PREVALENCE AND CLINICAL IMPLICATIONS OF DRUG-DRUG INTERACTIONS IN PSYCHIATRIC PATIENTS ATTENDING FEDERAL NEURO PSYCHIATRIC HOSPITAL, MAIDUGURI

224 Nigerian Journal of Pharmaceutical and Biomedical Research Vol. 8 Issue.3 December, 2024. p-ISSN: 2579-1419 e-ISSN: 2814-1423 PREVALENCE AND CLINICAL IMPLICATIONS OF DRUG-DRUG INTERACTIONS IN PSYCHIATRIC PATIENTS ATTENDING FEDERAL NEURO PSYCHIATRIC HOSPITAL, MAIDUGURI OGENYI M1*, YUSUF H 1, OHIEKU JD 1, WESLEY E2 1Department of Clinical Pharmacy and Pharmacy Administration, University of Maiduguri, Maiduguri, Nigeria. 2Department of Clinical Pharmacy and Pharmacy practice, Gombe State University, Gombe State, Nigeria. Correspondence Author Email: [email protected] Phone: 08067719033 http://doi.org/10.55639/607.phar.10401.004 ABSTRACT Drug-drug interactions (DDIs) significantly impact patient safety in psychiatric care due to polypharmacy and comorbidities. It has been reported that 20 − 30% of all adverse reactions to drugs are caused by drug-drug interactions, which can be prevented through appropriate monitoring and follow up. This study investigated the prevalence of DDIs among patients at the Federal Psychiatric Hospital, Maiduguri, Nigeria, using six online DDI checkers (Medscape, Epocrates, Lexi-Drug, Drug Bank, Drugs.com, and WEBMD). 541 prescriptions were analysed with a high prevalence of major DDIs (38.1%). The findings suggest a strong association between polypharmacy, comorbidities, and DDI risk. This study emphasizes the need for comprehensive DDI management strategies, including routine monitoring and the use of multiple DDI software programs to enhance patient safety. Continuous training for healthcare providers, accurate medication histories, and multicenter studies to validate these findings and explore DDIs in other settings are also recommended. By incorporating DDI checkers and mitigating DDI risks, healthcare professionals can improve patient safety and treatment outcomes. Keywords: Drug-drug interactions (DDIs), psychiatric patients, polypharmacy, comorbidities, psychiatric care, Nigeria. Introduction A drug-drug interaction can be defined as the pharmacological response to the administration or co-exposure of one drug with another drug that modifies the response of patients to the drug effect (Malone et al., 2015). The consequences of clinically significant pDDIs have a negative impact on the morbidity, mortality, duration of hospitalization, quality of life, and healthcare costs of the patients (Guthrie et al., 2015). It has been reported that 20 − 30% of all adverse reactions to drugs are caused by drug-drug interactions, which can be prevented through appropriate monitoring and follow up. But this incidence increases among the elderly and patients who take two or more medications (Kannan et al., 2013). In inpatients, the risk of having potentially interacting drug combinations can additionally increase because new drugs are often added to the existing drug therapy (Heininger-Rothbucher et al. 225 2012). DDIs are a concern for patients and providers, as polypharmacy is becoming more common in managing complex diseases or comorbidities and the consequences can range from untoward effects to drug-related morbidity and mortality (Balidemaj et al., 2021). Prescription drug interactions are a significant concern in psychiatric practice due to the complex pharmacotherapy often required for patients with mental health disorders. The concurrent use of multiple medications, including antipsychotics, mood stabilizers, antidepressants, and anxiolytics, increases the risk of potential drug interactions, which can lead to adverse effects, reduced therapeutic efficacy, or compromised patient safety (Reutfors et al., 2016). Psychiatric patients often present with complex clinical profiles, requiring polypharmacy to manage their symptoms effectively. However, this complexity increases the risk of drug interactions, as multiple medications may interact with each other pharmacokinetically or pharmacodynamically (Tamburello et al., 2020). Drug interactions in psychiatric patients can have profound effects on patient safety and treatment outcomes. Adverse interactions may lead to exacerbation of psychiatric symptoms, increased hospitalizations, or the need for additional interventions to manage side effects (Hefner et al., 2018). By identifying and mitigating potential interactions, healthcare providers can improve patient care and reduce the risk of harm. Online drug interaction checkers are valuable tools for healthcare professionals to screen for potential interactions between medications. These tools utilize extensive databases of drug-drug interactions and provide rapid assessments of the compatibility of prescribed medications (Palleria et al., 2013). Incorporating multiple online checkers in a prescription analysis study can enhance the comprehensiveness and accuracy of the assessment. The findings of a prescription analysis study can inform quality improvement initiatives within psychiatric hospitals. By identifying common patterns of drug interactions and associated risk factors, healthcare institutions can develop targeted interventions, such as educational programs for prescribers or updates to clinical guidelines, to improve the safety and effectiveness of medication management (Lombardi et al., 2019). The primary objectives of this study were to determine the prevalence of DDIs in psychiatric patients at FPNH Maiduguri and to explore the clinical implications of these interactions. The findings aim to inform clinical practice and provide recommendations for improving patient safety and treatment efficacy. Methodology Study Setting and Study Design This cross-sectional study was conducted at the Federal Neuro-Psychiatric Hospital (FNPH), which is in Maiduguri, Borno State. It is a regional Psychiatric Hospital, which serves the northeast region of Nigeria and receives influx of patients from the neighbouring countries of Chad, Cameroon, and Niger Republics. (Yusuf et al., 2018). Study Population and Sample Size Estimation 226 Sample size was determined using Fisher’s formula n= Z2 P (1-P) d2 Where: n is the sample size Z is the standard normal value at confidence interval of 95% = 1.96, P is proportion taken at 0.5, and d is precision or margin of sampling error tolerated (0.05). n = (1.96)2 x (0.5) x (1-0.5) = 3.8416 x 0.5 x 0.5 = 0.9604 = 384.16 (0.05)2 0.0025 0.0025 After obtaining the estimated minimum sample size, more folders of eligible patients that were readily available during the time of the data collection were added to the study population until a total number of 541 folders were reviewed. Ethical Approval The proposal for this research was submitted to the Hospital Research and Ethics Committee and ethical approval with reference number: FNPH/062023/REC138 was granted before the commencement of the study. Information about the clients was kept strictly confidential and were not disclosed to any third party. The confidentiality of this information was considered the exclusive right of the clients, and as such, it was handled with the utmost trust and discretion. The data collected were exclusively used for research purposes. Data Collection and Sampling Technique The current prescriptions of 541 patients were extracted from their medical folders (every clinic day for a period of 3 months) and analyzed for potential drug-drug interaction using 6 different online drug interaction checkers —Medscape, Drug bank, Epocrates, Lexi drug, Drugs.com, and WEBMD. A data collection form was used to extract the sociodemographic data, clinical information and current medications prescribed from each folder before screening with the six different DDI checkers. Convenience sampling was used in folder selection and only the folders of adult patients who were prescribed more than one medication were included in the study. Results obtained from all six checkers were recorded and analysed for level of severity to select those with major DDI from the total number of potential DDIs that were flagged. Data Analysis The prevalence of DDIs was calculated based on the number of prescriptions containing major interactions. Data were analysed using Statistical Products for Service Solutions (SPSS) version 25.0; SPSS Inc., Chicago, IL, USA (Saleh, 2023) after checking for errors and coding to numerical values. Mann-Whitney U-test was used (for variables of two categories) while Krustal-Wallis test was used (for variables with more than two categories) to 227 determine the difference in occurrence of major DDI with each patient characteristic parameter. A P-value < 0.05 was considered statistically significant (95% Confidence Interval). Univariate analysis was conducted to see the effect of independent variables on the outcome variable. And variables which showed P value less than 0.2 were considered for multivariate analysis. Results The characteristics of the patients that made up this study population is presented in Table 1. Majority of the patients were: aged 20-34 (55.27%), females (50.28%), married (53.05%), had no formal education (61.92%), unemployed (52.68%) and had no other comorbidity (69.32%). Patients’ Median age was 30 years. The clinical assessment of the patients is shown in Table 2. Patients with Schizophrenia spectrum disorders comprised 24.58% of the total study population. This is followed by patients with depressive disorders (20.70%). According to the result shown in Table 3, from the 541 prescriptions reviewed, 206 (38.07%) drug interactions were identified as clinically relevant drugdrug interactions. Among these, 95 (46.12%) were detected by DrugBank, while Epocrates, Medscape and WebMD identified 7 (3.40%) each and Lexi-Drug identified 5 (2.43%) respectively. The factors associated with major drug-druginteraction are presented in Table 4. MannWhitney U-test was used (for variables of two categories) while Krustal-Wallis test was used (for variables with more than two categories) to determine the difference in major DDI based on each patient characteristic parameter. A P-value < 0.05 was considered statistically significant (95% Confidence Interval). Comorbidity was significantly associated with major drug-drug interaction and the number of drugs prescribed was also found to be significantly associated with major drugdrug interaction (with those having more than 3 drugs showing a much higher level of association). The predictors for the presence of major DDIs in each prescription are depicted in Table 5. In the multivariate model, Patients with comorbidities, such as cardiovascular diseases or diabetes, exhibited a higher prevalence of major DDIs compared to those without comorbidities. The adjusted odds ratio for DDI occurrence in patients with comorbidities was 2.12 (95% CI, 1.44–3.14). Similarly, the adjusted odds ratio for DDI occurrence in patients with polypharmacy was 1.84 (95% CI, 1.13– 2.99), indicating a strong association between polypharmacy and DDI risk (as those with a prescription having more than three medications were 1.8 times more likely to have a major DDI compared to those with fewer medications prescribed). Discussion Patient Characteristics The demographic and social characteristics of the study population provide critical context for interpreting the results. Most of the patients were aged 20-34 years, with a median age of 30 years, suggesting that young adults constitute a significant proportion of the population requiring psychiatric care. This finding aligns with global trends where mental health disorders often manifest prominently in early adulthood. Furthermore, the predominance of females (50.28%) and married individuals (53.05%) underscores the need for gender-sensitive and family-inclusive approaches in mental health interventions. 228 A notable proportion of the participants (61.92%) had no formal education, and over half (52.68%) were unemployed. These socio-economic factors likely exacerbate the burden of mental health disorders and present additional barriers to accessing quality care. Addressing these systemic inequities is critical for improving health outcomes within this demographic. Clinical Assessments The clinical distribution of psychiatric disorders revealed that schizophrenia spectrum disorders (24.58%) and depressive disorders (20.70%) were the most prevalent diagnoses. These findings are consistent with existing literature highlighting the significant global burden of these conditions. Schizophrenia spectrum disorders often require long-term pharmacological management, which may increase the risk of drug-drug interactions (DDIs). Similarly, depressive disorders frequently involve polypharmacy, further emphasizing the importance of careful prescription monitoring. Clinically Relevant Drug-Drug Interactions Among the 541 prescriptions reviewed, 38.07% contained clinically relevant DDIs. This relatively high prevalence underscores the importance of vigilant pharmacovigilance in psychiatric care settings. The substantial contribution of Drug Bank (46.12%) in detecting clinically relevant DDIs compared to Epocrates, Medscape, WebMD, and Lexi-Drug highlights variability in the sensitivity of DDI detection tools. This variability indicates a potential need for standardizing DDI identification protocols in clinical practice to enhance patient safety. Factors Associated with Major DDIs The Mann-Whitney U-test and KruskalWallis test provided robust statistical analyses of the factors influencing major DDIs. Comorbidity emerged as a significant factor, with patients having additional medical conditions such as cardiovascular diseases or diabetes being more likely to experience major DDIs. This finding is supported by the adjusted odds ratio of 2.12, indicating that comorbidities more than double the risk of DDIs. These results highlight the importance of integrating multidisciplinary care approaches to mitigate risks in patients with complex medical histories. Polypharmacy was another critical factor associated with increased DDI risk. Patients prescribed more than three medications were 1.84 times more likely to encounter major DDIs. This finding underscores the need for prescribing practices that prioritize medication optimization and deprescribing where possible. Implementing clinical decision support systems and regular medication reviews could significantly reduce the prevalence of DDIs in this highrisk group. While this study provides valuable insights, some limitations warrant consideration. The reliance on specific DDI detection tools may have led to underestimation or overestimation of clinically relevant DDIs. Additionally, the cross-sectional nature of the study limits the ability to establish causality. Longitudinal studies are therefore recommended for further insight regarding DDI prevalence at FNPH Maiduguri. This study has demonstrated that drug-drug interactions are prevalent in psychiatric patients at FPNH Maiduguri, with polypharmacy and comorbidities serving as 229 significant risk factors. 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NigJournPharm Bio Res 3(2):132136 231 Table 1: Characteristics of patients attending FNPH Maiduguri Variable Frequency Percent Age Group < 20 40 7.39 20 - 34 299 55.27 35 - 44 92 17.01 > 45 110 20.33 Total Gender 541 100 Male 269 49.72 Female Total 272 541 50.28 100 Marital Status Single 195 36.05 Married 287 53.05 Widowed 29 5.36 Divorced Total 30 541 5.54 100 Highest Qualification None 335 61.92 FSL 26 4.80 SSCE 118 21.81 BSc/Diploma 59 10.90 MSc Total 3 541 0.57 100 Employment Unemployed 285 52.68 Employed 247 45.65 Retired Total 9 541 1.67 100 Comorbidity Yes 166 30.68 No Total 375 541 69.32 100 Table 2: Clinical Assessment of Patients attending FNPH Maiduguri Diagnosis Frequency Percent Schizophrenia spectrum disorders 133 24.58 Seizure disorders 81 14.97 Bipolar related disorders 62 11.46 Depressive illness 112 20.70 Mental and behavioral disorder secondary to substance(s) use 54 9.98 Headaches 20 3.69 Organic mental illness 20 3.69 Cognitive disorders 16 2.95 Anxiety disorders 5 0.94 Others Total 38 541 7.04 100 232 N = 541 Table 4: Factors Associated with major drug-drug interaction in patients attending FNPH Maiduguri Variable Mean rank P value µ Age Group < 20 357.23 0.34 20 - < 35 349.56 35 - < 45 322.18 > 45 345.60 Ω Gender Male 365.14 0.002* Female 330.0 Ω Marital Status Single 343.81 0.69 Married 346.02 Widowed 376.92 Divorced 344.24 Ω Highest Qualification None 359.51 0.02* FSL 349.66 SSCE 326.92 BSc/Diploma/ MSc 313.03 µ Employment Unemployed 352.12 0.23 Employed 337.88 µ Comorbidity Yes 389.48 <0.001* No 332.94 µ Number of prescribed medications ≤ 3 340.32 0.006* > 3 387.32 µ: Mann-Whitney test Ω: Kruskal-Wallis test *significant at p<0.05 Table 3: Major DDIs detected from prescriptions of patients attending FNPH Maiduguri DDI Checker Major DDI Detected Percentage (%) Medscape 7 3.39 Drugbank 95 46 Epocrates 7 3.39 Lexicomp 5 2.43 Drugs,com Web MD Total 85 7 206 41.26 3.39 100