Are there any potential drug-drug interactions with oral inhaler medications?: A retrospective study.

Background: Oral inhaler medications (OIMs) are widely used for many respiratory diseases. Although OIMs have minimal systemic effects, they may cause potential drug-drug interactions (pDDIs).Objectives: This study aims to evaluate drug interactions in patients using OIMs. Methods: This retrospective, and descriptive study was conducted in a community pharmacy in Istanbul (Turkey) between January 1, andMay 312,021. Prescriptions of all asthma and COPD patients aged 18 and over on the specified date were included in the study. Data were collected from the pharmacy information system. Sociodemograhic characteristics were recorded. pDDIs were analyzed via Medscape and Lexicomp drug interaction checker databases. Significant (monitor closely), Serious (use alternative), Contraindicated categories in the Medscape database and D (consider treatment modification) and X (avoid combination) categories in the Lexi-Interact™ database were evaluated as pDDIs. SPSS analysis was performed. Results: A total of 54 asthma and 42 chronic obstructive pulmonary disease (COPD) patients were included in the study. Most asthma (76%) and COPD (83%) patients were found to have at least one comorbid disease. A total of81 pDDIs were identified in the Medscape database in asthma patients, and 86.5% of them were classified as "monitor closely". A total of 12 drug interactions were detected in the Lexicomp database, with 75% of them were "D" category for asthma patients. In the prescriptions of COPD patients, a total of 162 drug interactions were determined via the Medscape database, with 94.4% classified as "monitor closely". A total of 13 drug interactions were detected in the Lexicomp database, with 61.5% of them falling into the "X" category for COPD patients. Conclusions: According to the results of this study COPD patients who may be at a high risk of experiencing pDDIs. Healthcare providers should consider the individual patient's clinical profile, including comorbidities and medication regimen, to minimize the risk of pDDIs and optimize treatment outcomes. Further research is needed to elucidate the mechanisms underlying these findings and develop tailored strategies to diminish the risks associated with pDDIs in respiratory disease management.

Incidence and risk factors for potential drug-drug interactions in outpatients receiving opioid analgesics.

BACKGROUND: Opioids are the most frequently used drugs to treat pain in cancer patients. However opioid analgesics can cause adverse effects and potential drug-drug interaction. RESEARCH DESIGN AND METHODS: This cross-sectional retrospective study analyzed pDDI in 1839 patients with opioid analgesics in a large comprehensive hospital in China from January 1 to 31 December 2022. Three drug interaction databases were used to screen for pDDI including Drugs (U.S.A.), Medscape (U.S.A.), and Drug Assistant of Dingxiangyuan (China). RESULTS: The prevalence of pDDIs among 1839 patients was around 41.27% of 759 patients, and 564 patients (74.31%) with pDDIs were diagnosed with tumor. Further, the total of 275 various pDDIs combinations were identified. The combination of oxycodone with morphine had the most frequent occurrence of 229 times, and its adverse effects mainly related to exacerbate central respiratory depression. While, gender, tumor, number of diagnoses, and the variety of opioid analgesics used were independent risk factors for pDDIs. CONCLUSIONS: Outpatients taking opioid analgesics had a higher incidence of pDDIs. As consequently, optimized monitoring and management of patients taking opioid analgesics is recommended in order to ensure patient medication safety.

Drug-Drug Interactions in Patients with Acute Respiratory Distress Syndrome.

Acute respiratory distress syndrome (ARDS) is a potential life-threatening, heterogenous, inflammatory lung disease. There are no data available on potential drug-drug interactions (pDDIs) in critically ill patients with ARDS. This study analyzed pDDIs in this specific cohort and aimed to investigate possible associations of coronavirus disease 2019 (COVID-19) as an underlying cause of ARDS and treatment with extracorporeal membrane oxygenation (ECMO) with the occurrence of pDDIs. This retrospective study included patients ≥18 years of age diagnosed with ARDS between January 2010 and September 2021. The Janusmed database was used for the identification of pDDIs. A total of 2694 pDDIs were identified in 189 patients with a median treatment duration of 22 days. These included 323 (12%) clinically relevant drug combinations that are best avoided, corresponding to a median rate of 0.05 per day. There was no difference in the number of pDDIs between COVID-19- and non-COVID-19-associated ARDS. In patients treated with ECMO, the rate of the most severely graded pDDIs per day was significantly higher compared with those who did not require ECMO. PDDIs occur frequently in patients with ARDS. On average, each patient may encounter at least one clinically relevant drug combination that should be avoided during their intensive care unit stay.

Prevalence and clinical significance of potential drug-drug interactions among lung transplant patients.

Background: Drug-drug interactions (DDIs) are a major but preventable cause of adverse drug reactions. There is insufficient information regarding DDIs in lung transplant recipients. Objective: This study aimed to determine the prevalence of potential DDIs (pDDIs) in intensive care unit (ICU) lung transplant recipients, identify the real DDIs and the most frequently implicated medications in this vulnerable population, and determine the risk factors associated with pDDIs. Methods: This retrospective cross-sectional study included lung transplant recipients from January 2018 to December 2021. Pertinent information was retrieved from medical records. All prescribed medications were screened for pDDIs using the Lexicomp® drug interaction software. According to this interaction software, pDDIs were classified as C, D, or X (C = monitor therapy, D = consider therapy modification, X = avoid combination). The Drug Interaction Probability Scale was used to determine the causation of DDIs. All statistical analysis was performed in SPSS version 26.0. Results: 114 patients were qualified for pDDI analysis, and total pDDIs were 4051. The most common type of pDDIs was category C (3323; 82.0%), followed by D (653; 16.1%) and X (75; 1.9%). Voriconazole and posaconazole were the antifungal medicine with the most genuine DDIs. Mean tacrolimus concentration/dose (Tac C/D) before or after co-therapy was considerably lower than the Tac C/D during voriconazole or posaconazole co-therapy (p < 0.001, p = 0.027). Real DDIs caused adverse drug events (ADEs) in 20 patients. Multivariable logistic regression analyses found the number of drugs per patient (OR, 1.095; 95% CI, 1.048-1.145; p < 0.001) and the Acute Physiology and Chronic Health Evaluation II (APACHE Ⅱ) score (OR, 1.097; 95% CI, 1.021-1.179; p = 0.012) as independent risk factors predicting category X pDDIs. Conclusion: This study revealed a high incidence of both potential and real DDIs in ICU lung transplant recipients. Immunosuppressive drugs administered with azole had a high risk of causing clinically significant interactions. The number of co-administered drugs and APACHE Ⅱ score were associated with an increased risk of category × drug interactions. Close monitoring of clinical and laboratory parameters is essential for ensuring successful lung transplantation and preventing adverse drug events associated with DDIs.

The Prevalence and Severity of Potential Drug-Drug Interactions in Internal Medicine Ward at Soba Teaching Hospital.

Background: Multiple drug therapies are commonly used to achieve a desired therapeutic goal, especially in hospitalized patients. However, drug-drug interactions might occur and threaten the patients' safety. Objective: This study aims to assess the prevalence and severity of potential drug-drug interactions (PDDIs) in the internal medicine ward at Soba Teaching Hospital. Methods: A retrospective cross-sectional hospital-based study was carried out in the internal medicine ward at Soba Teaching Hospital from June 2021 to December 2021. The data was collected from patients' medical records. PDDIs were identified using Lexicomp® drug interaction software. Results: A total of 377 patients were included in this study, and overall prevalence of PDDIs was 62.9%. We have identified 989 potential DDIs and 345 pairs of interacting drugs, the mean of the PDDIs per patient was 4.17 ± 4.079. Among 345 PDDIs most were of moderate interactions 70.1% (n=242) followed by Minor interactions 19.1% (n=66). The most common type of interaction was of category C representing 63.5% (n=219). A significant association was observed between the occurrence of PDDIs with patients' age, presence of chronic diseases, length of hospital stay, and number of medications received by the patients. Conclusion: Drug-drug interactions were highly prevalent in the internal medicine ward. Therefore, certain attempts are required to increase the awareness of the physicians about these interactions and minimize their occurrence.

Analysis of Prescribing Practices in the Dermatology Outpatient Department of a Tertiary Care Teaching Hospital.

Introduction The practice of appropriately prescribing and delivering pharmaceuticals to the right patient for the diagnosis, prevention, and treatment of diseases is referred to as "rational drug usage". Patients should receive pharmaceuticals that are appropriate for their clinical needs, given in doses that meet their needs, for long enough periods of time, and for the least amount of money possible. Minimizing drug therapy costs without sacrificing therapeutic effectiveness, avoiding unnecessary adverse medication reactions and drug-drug interactions, and improving therapeutic care while encouraging patient adherence are the main objectives of rational drug usage. The present study was planned to assess the current prescribing practices in the dermatology outpatient department of a tertiary care hospital. Materials and methods A prospective descriptive study was conducted in the department of dermatology at a tertiary care teaching hospital after receiving permission from the institutional ethics committee. The study was conducted from November 2022 to February 2023 and followed the WHO recommendation for sample size. A total of 617 prescriptions were analyzed thoroughly. Results Regarding the demographic profile of the 617 prescriptions, 299 were male and 318 were female. The patients had diverse diseases, with the most common being tinea infection (57 cases, 9%) and acne vulgaris (53 cases, 8.5%), followed by scabies (38 cases, 6%), urticaria, and eczema (30 cases, 5%). Twenty-six (4%) prescriptions were not written in capital letters, 86 (13%) prescriptions did not mention the route of drug administration, and the consultant's or physician's name and signature were missing in 13 (2%), and six (1%) prescriptions, respectively. None of the prescriptions were written using the generic names of the drugs. Polypharmacy was observed in 51 (8%) prescriptions. Moreover, potential drug-drug interactions were identified in 12 (1.9%) instances. The most prescribed drugs were antihistaminics, with 393 (23%) prescriptions. Antifungal drugs were the second most prescribed, with 291 (17%) prescriptions. Corticosteroids were also commonly prescribed, with 271 (16%) prescriptions. Antibiotics were prescribed in 168 (10%) cases; other drugs were prescribed in 597 (35%) cases, including retinoids, anti-scabies drugs, antileprotic drugs, moisturizers, sunscreens, etc. Conclusion The study highlighted the prescription errors in writing the drugs in capital letters, mentioning the dose, route, and frequency of drugs, etc. It provided insight into the common diseases in dermatology and routine prescribing patterns and addressed the frequency of polypharmacy and drug-drug interactions.

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

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.

Potential Drug-Drug Interactions among Patients with Schizophrenia Spectrum Disorders: Prevalence, Association with Risk Factors, and Replicate Analysis in 2021.

Background and Objectives: Patients with schizophrenia are often exposed to polypharmacotherapy, which may lead to drug-drug interactions. The aim of the study was to investigate the prevalence of potential drug-drug interactions (pDDIs) in hospitalized patients with schizophrenia spectrum disorders and to identify factors associated with pDDIs and manifested symptoms and signs. Materials and Methods: This cross-sectional observational study included 311 inpatients admitted to a psychiatric hospital. The LexiComp drug interaction program was used to identify pDDIs in 2014. Factors associated with the prevalence of pDDIs and factors related to clinically observed symptoms and signs were assessed using multivariable regression. In addition, replicate analysis of pDDI was performed using 2021 program updates. Results: The prevalence of pDDIs was 88.7%. Our study showed that more than half of the patients received at least one drug combination that should be avoided. The most common pDDIs involved combinations of two antipsychotics or combinations of antipsychotics and benzodiazepines, which can lead to cardio-respiratory depression, sedation, arrhythmias, anticholinergic effects, and neuroleptic malignant syndrome. The number of prescribed drugs was a risk factor for pDDIs (OR 2.85; 95% CI 1.84-5.73). All groups of clinically observed symptoms and signs were associated with the number of drugs. In addition, symptoms and signs characteristic of the nervous system and psychiatric disorders were associated with antipsychotic dosage (IRR 1.33; 95% CI 1.12-1.58), which could contribute to the development of extrapyramidal syndrome, insomnia, anxiety, agitation, and bipolar mania. The 2021 version of the drug interaction program showed a shift in drug interactions toward a lower risk rating, implying less severe patient management and possibly less alert fatigue. Conclusions: Patients with schizophrenia spectrum disorders are at high risk of developing drug-drug interactions. Optimization of drug therapy, patient monitoring, and use of drug interaction programs could help to prevent pDDIs and subsequent adverse drug events.

Associated factors of potential drug-drug interactions and drug-food interactions in patients with multiple sclerosis.

Background: Multiple sclerosis (MS) is the most common immune-mediated demyelinating disease in younger adults. Patients with MS (PwMS) are vulnerable to the presence of potential drug-drug interactions (pDDIs) and potential drug-food interactions (pDFIs) as they take numerous medications to treat MS, associated symptoms and comorbidities. Knowledge about pDDIs and pDFIs can increase treatment success and reduce side effects. Objective: We aimed at determining the frequency and severity of pDDIs and pDFIs in PwMS, with regard to polypharmacy. Methods: In the cross-sectional study, we analysed pDDIs and pDFIs of 627 PwMS aged ⩾18 years. Data collection was performed through patient record reviews, clinical examinations and structured patient interviews. pDDIs and pDFIs were identified using two DDI databases: Drugs.com Interactions Checker and Stockley's Interactions Checker. Results: We identified 2587 pDDIs (counted with repetitions). Of 627 PwMS, 408 (65.1%) had ⩾ 1 pDDI. Polypharmacy (concomitant use of ⩾ 5 drugs) was found for 334 patients (53.3%). Patients with polypharmacy (Pw/P) were found to have a 15-fold higher likelihood of having ⩾ 1 severe pDDI compared with patients without polypharmacy (Pw/oP) (OR: 14.920, p < 0.001). The most frequently recorded severe pDDI was between citalopram and fingolimod. Regarding pDFIs, ibuprofen and alcohol was the most frequent severe pDFI. Conclusion: Pw/P were particularly at risk of severe pDDIs. Age and educational level were found to be factors associated with the occurrence of pDDIs, independent of the number of medications taken. Screening for pDDIs/pDFIs should be routinely done by the clinical physician to increase drug safety and reduce side effects.

Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases.

Background: Patients with multiple sclerosis (MS) often undergo complex treatment regimens, resulting in an increased risk of polypharmacy and potential drug-drug interactions (pDDIs). Drug interaction databases are useful for identifying pDDIs to support safer medication use. Objective: To compare three different screening tools regarding the detection and classification of pDDIs in a cohort of MS patients. Furthermore, we aimed at ascertaining sociodemographic and clinical factors that are associated with the occurrence of severe pDDIs. Methods: The databases Stockley's, Drugs.com and MediQ were used to identify pDDIs by screening the medication schedules of 627 patients. We determined the overlap of the identified pDDIs and the level of agreement in pDDI severity ratings between the three databases. Logistic regression analyses were conducted to determine patient risk factors of having a severe pDDI. Results: The most different pDDIs were identified using MediQ (n = 1,161), followed by Drugs.com (n = 923) and Stockley's (n = 706). The proportion of pDDIs classified as severe was much higher for Stockley's (37.4%) than for Drugs.com (14.4%) and MediQ (0.9%). Overall, 1,684 different pDDIs were identified by at least one database, of which 318 pDDIs (18.9%) were detected with all three databases. Only 55 pDDIs (3.3%) have been reported with the same severity level across all databases. A total of 336 pDDIs were classified as severe (271 pDDIs by one database, 59 by two databases and 6 by three databases). Stockley's and Drugs.com revealed 47 and 23 severe pDDIs, respectively, that were not included in the other databases. At least one severe pDDI was found for 35.2% of the patients. The most common severe pDDI was the combination of acetylsalicylic acid with enoxaparin, and citalopram was the drug most frequently involved in different severe pDDIs. The strongest predictors of having a severe pDDI were a greater number of drugs taken, an older age, living alone, a higher number of comorbidities and a lower educational level. Conclusions: The information on pDDIs are heterogeneous between the databases examined. More than one resource should be used in clinical practice to evaluate pDDIs. Regular medication reviews and exchange of information between treating physicians can help avoid severe pDDIs.

Evaluation of Drug-Related Problems in Chronic Kidney Disease Patients.

Background Chronic kidney disease (CKD) is a challenging global health problem with increasing prevalence worldwide. Concurrence of CKD and comorbidities results in the use of multiple medications and exposing patients to polypharmacy. Polypharmacy in CKD is common across all the stages of the disease and leads to poor medication adherence, higher healthcare costs, and drug-related problems, such as drug-drug interactions (DDIs) and adverse drug reactions (ADRs). DDIs and ADRs in CKD patients may lower the quality of life, increase the length of hospital stay, and augment the risks of morbidity and mortality. Methodology This was a hospital-based, prospective, cross-sectional study conducted in a secondary care hospital. The study population comprised 130 adult CKD patients admitted to the nephrology department including those on maintenance hemodialysis. Study-related data were obtained from the electronic patient case records. Medications prescribed to the patients were analyzed for potential DDIs (pDDIs) using Portable Emergency and Primary Care Information Database (PEPID 12.1) drug interaction checker. All observed and reported suspected ADRs related to the prescribed drugs were evaluated for causality, severity, preventability, and predictability. Results Out of the 130 patients, majority were males (n = 71, 54.6%), in the age group of 61-70 years (n = 45, 34.6%), and belonged to CKD stage 5 (n = 105, 80.8%). The mean number of drugs prescribed was 11.1 ± 3.8 per patient. The prevalence of pDDIs was found to be 89.2%. Upon analysis by the PEPID database, 708 pDDIs with 215 different pairs of interacting drugs were identified. Polypharmacy (odds ratio (OR): 62.34, 95% confidence interval (CI): 7.97-487.64, p < 0.001) was identified as an independent predictor of the occurrence of pDDIs. Negative binomial regression analysis revealed that dyslipidemia (incidence rate ratio (IRR): 2.7, 95% CI 2.09-3.48, p < 0.001) and diabetes (IRR: 1.2, 95% CI 1.01-1.54, p = 0.040) increased the probability of occurrence of pDDI by 2.7 and 1.2 folds, respectively. Furthermore, the likelihood of pDDI increased with every one-day increase in the length of hospital stay (IRR: 1.02, 95% CI 1.00-1.03, p = 0.015) by 1.02 times and polypharmacy (IRR: 6.30, 95% CI 3.04-13.02, p < 0.001) by 6.3 times. The incidence of ADRs was found to be 10.7%. Majority of suspected ADRs were possible (n = 7, 50.0%), of mild and moderate severity (n = 7, 50.0%), and non-preventable (n = 8, 57.1%) type. Conclusions This study investigated two important drug-related problems, pDDIs, and ADRs, in the CKD population. High proportion of CKD patients in the study had pDDIs. Comorbid conditions such as dyslipidemia and diabetes mellitus, length of hospital stay, and polypharmacy were significantly associated with increased likelihood of pDDIs. Furthermore, there was a burden of ADRs in the study population, of which most ADRs were possible and of mild to moderate severity. Prevention, identification, and resolution of these problems in CKD patients is important and can be achieved through medication optimization, which requires a proactive interdisciplinary collaboration between clinicians, clinical pharmacists, and other healthcare professionals.

Investigating Potential Drug-Drug Interactions from Greek e-Prescription Data.

BACKGROUND: The prevalence of potential drug-drug interactions (pDDIs) is indicative of the prevalence of actual drug-drug interactions and prescription quality. However, they are significantly understudied in Greece. OBJECTIVE: The objective of the study was to determine the prevalence of pDDIs among outpatients and identify factors associated with their occurrence. METHODS: Anonymous e-prescription data between 2012 and 2017 were obtained from community pharmacies in Thessaloniki, Greece. Patients taking more than one medication for at least three months were included. pDDIs were identified and categorized depending on their clinical significance using Drug Interactions Checker. Crude and adjusted odds ratios (ORs) with accompanying 95% confidence intervals (CIs) of risk factors of pDDIs occurrence were identified using multivariable logistic regression. RESULTS: During the study period, 6,000 anonymous e-prescriptions (1,000 per year) satisfying the inclusion criteria were collected. The overall prevalence of major pDDIs was 17.4% (63.0% for moderate pDDIs). The most common major pDDIs were between amlodipine and simvastatin (22.8% of major interactions), followed by clopidogrel and omeprazole (6.4% of major interactions). Polypharmacy (≥5 concomitantly received medications) was associated with an increased risk of major pDDIs (adjusted OR, 5.72; 95% CI, 4.87-6.72); no associations were observed regarding age, sex, and number of prescribing physicians. CONCLUSION: The prevalence of pDDIs in this study was higher than previously reported in other European countries, with polypharmacy being a potential risk factor. Those results argue for a need for improvement in the area of prescribing in Greece.

Risk factors for potential drug-drug interactions in patients with myasthenia gravis.

OBJECTIVES: Our aim was to determine risk factors for and frequency of potential drug-drug interactions (pDDIs) among hospitalized patients with myasthenia gravis (MG). METHODS: This was a retrospective cross-sectional study of the-first time hospitalized MG patients or patients hospitalized because of the exacerbation of MG at the Neurology Clinic of the Clinical Center of Serbia, Belgrade. Medical records and discharge summaries of hospitalized MG patients over a 10-year period were reviewed. The pDDIs were identified by means of Micromedex, and multivariate regression methods were used to reveal potential predictors of number of pDDIs per patient. RESULTS: The study included 687 patients with MG. In total, 2041 pDDIs were detected in 608 (88.5%) patients. Among the discovered pDDIs, 329 different pDDIs were observed. The most frequent pDDIs were pyridostigmine-prednisone (487patients/70.9%) and aspirin-prednisone (90 patients/13.1%) classified as moderate, and enalapril-potassium chloride (71patients/10.3%) classified as major pDDI. Five drugs (aspirin, insulin, prednisone, cyclosporine, metformin) were responsible for 22.6% of different pDDIs. Dyspnea, generalized form of MG, diabetes mellitus, hypertension, total number of drugs-used, use of antiplatelets were identified as the relevant risk factors for total number of pDDIs (R2 = 0.626,F = 73.797, p < 0.001), while age of patients and history of cancer were inversely correlated with such an outcome. CONCLUSION: The frequency of the pDDIs in hospitalized MG patients is high, and adversely influenced by dyspnea, generalized MG, diabetes mellitus, hypertension, total number of drugs-used and use of antiplatelets.

Potential drug-drug interactions in patients with cardiovascular diseases: findings from a prospective observational study.

BACKGROUND: Patients with cardiovascular diseases (CVD) are at high risk of experiencing drug-drug interactions (DDIs). The objective of this study was to evaluate the frequency, level and risk factors associated with potential-DDIs (pDDIs) in hospitalized CVD patients at cardiology departments of two tertiary care hospitals in Quetta, Pakistan. METHODS: In the current prospective observational study, a total of 300 eligible CVD inpatients were evaluated for pDDIs using Lexicomp Interact®. The pDDIs were classified into class A (no known interaction); B (no action needed); C (monitor therapy: it is documented that the benefits of an interaction outweigh the risk, appropriately monitor therapy in order to avoid potential adverse outcomes); D (consider therapy modification: it is documented that proper actions must be taken to reduce the toxicity resulting from an interaction); X (avoid combination: the risk of an interaction outweighs the benefits and are usually contraindicated). Multivariate binary logistic regression analysis was used to find factors associated with the presence of Class-D and/or X pDDIs. A p-value < 0.05 was considered statistically significant. RESULTS: With a median of 8.50 pDDIs per patient, all patients (100%) had ≥ 1 pDDIs. Out of total 2787 pDDIs observed, 74.06% (n = 2064) were of moderate and (n = 483) 17.33% of major severity. Class C pDDIs were most common (n = 1971, 70.72%) followed by D (n = 582, 20.88%), B (n = 204, 7.32%) and X (n = 30, 1.08%). Suffering from cardiovascular diseases other than myocardial infarction (OR 0.053, p-value < 0.001) and receiving > 12 drugs (OR 4.187, p-value = 0.009) had statistical significant association with the presence of class D and/or X pDDIs. CONCLUSION: In the current study, pDDIs were highly prevalent. The inclusion of DDI screening tools, availability of clinical pharmacists and paying special attention to the high-risk patients may reduce the frequency of pDDIs at the study sites.

Drug safety in hospitalized patients with tuberculosis: Drug interactions and adverse drug effects.

BACKGROUND: Hospitalized patients with tuberculosis (TB) are prescribed with drugs having high risk of potential drug-drug interactions (pDDIs) and adverse drug effects (ADEs). OBJECTIVES: To explore the adverse effects of anti-tuberculosis (anti-TB) drugs and the prevalence and predictors of pDDIs in hospitalized patients with TB. METHODS: Clinical profiles of 436 TB patients were reviewed for adverse effects induced by anti-TB drugs and screened for pDDIs using Micromedex-DrugReax. Prevalence and severity levels of pDDIs were reported. Odds ratios for predictors were calculated using logistic regression analysis. RESULTS: Of total 436 patients, adverse effects of anti-TB drugs were found in 36%. ADEs were highly prevalent in patients with high doses of anti-TB drugs. Hepatotoxicity, neuropathy, insomnia, arthralgia, psychosis, hematological alterations, skin rashes, red color stool, diplopia, and photophobia were the identified ADEs. All drugs types- and anti-TB drugs-pDDIs were reported in 78.2% and 55.7%, respectively. Major-pDDIs of anti-TB drugs were identified in 55.5%. Total 1090 anti-TB drugs pDDIs were found, among them, 55.6% were of major- and 40.5% were of moderate-severity. Significant association was observed for the pDDIs with ≥7 prescribed medicines (P < 0.001). Potential adverse outcomes of the most frequent interactions were hepatotoxicity, decreased drug's effectiveness, QT-interval prolongation, nephrotoxicity, and gastrointestinal ulceration. CONCLUSIONS: Patients with TB present with a considerable number of clinically important pDDIs and ADEs (particularly hepatotoxicity). TB patients should be monitored for adverse effects of anti-TB drugs. Attention should be given to major-pDDIs. Patients more at risk to interactions should be identified and monitored for related adverse outcomes.

Potential drug-drug interactions associated with adverse clinical outcomes and abnormal laboratory findings in patients with malaria.

BACKGROUND: Hospitalized patients with malaria often present with comorbidities or associated complications for which a variety of drugs are prescribed. Multiple drug therapy often leads to drug-drug interactions (DDIs). Therefore, the current study investigated the prevalence, levels, risk factors, clinical relevance, and monitoring parameters/management guidelines of potential DDIs (pDDIs) among inpatients with malaria. METHODS: A retrospective cohort study was carried out at two tertiary care hospitals. A total of 398 patients' profiles were evaluated for pDDIs using the Micromedex Drug-Reax®. Odds ratios were calculated to identify the strength of association between presence of DDIs and potential risk factors via logistic regression analysis. Further, the clinical relevance of frequent pDDIs was investigated. RESULTS: Of 398 patients, pDDIs were observed in 37.2% patients, while major-pDDIs in 19.3% patients. A total of 325 interactions were found, of which 45.5% were of major- and 34.5% moderate-severity. Patients with the most common pDDIs were found with signs/symptoms and abnormalities in laboratory findings representing nephrotoxicity, hepatotoxicity, QT interval prolongation, and reduced therapeutic efficacy. The following drug pairs reported the highest frequency of adverse events associated with the interactions; calcium containing products-ceftriaxone, isoniazid-rifampin, pyrazinamide-rifampin, isoniazid-acetaminophen, and ciprofloxacin-metronidazole. The adverse events were more common in patients prescribed with the higher doses of interacting drugs. Multivariate regression analysis showed statistically significant association of pDDIs with 5-6 prescribed medicines (p = 0.01), > 6 prescribed medicines (p < 0.001), > 5 days of hospital stay (p = 0.03), and diabetes mellitus (p = 0.04). CONCLUSIONS: PDDIs are commonly observed in patients with malaria. Healthcare professional's knowledge about the most common pDDIs could help in preventing pDDIs and their associated negative effects. Pertinent clinical parameters, such as laboratory findings and signs/symptoms need to be checked, particularly in patients with polypharmacy, longer hospital stay, and diabetes mellitus.

Potential drug-drug interactions in COVID 19 patients in treatment with lopinavir/ritonavir.

OBJECTIVES: To determine the prevalence of potential interactions in COVID19 patients receiving lopinavir/ritonavir (LPV/r). The secondary objective was to develop recommendations and identify the risk factors associated with presenting potential interactions with LPV/r. SUBJECTS AND METHODS: Cross-sectional and multicenter study with the participation of 2 hospitals. COVID 19 patients over 18 years of age, admitted to hospital and under treatment with LPV/r were included. A screening of potential interactions related to LPV/r and home and hospital medication was carried out. Lexicomp® (Uptodate), HIV-drug interactions and COVID-drug interactions were used as the query database. RESULTS: 361 patients with a mean age of 62.77 ±â€¯14.64 years were included, where 59.6% (n = 215) were men. 62.3% (n = 225) had 1 or more potential interactions and 26, 87% (n = 97) 2 or more. The independent variables associated with presenting ≥1 potential interactions were age (>65) (OR 1.95; 95% CI 1.06-3.59, P = .033), ICU admission (OR 9.22; CI 95% 1.98-42.93; P = .005), previous respiratory pathology (OR 2.90; 95% CI 1.15-7.36; P = .024), psychiatric (OR 4.14; 95 CI% 1.36-12.61; P = .013), dyslipidemia (OR 3.21; 95% CI 1.63-6.35; P = .001) and the number of drugs prescribed (OR 4.33; 95% CI 2.40-7.81; P = .000). CONCLUSION: The prevalence of potential interactions in COVD 19 patient undergoing treatment with LPV/r is high, with age (>65), ICU admission, previous respiratory and psychiatric pathology, dyslipidemia and the number of prescribed drugs acting as risk factors.

OBJETIVOS: Determinar la prevalencia de interacciones potenciales en pacientes COVID19 en tratamiento con lopinavir/ritonavir (LPV/r). El objetivo secundario fue elaborar recomendaciones e identificar los factores de riesgo asociados a presentar interacciones potenciales con LPV/r. SUJETOS Y MÉTODOS: Estudio transversal y multicéntrico con la participación 2 hospitales. Se incluyeron pacientes COVID 19 mayores de 18 años, con ingreso hospitalario y en tratamiento con LPV/r. Se realizó un cribado de las interacciones potenciales relacionadas con LPV/r y la medicación domiciliaria y hospitalaria. Se utilizó como base de datos de consulta Lexicomp® (Uptodate), HIV-drug interacctions y COVID-drug interacctions. RESULTADOS: Se incluyeron 361 pacientes con una media de edad de 62,77 ±â€¯14,64 años, donde el 59,6% (n = 215) fueron hombres. El 62,3% (n = 225) tuvieron 1 o más interacciones potenciales y el 26, 87% (n = 97) 2 o más. Las variables independientes asociadas a presentar ≥ 1 interacciones potenciales fueron la edad (> 65) (OR 1,95; IC 95% 1,06­3,59; P = ,033), el ingreso en UCI (OR 9,22; IC 95% 1,98­42,93; P = ,005), la patología previa respiratoria (OR 2,90; IC 95% 1,15­7,36; P = ,024), psiquiátrica (OR 4,14; IC 95% 1,36­12,61; P = ,013), la dislipemia (OR 3,21; IC 95% 1.63­6,35; P = ,001) y el número de fármacos prescrito (OR 4,33; IC 95% 2,40­7,81; P = ,000). CONCLUSIÓN: La prevalencia de interacciones potenciales en paciente COVD 19 en tratamiento con LPV/r es elevada, comportándose como factores de riesgo asociados la edad (>65), el ingreso en UCI, la patología previa respiratoria, psiquiátrica y la dislipemia y el número de fármacos prescritos.

[Potential drug-drug interactions in COVID 19 patients in treatment with lopinavir/ritonavir]. / Interacciones medicamentosas potenciales en pacientes COVID 19 en tratamiento con lopinavir/ritonavir.

OBJECTIVES: To determine the prevalence of potential interactions in COVID-19 patients receiving lopinavir/ritonavir (LPV/r). The secondary objective was to develop recommendations and identify the risk factors associated with presenting potential interactions with LPV/r. SUBJECTS AND METHODS: Cross-sectional and multicenter study with the participation of 2 hospitals. COVID-19 patients over 18 years of age, admitted to hospital and under treatment with LPV/r were included. A screening of potential interactions related to LPV/r and home and hospital medication was carried out. Lexicomp® (Uptodate), HIV-drug interactions and COVID-drug interactions were used as the query database. RESULTS: 361 patients with a mean age of 62.77 ± 14.64 years were included, where 59.6% (n = 215) were men. 62.3% (n = 225) had 1 or more potential interactions and 26, 87% (n = 97) 2 or more. The independent variables associated with presenting ≥1 potential interactions were age (> 65) (OR 1.95; 95% CI 1.06-3.59, P =.033), ICU admission (OR 9.22; CI 95% 1.98-42.93; P =.005), previous respiratory pathology (OR 2.90; 95% CI 1.15-7.36; P =.024), psychiatric (OR 4.14; 95 CI % 1.36-12.61; P =.013), dyslipidemia (OR 3.21; 95% CI 1.63-6.35; P =.001) and the number of drugs prescribed (OR 4.33; 95% CI 2.40-7.81; P =.000). CONCLUSION: The prevalence of potential interactions in COVD-19 patient undergoing treatment with LPV/r is high, with age (> 65), ICU admission, previous respiratory and psychiatric pathology, dyslipidemia and the number of prescribed drugs acting as risk factors.

Major Neurologic Adverse Drug Reactions, Potential Drug-Drug Interactions and Pharmacokinetic Aspects of Drugs Used in COVID-19 Patients with Stroke: A Narrative Review.

Stroke has been considered as one of the underlying diseases that increases the probability of severe infection and mortality. Meanwhile, there are ongoing reports of stroke subsequent to COVID-19 infection. In this narrative paper, we reviewed major neurologic adverse drug reactions (ADRs) and pharmacokinetics of drugs which are routinely used for COVID-19 infection and their potential drug-drug interactions (PDDIs) with common drugs used for the treatment of stroke. It is highly recommended to monitor patients on chloroquine (CQ), hydroxychloroquine (HCQ), antiviral drugs, and/or corticosteroids about initiation or progression of cardiac arrhythmias, delirium, seizure, myopathy, and/or neuropathy. In addition, PDDIs of anti-COVID-19 drugs with tissue plasminogen activator (tPA), anticoagulants, antiaggregants, statins, antihypertensive agents, and iodine-contrast agents should be considered. The most dangerous PDDIs were interaction of lopinavir/ritonavir or atazanavir with clopidogrel, prasugrel, and new oral anticoagulants (NOACs).

Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy.

BACKGROUND: Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients. METHODS: Six hundred and 78 patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs. RESULTS: The overall prevalence of pDDIs was 78%, majority of patients had 1-2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between > 7 all prescribed drugs (p < 0.001) and ≥ 3 anti-cancer drugs (p < 0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity. CONCLUSIONS: Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with > 7 any types of drugs or ≥ 3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.