Prevalence of clinically manifested drug interactions in hospitalized patients: A systematic review and meta-analysis

Risk Factors for Potential Drug-Drug Interactions in Patients on Chronic Peritoneal Dialysis

INTRODUCTION

The prevalence of potential drug-drug interactions (pDDIs) is becoming a major safety concern, as it has been previously linked to a significant number of adverse drug events and could have serious consequences for patients, including death. This is especially relevant for patients with chronic renal failure, as they are particularly vulnerable to drug-drug interactions. The aim of this study was to evaluate the prevalence and associated factors of pDDIs in patients receiving chronic peritoneal dialysis.

METHODS

An observational, cross-sectional study was conducted on consecutive peritoneal dialysis patients attending four tertiary care hospitals for regular monthly examination. The primary outcome was the number of pDDIs identified using Lexicomp. Potential predictors were determined using multiple linear regression.

RESULTS

Total number of patients included in the study was 140. The results showed that pDDIs were highly prevalent, especially in patients who use antiarrhythmics (p = 0.001), have diabetes mellitus (p = 0.001), recently started peritoneal dialysis (p = 0.003), or have higher number of prescribed drugs (p < 0.001). Number of prescribed drugs (p < 0.001) remained a significant predictor of high-risk pDDIs in addition to the female gender (p = 0.043).

CONCLUSION

Clinicians should be particularly cautious when prescribing multiple medications to high-risk patients, such as peritoneal dialysis patients, to mitigate the risk of drug-drug interactions and associated adverse health outcomes.

Report of the 2023 AACP Council of Deans Taskforce on Pharmacy Research and Scholarship

OBJECTIVE

The objective of this review is to provide the conclusions from the American Association of Colleges of Pharmacy (AACP) Council of Deans (COD) Taskforce on Research and Scholarship.

FINDINGS

The charges and the findings of the committee are: (1) Define the scholarship needs/opportunities to strengthen the outputs. The committee recommends that AACP update its definitions of research/scholarship to include discovery, integration, application/practice, and teaching/learning. A deployed survey demonstrated a high Special Interest Groups research/scholarship interest. (2) Assemble a toolkit of grant and scholarship resources to assist colleges/schools. The AACP should update the existing funding opportunity listing and combine it with additional resources. (3) Create a framework for effective research collaboration and mentorship. The AACP should identify key areas of pharmacy research and experts to serve as mentors and to meet with external stakeholders. (4) and (5) Consider the need for and purpose of a COD standing committee for research and scholarship. Explore the value of a formal research dean's subcommittee. It was recommended that AACP form a research/scholarship committee or Special Interest Groups and create the Pharmacy Scholarship, Research, and Graduate Education pre-meeting to the Interim Meeting. (6) Identify key statements/outputs of the COD that need to be prepared for publication/sharing. We recommended the key statement/outputs in the areas of discovery, integration, application/practice, and teaching and learning.

SUMMARY

The taskforce reviewed the state of research and scholarship across the Academy and provided recommendations with the goal of advancing research across all areas of the pharmacy profession.

Adverse drug events caused by three high-risk drug-drug interactions in patients admitted to intensive care units: A multicentre retrospective observational study

AIMS

Knowledge about adverse drug events caused by drug-drug interactions (DDI-ADEs) is limited. We aimed to provide detailed insights about DDI-ADEs related to three frequent, high-risk potential DDIs (pDDIs) in the critical care setting: pDDIs with international normalized ratio increase (INR+ ) potential, pDDIs with acute kidney injury (AKI) potential, and pDDIs with QTc prolongation potential.

METHODS

We extracted routinely collected retrospective data from electronic health records of intensive care units (ICUs) patients (≥18 years), admitted to ten hospitals in the Netherlands between January 2010 and September 2019. We used computerized triggers (e-triggers) to preselect patients with potential DDI-ADEs. Between September 2020 and October 2021, clinical experts conducted a retrospective manual patient chart review on a subset of preselected patients, and assessed causality, severity, preventability, and contribution to ICU length of stay of DDI-ADEs using internationally prevailing standards.

RESULTS

In total 85 422 patients with ≥1 pDDI were included. Of these patients, 32 820 (38.4%) have been exposed to one of the three pDDIs. In the exposed group, 1141 (3.5%) patients were preselected using e-triggers. Of 237 patients (21%) assessed, 155 (65.4%) experienced an actual DDI-ADE; 52.9% had severity level of serious or higher, 75.5% were preventable, and 19.3% contributed to a longer ICU length of stay. The positive predictive value was the highest for DDI-INR+ e-trigger (0.76), followed by DDI-AKI e-trigger (0.57).

CONCLUSION

The highly preventable nature and severity of DDI-ADEs, calls for action to optimize ICU patient safety. Use of e-triggers proved to be a promising preselection strategy.

A meta-analysis assessing the prevalence of drug-drug interactions among hospitalized patients

PURPOSE

Drug-drug interactions (DDIs), particularly in hospitalized patients can result in adverse drug events and unfavorable health consequences. The aim of this meta-analysis is to provide up-to-date evidence on the prevalence of clinically evident adverse drug events due to DDIs in hospitalized patients.

METHODS

Data from Scopus, PubMed, Cochrane and Web of Science were extracted using these keywords (Drug interaction/drug-drug interactions, Hospital/ hospitals, Adverse drug event, Hospitalized patients, inpatient, Department, Hospital stay, Harm, Mortality, death). The studies that include Observational studies on hospitalized patients, reporting potential DDIs using an electronic database, and reporting the clinically observed adverse drug interactions (ADI) through symptoms, signs or Laboratory tests are included. Using Open meta-Software (version 12.11.14), the incidence of clinically evident DDIs among hospitalized patients was determined and shown in a forest plot.

RESULTS

Only 15 of the 8261 articles found through a literature search met the inclusion criteria and reported the desired outcome. The pooled prevalence of potential drug-drug interactions is 64.9% (CI 95% 0.618-0.736). While clinically evident DDIs have a pooled frequency of 17.17% (CI 95% 0.133-0.256).

CONCLUSION

The issue of DDIs remains a significant concern in hospitalized patients, with a notable rise in their prevalence. This meta-analysis encompassed a greater quantity of studies and demonstrated a heightened proportion of drug-drug interaction prevalence in comparison to the percentages reported in the previously published meta-analysis.

Design, development, implementation, and evaluation of a severe drug-drug interaction alert system in the ICU: An analysis of acceptance and override rates

BACKGROUND AND OBJECTIVE

The override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system.

METHODS

This is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046).

RESULTS

The knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75.

CONCLUSION

This study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers' response to DDIAS on a larger scale.

Screening the Drug-Drug Interactions Between Antimicrobials and Other Prescribed Medications Using Google Bard and Lexicomp® Online™ Database

Aim This study aimed to critically appraise the drug-drug interaction (DDI) screening performance of Google Bard (Google AI, Mountain View, California, United States) by comparing it with the authorized Lexicomp® Online™ database (Wolters Kluwer Health, Philadelphia, Pennsylvania, United States). Methods This cross-sectional study was conducted between April 2023 and August 2023, and enrolled 414 prescriptions that had been collected randomly between April 2023 and June 2023. These prescriptions were processed individually by Lexicomp online and Google Bard to screen for DDIs between antimicrobials and other prescribed medications. Results The total number of DDIs based on Lexicomp and Google Bard were 90 and 68, respectively. Cohen's Kappa (κ) values showed that there was a nil to slight agreement between Lexicomp and Google Bard regarding the DDI risk rating (κ=0.01). Regarding the severity rate, there was a slight agreement between them (κ=0.02), but in terms of reliability rate, there was no agreement (κ =-0.02). Conclusion This study unveiled differences between Lexicomp and Google Bard regarding their DDI identification, severity rating, and reliability rates. It is fundamental to consider that both tools have their strengths and weaknesses and, therefore, should not be individually depended on for final clinical decisions. However, Lexicomp can be considered authoritative in screening DDIs, but Google Bard currently lacks the necessary precision and reliability for conducting such screenings.

Retrospective cohort observation on psychotropic drug-drug interaction and identification utility from 3 databases: Drugs.com®, Lexicomp®, and Epocrates®

BACKGROUND

Pharmacotherapy is necessary for many people with psychiatric disorders and polypharmacy is common. The psychotropic drug-drug interaction (DDI) should be concerned and efficiently monitored by a proper instrument.

OBJECTIVES

This study aimed to investigate the prevalence and associated factors of psychotropic DDI and to compare the identification utility from three databases: Drugs.com®, Lexicomp®, and Epocrates®.

METHODS

This was a retrospective cohort design. We collected demographic and clinical data of all patients hospitalised in the psychiatric inpatient unit in 2020. Psychotropic DDI profiles were examined through three databases. Descriptive statistics were used to report comprehensiveness of each database and prevalence of psychotropic DDI. The Fleiss' kappa index would be analysed to indicate agreement strength of DDI severity classification among three databases.

RESULTS

From 149 total admissions, the psychotropic DDIs were found in 148 admissions (99.3%). Thorough the study, there were 182 of both psychotropic and other agents prescribed under 1,357 prescriptions. In total, 2,825 psychotropic DDIs were identified by using Drugs.com® 2,500 times, Epocrates® 2,269 times, and Lexicomp® 2,265 times. Interactions with clonazepam was the three most frequent agents when co-administrated with quetiapine (n = 56), risperidone (n = 36), and valproic acid and derivatives (n = 36). Serious DDIs were comparatively lower in incidence and there was no evidence of its association with reported clinical adverse consequences. The study revealed slight and fair agreement regarding severity classification among the three databases was found. DDI events detected by Drugs.com® were greatest in number, but Lexicomp® provided the broadest list of medications prescribed in our study.

CONCLUSION

Among three databases, interactions detected by Drugs.com® were greatest in number, whereas Lexicomp® provided the broadest list of medications. Development of such databases, based on both theoretical and clinical conceptions, should be focused to balance safety of patients and weariness of healthcare providers.

Adverse drug reactions in the ambulatory internal patients at the emergency department: Focus on causality assessment and drug-drug interactions

A non-interventional retrospective study in ambulatory patients was conducted at the emergency department of the Division of internal medicine. In 2 months, 266 suspected adverse drug reactions (ADRs) were identified in 224/3453 patients (6.5 %). In 158/3453 patients (4.6 %), an ADR was the reason for emergency department visit and in 49 patients (1.4 %), ADRs led to hospitalisation. A causality assessment algorithm was developed, which included Naranjo algorithm and levels of ADR recognition by the treating physician and the investigators. Using this algorithm, 63/266 ADRs (23.7 %) were classified as "certain", whereas using solely the Naranjo score calculation, only 19/266 ADRs (7.1 %) were assessed as "probable" or "certain", and the rest of ADRs (namely, 247/266 = 92.9 %) were assessed as "possible". There were 116/266 (43.6 %) ADRs related to potential drug-drug interactions (DDIs), stated in at least one of the literature sources used. Based on the causality relationship, the rate of the clinically expressed DDIs was 19.0 %, or 12/63 "certain" ADR cases. Of these, 10 cases presented serious DDI-related ADRs. In summary, ADR causality assessment based exclusively on Naranjo algorithm demonstrated low sensitivity at an ambulatory emergency setting. Additional clinical judgment, including the opinion of the treating physician, proved necessary to avoid under-rating of the causality relationship, and enabled the determination of clinically expressed DDIs.

Frequency, severity, and factors associated with clinically significant drug-drug interactions among patients with cancer attending Mbarara Regional Referral Hospital Cancer Unit, Uganda

BACKGROUND

Cancer is a major public health problem with pharmacotherapy being the cornerstone of its management. Cancer patients receive multiple drugs concurrently risking Drug-Drug Interactions (DDIs). DDIs, though avoidable, can significantly contribute to morbidity, mortality, and increased healthcare costs in this population of patients. Currently, there is no published study from Uganda on clinically significant DDIs (cs-DDIs) among cancer patients. This study identifies frequency, severity, and factors associated with cs-DDIs at Mbarara Regional Referral Hospital Cancer Unit (MRRHCU).

METHOD

A cross-sectional study was conducted among 300 cancer patients receiving chemotherapy from a tertiary care hospital in western Uganda from January-February 2022. A questionnaire and data collection form were used to collect patient data. Lexicomp® Drug interaction software was used to screen the patient drug information for DDIs and assess their severity. Predictors of DDIs were identified using logistic regression using SPSS (Statistical Package for Social Sciences).

RESULT

Three hundred participants were enrolled with a mean age of 48 ± 23.3 years. One hundred eighty-one patients experienced 495 cs-DDIs; with a mean of 1.7 ± 2.2. The prevalence of cs-DDI was 60.3% (55.0-66.0% at 95% CI). Digestive organ neoplasms were the most commonly (80, 26.7%) diagnosed category, and 'plant alkaloids and other natural products were the most frequently (143, 47.7%) used chemotherapeutic drug classes. About three-quarters of cs-DDIs were rated as category C risk (367, 74.1%) whereas over two-thirds (355, 71.7%) were moderate in severity.. Being female (aOR = 2.43 [1.23-4.48 at 95% CI]; P-value = 0.011) and use of ≥ 6 drugs concurrently (aOR = 18.82 [9.58-36.95 at 95% CI]; P-value < 0.001)) were significantly associated with cs-DDIs.

CONCLUSION

More than half of the participants experienced at-least one cs-DDI which is generally higher than what was reported in high-income settings. About three-quarters were category C and moderate in severity, and require enhanced monitoring for safety and treatment outcome. Being female and using ≥ 6 drugs were significantly associated with cs-DDIs.

Potential drug-drug interactions and associated factors among admitted patients with psychiatric disorders at selected hospitals in Northwest Ethiopia

BACKGROUND

Prescribing medications without potential drug-drug interactions (pDDIs) is one of the components of the rational use of medications. However, taking combined medications has resulted in life-threatening pDDIs, which are causing severe clinical outcomes for patients. This study was aimed at assessing the prevalence of pDDIs and associated factors in admitted patients with psychiatric disorders.

METHODS

An institution-based multicenter cross-sectional study was conducted among patients with psychiatric disorders admitted to a selected hospital in Northwest Ethiopia. Samples were approached through a systematic sampling method. The Statistical Package for the Social Sciences (SPSS) version 26 was used to analyze the data. Logistic regression was applied to determine the association of variables with pDDIs. A p-value of < 0.05 was statistically significant.

RESULTS

Out of 325 study participants, more than half (52.9%) were females, with a median age of 61 years. Overall, more than two-thirds (68.9%) were exposed to at least one clinically significant, either significant or serious level of pDDIs. Nearly one-fourth (23.2%) of participants had at least one serious level of pDDIs. Older patients were found more likely to have pDDIs compared to younger patients (p = 0.043). Similarly, patients with a higher number of prescribed medications (p = 0.035) and patients with longer hospital admissions (p = 0.004) were found more likely to be exposed to pDDIs than their counterparts.

CONCLUSION

In this study, a significant number of admitted patients with psychiatric problems encountered clinically significant pDDIs. As a result, healthcare providers could assess and follow patients with a combination of medications that potentially have a drug-drug interaction outcome.

Quantitative Prediction of Adverse Event Probability Due to Pharmacokinetic Interactions

INTRODUCTION

Iatrogeny due to drug-drug interactions is insufficiently documented, due to the high number of possible combinations.

OBJECTIVE

This study aimed to design a simple but general method to predict the variation of adverse events (AE) frequency due to a pharmacokinetic or pharmacodynamic interaction.

METHODS

Three prediction models were designed using a logistic probability density function. Each prediction model was based on three components: the AE odds ratio of each drug in the combination, and the area under the curve ratio (Rauc) of the pharmacokinetic interaction, if any. Pharmacodynamic interaction was assumed to be additive on logit scale. Rauc was predicted using a well-validated mechanistic static model, freely available online. No combination study is required. The method was evaluated against a wide range of AEs (28 High Level Terms) and 211 drug combinations (involving 43 victim drugs and 55 perpetrators), by comparing the observed and predicted frequencies. The observed odds ratios were estimated with a disproportionality analysis from the FDA Adverse Event Reporting System, using an approach that minimizes biases.

RESULTS

With the best model, the rate of prediction considered as correct (within 50-200% of the observed value) was 72%, and the bias was negligible (-5%). The AE odds ratio due to pharmacokinetic and pharmacodynamic interactions was equally well predicted.

CONCLUSIONS

A simple workflow to implement the method in practice is proposed. This method may help to foresee and to anticipate the harmful consequences associated with drug-drug interactions, at virtually no experimental cost, when the odds ratio of an AE is known for each drug alone and the AUC ratio is known or predicted by a suitable model.

Evaluation of pharmacokinetic interactions of amoxicillin with ranitidine in healthy human volunteers of Karachi, Pakistan

Polypharmacy may be considered as the customary practice to provide optimum care services to patients but inter resulted in augmented probability of multiple drug interaction. Keeping in view the importance of drug interaction possibility, this study was designed to evaluate the effect of ranitidine on pharmacokinetics of amoxicillin in the local population of Karachi, Pakistan. Amoxicillin and ranitidine are the most commonly prescribed drugs to treat duodenal ulcer caused by Helicobacter pylori. The current investigation was carried out as a single center, open label, two phase, single dose, randomized way in cross over manner to evaluate the potential of pharmacokinetic interaction among amoxicillin formulation and ranitidine in adult healthy male volunteers. Post dosing blood samples were collected at multiple time points that are 0.5, 1, 1.5, 2, 3, 4, 6, 8 and 10 hours after administering amoxicillin 250mg capsule with and without ranitidine. For estimation of amoxicillin concentration in plasma, an HPLC method was developed and validated. The solvent system consisted of 0.025M phosphate buffer: acetonitrile (94:6 v/v). C18 column was employed with a flow rate of 1.0 ml/minute and at 230nm. A linear pattern with a correlation coefficient of 0.999 in the concentration ranges of 25μg/mL to 0.097μg/mL for amoxicillin and 25μg/mL to 0.048μg/mL for ranitidine was observed. Amoxicillin retention time was about 8 minutes and ranitidine retention time was around 12 minutes. Amoxicillin levels were computed and the concentrations were applied to calculate the pharmacokinetic parameters. Pharmacokinetic parameters were estimated by Kinetica TM 4.4.1 (Thermo Electron Corp. USA). The analysis of variance (two way) and t test (two one sided) were applied on log transformed pharmacokinetic parameters of amoxicillin. The T_max was determined between amoxicillin alone and amoxicillin with ranitidine by Friedman test. The 90% confidence interval values for C_max(calc) (0.687–0.743) and T_max(calc) (1.148–1.742) for amoxicillin with or without ranitidine were not found within the FDA acceptable limits of 0.8–1.25. Study demonstrated the significant reduction in peak plasma levels of amoxicillin in presence of ranitidine. It is advisable to administer both drugs with time interval to avoid such interactions and increases in the bactericidal efficacy of amoxicillin.

Drug interaction alerts in older primary care patients, and related medically justified actions

PURPOSE

To describe presented interaction alerts in older patients, and the extent to which these require further medical action for the specific patient or are already being addressed.

METHODS

Interaction alerts presented at a physician consultation, for 274 consecutive primary care patients treated with two or more drugs (median age: 75 years; 59% female), were extracted. These alerts are based on Janusmed, a decision support integrated in the medical records that provides recommendations for managing the interactions. One general practitioner (GP) and one GP/clinical pharmacologist determined in retrospect, first independently and then in consensus, whether the alerts justified further medical action, considering each patient's health condition.

RESULTS

In all, 405 drug interaction alerts in 151 (55%) patients were triggered. Medical action in response was deemed medically justified for 35 (9%) alerts in 26 (17%) patients. These actions most often involved a switch to a less interacting drug from the same drug class (n = 10), a separate intake (n = 9), or the ordering of a laboratory test (n = 8). Out of 531 actions suggested by the alert system, only 38 (7%) were applicable to the specific patient, as, for instance, laboratory parameters were already being satisfactorily monitored or a separate intake implemented.

CONCLUSIONS

More than every other older patient receives drug treatment that triggers drug interaction alerts. Nine in ten alerts were already being addressed or were not relevant in the clinical setting, whereas, for the remaining tenth, some medical action, that for unknown reasons had not been taken, was reasonable. These findings show that interaction alerts are questionable as indicators of problematic prescribing.

Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes

Purpose

While the beneficial effects of medications are numerous, drug–drug interactions may lead to adverse drug reactions that are preventable causes of morbidity and mortality. Our goal was to quantify the prevalence of potential drug–drug interactions in drug prescriptions at Danish hospitals, estimate the risk of adverse outcomes associated with discouraged drug combinations, and highlight the patient types (defined by the primary diagnosis of the admission) that appear to be more affected.

Methods

This cross‐sectional (descriptive part) and cohort study (adverse outcomes part) used hospital electronic health records from two Danish regions (~2.5 million people) from January 2008 through June 2016. We included all inpatients receiving two or more medications during their admission and considered concomitant prescriptions of potentially interacting drugs as per the Danish Drug Interaction Database. We measured the prevalence of potential drug–drug interactions in general and discouraged drug pairs in particular during admissions and associations with adverse outcomes: post‐discharge all‐cause mortality rate, readmission rate and length‐of‐stay.

Results

Among 2 886 227 hospital admissions (945 475 patients; median age 62 years [IQR: 41–74]; 54% female; median number of drugs 7 [IQR: 4–11]), patients in 1 836 170 admissions were exposed to at least one potential drug–drug interaction (659 525 patients; median age 65 years [IQR: 49–77]; 54% female; median number of drugs 9 [IQR: 6–13]) and in 27 605 admissions to a discouraged drug pair (18 192 patients; median age 68 years [IQR: 58–77]; female 46%; median number of drugs 16 [IQR: 11–22]). Meropenem‐valproic acid (HR: 1.5, 95% CI: 1.1–1.9), domperidone‐fluconazole (HR: 2.5, 95% CI: 2.1–3.1), imipramine‐terbinafine (HR: 3.8, 95% CI: 1.2–12), agomelatine‐ciprofloxacin (HR: 2.6, 95% CI: 1.3–5.5), clarithromycin‐quetiapine (HR: 1.7, 95% CI: 1.1–2.7) and piroxicam‐warfarin (HR: 3.4, 95% CI: 1–11.4) were associated with elevated mortality. Confidence interval bounds of pairs associated with readmission were close to 1; length‐of‐stay results were inconclusive.

Conclusions

Well‐described potential drug–drug interactions are still missed and alerts at point of prescription may reduce the risk of harming patients; prescribing clinicians should be alert when using strong inhibitor/inducer drugs (i.e. clarithromycin, valproic acid, terbinafine) and prevalent anticoagulants (i.e. warfarin and non‐steroidal anti‐inflammatory drugs ‐ NSAIDs) due to their great potential for dangerous interactions. The most prominent CYP isoenzyme involved in mortality and readmission rates was 3A4.

Potential and real drug interactions in patients treated with abiraterone or enzalutamide in clinical practice

BACKGROUND

Abiraterone and enzalutamide, androgen receptor pathway inhibitors (ARPI) for the treatment of metastatic castration-resistant prostate cancer (mCRPC), are at high risk of potential drug interactions (PDIs). We aimed to describe PDIs and their management, and triggered adverse events (AEs) in clinical practice.

METHODS

We conducted a cross-sectional study in mCRPC patients who started treatment with abiraterone or enzalutamide in a university hospital between August 1st, 2016 and July 31st, 2020. Lexicomp® was used to identify and analyze PDIs, and the clinical records to assess their management and the occurrence of AEs.

RESULTS

We included 173 patients: 36.8% and 93.0% of patients treated with abiraterone and enzalutamide, respectively, had at least 1 PDI. Globally, 6.3% of PDIs had X-risk (contraindication due to high probability of AE). Treatment was modified in 9.2% of patients and 9.8% suffered AEs due to PDIs. Factors associated with a higher risk of PDIs were polypharmacy (OR=41.0, p=0.003) and treatment with enzalutamide (OR=128.26, p< 0.001).

CONCLUSIONS

At least two-thirds of patients treated with ARPI suffered a PDI. Overall, abiraterone would have a more favorable PDI profile. Knowing these interaction profiles may be helpful to develop a more efficient therapeutic follow-up and to select the safest treatment.

Real clinical impact of drug-drug interactions of immunosuppressants in transplant patients

The main objective was to determine the prevalence of real drug-drug interactions (DDIs) of immunosuppressants in transplant patients. We conducted a prospective, observational 1-year study at a tertiary hospital, including all transplanted patients. We evaluated data from monitoring blood concentrations of immunosuppressive drugs and adverse drug events (ADEs) caused by DDIs. The DDIs were classified as C, D, or X according to their Lexi-Interact rating (C = monitor therapy, D = consider therapy modification, X = avoid combination). The clinical importance of real DDIs was expressed in terms of patient outcomes. The causality of DDIs was determined using Drug Interaction Probability Scale. The data were analyzed using Statistical Package for Social Sciences v. 25.0. A total of 309 transplant patients were included. Their mean age was 52.0 ± 14.7 years (18-79) and 69.9% were male. The prevalence of real DDIs was 21.7%. Immunosuppressive drugs administered with antifungal azoles and tacrolimus (TAC) with nifedipine have a great clinical impact. Real DDIs caused ADEs in 22 patients. The most common clinical outcome was nephrotoxicity (1.6%; n = 5), followed by hypertension (1.3%; n = 4). Suggestions for avoiding category D and X DDIs included: changing the immunosuppressant dosage, using paracetamol instead of non-steroidal anti-inflammatory drugs, and interrupting atorvastatin. The number of drugs prescribed and having been prescribed TAC was associated with an increased risk of real DDIs. There are many potential DDIs described in the literature but only a small percentage proved to be real DDIs, based on the patients´ outcomes.

Assessment of Drug Interactions with Online Electronic Checkers in Multi-Pathological Patients

INTRODUCTION

Multi-pathological patients are at high risk of drug interactions and side effects. We aimed to assess the usefulness of 3 online drug interaction checkers.

METHODS

In a cross-sectional study, carried out in the Medicine Department of Hospital General of Castellon, Spain, in February 2020, we assessed drug interaction detection with 3 online electronic checkers, Drugs.com, Lexicomp®, and Medscape, and compared results obtained with the 3 tools. From every hospitalized patient, we obtained the list of drugs he or she had been taking until admission. Bivariable tests were used for analysis. p values <0.05 were considered significant.

RESULTS

We included data from 134 patients; 68 (51%) were male; median (and interquartile range) of their age was 82 (76-88) years. A total of 1,082 substance drugs were entered in the checkers. The number of highest grade interactions found with every program was Drugs.com 85, Lexicomp® 33, and Medscape 67. Positive correlations were found between age and number of drug substances prescribed (p = 0.009) and between number of drug substances prescribed and interactions found with any of the 3 checkers (p < 0.001 in all 3 cases). Regarding highest grade interactions, agreement among all 3 checkers was poor.

CONCLUSIONS

The 3 online checkers we assessed found a large number of interactions. The 3 programs gave very discrepant results. Impact on Practice Statements: The analyzed programs, Drugs.com, Lexicomp®, and Medscape Interactions, found a large number of drug interactions in the studied patients. The 3 programs gave very discrepant results among them.

[Potential Drug-Drug Interactions among Three or More Drugs: A Retrospective Multi-center Study in Pharmacies in the Kojima Region, Okayama]

Drug-drug interactions (DDIs) are responsible for an increase in the incidence of adverse drug reactions. Although CYP is known to be involved in metabolic processes, the DDIs among three or more drugs that involve the same CYP molecular species have not been fully investigated. In this study, we retrospectively examined the relationship between the number of drugs and potential DDIs in metabolic processes involving CYPs in patients who picked up their prescribed drugs at 11 pharmacies in the Kojima Branch of the Okayama Pharmaceutical Association. We found that 66.5% of the 924 patients had potential DDIs; more than half of the patients who took six or more drugs had potential DDIs among three or more drugs. The mean number of CYP3A4-related drugs involved in potential DDIs was 3.52±1.56 in patients who took seven drugs, suggesting the need for careful monitoring of specific symptoms and blood test results for the early detection of adverse drug reactions caused by DDIs among three or more drugs.

Current Knowledge about Providing Drug-Drug Interaction Services for Patients-A Scoping Review

Drug-drug interactions (DDIs) pose a major problem to patient safety. eHealth solutions have the potential to address this problem and generally improve medication management by providing digital services for health care professionals and patients. Clinical decision support systems (CDSS) to alert physicians or pharmacists about DDIs are common, and there is an extensive body of research about CDSS for professionals. Information about DDIs is commonly requested by patients, but little is known about providing similar support to patients. The aim of this scoping review was to explore and describe current knowledge about providing digital DDI services for patients. Using a broad search strategy and an established framework for scoping reviews, 19 papers were included. The results show that although some patients want to check for DDIs themselves, there are differences between patients, in terms of demands and ability. There are numerous DDI services available, but the existence of large variations regarding service quality implies potential safety issues. The review includes suggestions about design features but also indicates a substantial knowledge gap highlighting the need for further research about how to best design and provide digital DDI to patients without risking patient safety or having other unintended consequences.