CYP enzymes, transporters, and the guidance process. J Clin Pharmacol, 48(6), 662-670. doi:10.1177/0091270007312153. [DOI] [PubMed]
  17. Ismail M., Noor S., Harram U., Haq I., Haider I., Khadim F., Khan Q., Ali Z., Muhammad T., Asif M. Potential drug-drug interactions in outpatient department of a tertiary care hospital in Pakistan: a cross-sectional study. BMC Health Serv. Res. 2018;18(1) doi: 10.1186/s12913-018-3579-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Jankovic S.M., Pejcic A.V., Milosavljevic M.N., Opancina V.D., Pesic N.V., Nedeljkovic T.T., Babic G.M. Risk factors for potential drug-drug interactions in intensive care unit patients. J. Crit. Care. 2018;43:1–6. doi: 10.1016/j.jcrc.2017.08.021. [DOI] [PubMed] [Google Scholar]
  19. Jazbar J., Locatelli I., Horvat N., Kos M. Clinically relevant potential drug-drug interactions among outpatients: a nationwide database study. Res. Social Adm. Pharm. 2018;14(6):572–580. doi: 10.1016/j.sapharm.2017.07.004. [DOI] [PubMed] [Google Scholar]
  20. Johnell K., Klarin I. The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register. Drug Saf. 2007;30(10):911–918. doi: 10.2165/00002018-200730100-00009. [DOI] [PubMed] [Google Scholar]
  21. Kessler R.C., Birnbaum H.G., Shahly V., Bromet E., Hwang I., McLaughlin K.A., Sampson N., Andrade L.H., de Girolamo G., Demyttenaere K., Haro J.M., Karam A.N., Kostyuchenko S., Kovess V., Lara C., Levinson D., Matschinger H., Nakane Y., Browne M.O., Ormel J., Posada-Villa J., Sagar R., Stein D.J. Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO World Mental Health Survey Initiative. Depress Anxiety. 2010;27(4):351–364. doi: 10.1002/da.20634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Khan Q., Ismail M., Haider I., Ali Z. Prevalence of the risk factors for QT prolongation and associated drug-drug interactions in a cohort of medical inpatients. J. Formos Med. Assoc. 2019;118(1 Pt 1):109–115. doi: 10.1016/j.jfma.2018.01.016. [DOI] [PubMed] [Google Scholar]
  23. Kok R.M., Reynolds C.F., 3rd. Management of depression in older adults: a review. JAMA. 2017;317(20):2114–2122. doi: 10.1001/jama.2017.5706. [DOI] [PubMed] [Google Scholar]
  24. Lai L.L., Alvarez G., Dang L., Vuong D., Ngo V., Jo Y., Koh L. Prevalence and trend of potential drug–drug interaction among children with depression in U.S. outpatient settings. J. Pharma. Health Services Res. 2019;10(4):393–399. doi: 10.1111/jphs.12320. [DOI] [Google Scholar]
  25. Leone R., Magro L., Moretti U., Cutroneo P., Moschini M., Motola D., Tuccori M., Conforti A. Identifying adverse drug reactions associated with drug-drug interactions: data mining of a spontaneous reporting database in Italy. Drug Saf. 2010;33(8):667–675. doi: 10.2165/11534400-000000000-00000. [DOI] [PubMed] [Google Scholar]
  26. Liu X., Hatton R.C., Zhu Y., Hincapie-Castillo J.M., Bussing R., Barnicoat M., Winterstein A.G. Consistency of psychotropic drug–drug interactions listed in drug monographs. J. Am. Pharm. Assoc. 2017;57(6):698–703.e2. doi: 10.1016/j.japh.2017.07.008. [DOI] [PubMed] [Google Scholar]
  27. Low Y., Setia S., Lima G. Drug-drug interactions involving antidepressants: focus on desvenlafaxine. Neuropsychiatr. Dis. Treat. 2018;14:567–580. doi: 10.2147/NDT.S157708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Marcath L.A., Xi J., Hoylman E.K., Kidwell K.M., Kraft S.L., Hertz D.L. Comparison of nine tools for screening Drug-Drug interactions of oral oncolytics. J. Oncol. Pract. 2018;14(6):e368–e374. doi: 10.1200/jop.18.00086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Marengoni, A., Onder, G. (2015). Guidelines, polypharmacy, and drug-drug interactions in patients with multimorbidity. Bmj, 350, h1059. doi:10.1136/bmj.h1059. [DOI] [PubMed]
  30. 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(3):371–375. doi: 10.5530/jyp.2017.9.74. [DOI] [Google Scholar]
  31. Morales-Ríos O., Jasso-Gutiérrez L., Reyes-López A., Garduño-Espinosa J., Muñoz-Hernández O., Eberini I. Potential drug-drug interactions and their risk factors in pediatric patients admitted to the emergency department of a tertiary care hospital in Mexico. PLoS One. 2018;13(1):e0190882. doi: 10.1371/journal.pone.0190882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Nelson J.C., Papakostas G.I. Atypical antipsychotic augmentation in major depressive disorder: a meta-analysis of placebo-controlled randomized trials. Am. J. Psychiatry. 2009;166(9):980–991. doi: 10.1176/appi.ajp.2009.09030312. [DOI] [PubMed] [Google Scholar]
  33. Nguyen T., Liu X., Abuhashem W., Bussing R., Winterstein A.G. Quality of evidence supporting major psychotropic drug-drug interaction warnings: a systematic literature review. Pharmacotherapy. 2020;40(5):455–468. doi: 10.1002/phar.2382. [DOI] [PubMed] [Google Scholar]
  34. Nusair M.B., Al-Azzam S.I., Arabyat R.M., Amawi H.A., Alzoubi K.H., Rabah A.A. The prevalence and severity of potential drug-drug interactions among adult polypharmacy patients at outpatient clinics in Jordan. Saudi Pharm. J. 2020;28(2):155–160. doi: 10.1016/j.jsps.2019.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Offerhaus L. 2nd ed. World Health Organization. Regional Office for Europe; Copenhagen: 1997. Drugs for the elderly. [Google Scholar]
  36. Parker G., Brotchie H. Gender differences in depression. Int. Rev. Psychiatry. 2010;22(5):429–436. doi: 10.3109/09540261.2010.492391. [DOI] [PubMed] [Google Scholar]
  37. Pirnejad H., Amiri P., Niazkhani Z., Shiva A., Makhdoomi K., Abkhiz S., van der Sijs H., Bal R. Preventing potential drug-drug interactions through alerting decision support systems: a clinical context based methodology. Int. J. Med. Inform. 2019;127:18–26. doi: 10.1016/j.ijmedinf.2019.04.006. [DOI] [PubMed] [Google Scholar]
  38. Ramos-Esquivel A., Víquez-Jaikel Á., Fernández C. Potential Drug-Drug and herb-drug interactions in patients with cancer: a prospective study of medication surveillance. J. Oncol. Pract. 2017;13(7):e613–e622. doi: 10.1200/jop.2017.020859. [DOI] [PubMed] [Google Scholar]
  39. Rekić D., Reynolds K.S., Zhao P., Zhang L., Yoshida K., Sachar M., Piquette Miller M., Huang S.-M., Zineh I. Clinical Drug-Drug interaction evaluations to inform drug use and enable drug access. J. Pharm. Sci. 2017;106(9):2214–2218. doi: 10.1016/j.xphs.2017.04.016. [DOI] [PubMed] [Google Scholar]
  40. Ren W., Liu Y., Zhang J., Fang Z., Fang H., Gong Y., Lv X. Prevalence of potential drug-drug interactions in outpatients of a general hospital in China: a retrospective investigation. Int. J. Clin. Pharm. 2020;42(4):1190–1196. doi: 10.1007/s11096-020-01068-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Salk R.H., Hyde J.S., Abramson L.Y. Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol. Bull. 2017;143(8):783–822. doi: 10.1037/bul0000102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sandson N.B., Armstrong S.C., Cozza K.L. An overview of psychotropic drug-drug interactions. Psychosomatics. 2005;46(5):464–494. doi: 10.1176/appi.psy.46.5.464. [DOI] [PubMed] [Google Scholar]
  43. Song Y.K., Oh J.M. Nationwide prevalence of potential drug-drug interactions associated with non-anticancer agents in patients on oral anticancer agents in South Korea. Support Care Cancer. 2020;28(8):3711–3720. doi: 10.1007/s00520-019-05221-1. [DOI] [PubMed] [Google Scholar]
  44. Tesfaye Z.T., Nedi T. Potential drug-drug interactions in inpatients treated at the Internal Medicine ward of Tikur Anbessa Specialized Hospital. Drug Healthc. Patient Saf. 2017;9:71–76. doi: 10.2147/DHPS.S126336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Volpi-Abadie J., Kaye A.M., Kaye A.D. Serotonin syndrome. Ochsner. J. 2013;13(4):533–540. [PMC free article] [PubMed] [Google Scholar]
  46. Wang Z., Ma X., Xiao C. Standardized treatment strategy for depressive disorder. Adv. Exp. Med. Biol. 2019;1180:193–199. doi: 10.1007/978-981-32-9271-0_10. [DOI] [PubMed] [Google Scholar]
  47. Wolff J., Hefner G., Normann C., Kaier K., Binder H., Hiemke C., Toto S., Domschke K., Marschollek M., Klimke A. Polypharmacy and the risk of drug-drug interactions and potentially inappropriate medications in hospital psychiatry. Pharmacoepidemiol. Drug Saf. 2021;30(9):1258–1268. doi: 10.1002/pds.5310. [DOI] [PubMed] [Google Scholar]
  48. Wolff J., Reißner P., Hefner G., Normann C., Kaier K., Binder H., Hiemke C., Toto S., Domschke K., Marschollek M., Klimke A., Lupattelli A. Pharmacotherapy, drug-drug interactions and potentially inappropriate medication in depressive disorders. PLoS One. 2021;16(7):e0255192. doi: 10.1371/journal.pone.0255192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. World Health Organization. (2017). Depression and other common mental disorders: global health estimates. Retrieved from Geneva: https://apps.who.int/iris/handle/10665/254610 (accessed 22 May 2022).
  50. World Health Organization. Regional Office for the Eastern, M. (2019). Depression. Retrieved from Cairo: https://apps.who.int/iris/handle/10665/333464 (accessed 22 May 2022).
  51. Yalcin N., Ozdemir N., Cak Esen H.T., Cengel Kultur S.E., Demirkan K. Potential drug-drug interactions with psychotropic drugs in paediatric inpatients: a cross-sectional study. Int. J. Clin. Pract. 2021;75(6):e14107. doi: 10.1111/ijcp.14107. [DOI] [PubMed] [Google Scholar]
  52. Zhdanava M., Karkare S., Pilon D., Joshi K., Rossi C., Morrison L., Sheehan J., Lefebvre P., Lopena O., Citrome L. Prevalence of pre-existing conditions relevant for adverse events and potential drug-drug interactions associated with augmentation therapies among patients with treatment-resistant depression. Adv. Ther. 2021;38(9):4900–4916. doi: 10.1007/s12325-021-01862-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Saudi Pharmaceutical Journal : SPJ are provided here courtesy of Springer

RESOURCES