Adverse drug reactions and correlations with drug–drug interactions: A retrospective study of reports from 2011 to 2020

Recognizing and preventing unacknowledged prescribing errors associated with polypharmacy

BACKGROUND

Prescribing errors put an enormous burden on health and the economy, claiming implementation of effective methods to prevent/reduce them. Polypharmacy regimens (five or more drugs) are highly prone to unacknowledged prescribing errors, since the complex network of drug-drug interactions, guidelines and contraindications is challenging to be adequately evaluated in the prescription phase, especially if different doctors are involved. Clinical decision support systems aimed at polypharmacy evaluation may be crucial to recognize and correct prescribing errors.

METHODS

A commercial clinical decision support system (Drug-PIN®) was applied to estimate the frequency of unrecognized prescribing errors in a group of 307 consecutive patients accessing the hospital pre-admission service of the Sant'Andrea Hospital of Rome, Italy, in the period April-June 2023. Drug-PIN® is a two-step system, first scoring the risk (low, moderate or high) associated with a certain therapy-patient pair, then allowing therapy optimization by medications exchanges. We defined prescribing errors as cases where therapy optimization could achieve consistent reduction of the Drug-PIN® calculated risk.

RESULTS

Polypharmacy was present in 205 patients, and moderate to high risk for medication harm was predicted by Drug-PIN® in 91 patients (29.6%). In 58 of them (63.7%), Drug-PIN® guided optimization of the therapy could be achieved, with a statistically significant reduction of the calculated therapy-associated risk score. Patients whose therapy cannot be improved have a statistically significant higher number of used drugs. Considering the overall study population, the rate of avoidable prescribing errors was 18.89%.

CONCLUSIONS

Results suggest that computer-aided evaluation of medication-associated harm could be a valuable and actionable tool to identify and prevent prescribing errors in polypharmacy. We conducted the study in a Hospital pre-admission setting, which is not representative of the general population but represents a hotspot to intercept fragile population, where a consistent fraction of potentially harmful polypharmacy regimens could be promptly identified and corrected by systematic use of adequate clinical decision support tools.

Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure

INTRODUCTION

Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.

OBJECTIVE

This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis.

METHODS

The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression).

RESULTS

Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency's (EMA's) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs.

CONCLUSIONS

Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

Characteristics of CYP3A4-related potential drug-drug interactions in outpatients receiving prescriptions from multiple clinical departments

BACKGROUND

Drug-drug interactions (DDIs) increase the incidence of adverse drug reactions (ADRs). In a previous report, we revealed that the incidence of potential DDIs due to the same CYP molecular species in one prescription exceeds 90% among patients taking six or more drugs and that CYP3A4 markedly influences the increase in the number of potential DDIs in clinical practice. However, the factors contributing to an increased number of potential DDIs in prescriptions from multiple clinical departments remain poorly clarified.

METHODS

This observational study was performed at five pharmacies in Okayama Prefecture, Japan. Patients who visited these pharmacies from 11 April 2022 to 24 April 2022 were included, except those who had prescriptions only from a single clinical department. A stratified analysis was performed to determine the incidence of CYP3A4-related potential DDIs according to the number of drugs taken. Additionally, factors associated with an increase in the number of drugs involved in CYP3A4-related potential DDIs were identified using multiple linear regression analysis. In this study, potential DDIs for the prescription data subdivided by clinical department, containing two or more drugs, were used as control data.

RESULTS

Overall, 372 outpatients who received prescriptions from multiple clinical departments were included in the current study. The number of drugs contributing to CYP3A4-related potential DDIs increased with an increase in the number of clinical departments. Notably, in cases taking fewer than six drugs, prescriptions from multiple clinical departments had a higher frequency of CYP3A4-related potential DDIs than those in prescriptions subdivided by clinical department. Multiple regression analysis identified "Cardiovascular agents", "Agents affecting central nervous system", and "Urogenital and anal organ agents" as the top three drug classes that increase CYP3A4-related potential DDIs.

CONCLUSION

Collectively, these results highlight the importance of a unified management strategy for prescribed drugs and continuous monitoring of ADRs in outpatients receiving prescriptions from multiple clinical departments even if the number of drugs taken is less than six.

Patterns, outcomes, and preventability of clinically manifest drug-drug interactions in older outpatients: a subgroup analysis from a 6-year-long observational study in North India

Older adults are vulnerable to adverse drug reactions (ADRs) and drug-drug interactions (DDIs). Evidence on clinically manifest DDIs in older outpatients is scanty. The present study aims to report clinically manifest DDIs, their risk factors, and preventive measures. A subgroup analysis of a 6-year (2015-2021) long prospective study was conducted in a tertiary hospital in North India. Older outpatients with ADRs constituted the study participants. Among 933 ADRs reported in 10,400 patient registrations, clinically manifest DDIs were involved in 199 (21.3%). DDIs accounted for 29.9%, 26.5%, and 21.3% of drug-related metabolic, vascular, and nervous system disorders, respectively. Movement disorders (n = 18), hypotension (n = 16), and hypoglycemia (n = 15) were the most common manifestations. Eighty-six percent of DDIs were of the pharmacodynamic type, and 13.1% were immune-mediated. Around 35% of DDIs resulted in hospitalization, with hyponatremia, movement disorder, and renal impairment as the common reasons. Older adults with Parkinsonism, infection, coronary artery disease, neuropsychiatric disease, and diabetes mellitus, respectively, had 3.28, 2.85, 1.97, 1.76, and 1.80 times higher odds of DDIs. Those receiving ≥ 10 drugs had 5.31 times higher odds of DDIs compared to individuals receiving 1-4 drugs. "Avoiding the causative drug," "optimal monitoring of the patient," and "start-low and go-slow" policy together could prevent 85% of DDIs. In conclusion, every fifth case of ADRs and nearly one third of ADR-related hospitalizations in older adults are related to DDIs. Movement disorder, hypotension, and hypoglycemia are the common manifestations. A holistic approach with drug omission, optimal patient monitoring, and slow titration of therapy can prevent significant DDIs in older adults.

Zolpidem-triggered atrial fibrillation in a patient with cardiomyopathy: a case report

BACKGROUND

Zolpidem is a non-benzodiazepine hypnotic widely used to manage insomnia. Zolpidem-triggered atrial fibrillation (AF) in patients with cardiomyopathy has never been reported before.

CASE PRESENTATION

A 40-year-old man with Duchenne muscular dystrophy-related cardiomyopathy attempted suicide and developed new-onset AF after zolpidem overdose. One year before admission, the patient visited our clinic due to chest discomfort and fatigue after daily walks for 1 month; both electrocardiography (ECG) and 24-hour Holter ECG results did not detect AF. After administration of cardiac medication (digoxin 0.125 mg/day, spironolactone 40 mg/day, furosemide 20 mg/day, bisoprolol 5 mg/day, sacubitril/valsartan 12/13 mg/day), he felt better. AF had never been observed before this admission via continuous monitoring during follow-up. Sixteen days before admission, the patient saw a sleep specialist and started zolpidem tartrate tablets (10 mg/day) due to insomnia for 6 months; ECG results revealed no significant change. The night before admission, the patient attempted suicide by overdosing on 40 mg of zolpidem after an argument, which resulted in severe lethargy. Upon admission, his ECG revealed new-onset AF, necessitating immediate cessation of zolpidem. Nine hours into admission, AF spontaneously terminated into normal sinus rhythm. Results from the ECG on the following days and the 24-hour Holter ECG at 1-month follow-up showed that AF was not detected.

CONCLUSIONS

This study provides valuable clinical evidence indicating that zolpidem overdose may induce AF in patients with cardiomyopathy. It serves as a critical warning for clinicians when prescribing zolpidem, particularly for patients with existing heart conditions. Further large-scale studies are needed to validate this finding and to explore the mechanisms between zolpidem and AF.

The impacts of natural product miltirone and the CYP2D6 pharmacogenetic phenotype on fluoxetine metabolism

Introduction: To study the effects of drug-induced CYP2D6 activity inhibition and genetic polymorphisms on fluoxetine metabolism, rat liver microsomes (RLMs) and SD rats were used to investigate the potential drug‒drug interactions (DDIs), and CYP2D6 http://muchong.com/t-10728934-1 recombinant baculosomes were prepared and subjected to catalytic reactivity studies. Methods and Results: All analytes were detected by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC‒MS/MS). After screening for 27 targeted natural products, miltirone was identified as having obvious inhibitory effect on fluoxetine metabolism in RLMs. In vivo, the concentration of fluoxetine in rat blood increased markedly after miltirone administration. The molecular docking results showed that miltirone bound more strongly to CYP2D6 than fluoxetine, and PHE120 may be the key residue leading to the inhibition of CYP2D6-mediated fluoxetine N-demethylation by miltirone. In terms of the genetic polymorphism of CYP2D6 on fluoxetine metabolism, the intrinsic clearance values of most variants were significantly altered. Among these variants, CYP2D6*92 and CYP2D6*96/Q424X were found to be catalytically inactive for fluoxetine metabolism, five variants (CYP2D6*89/L142S, *97/F457L, *R497, *V342M and *R344Q) exhibited markedly increased clearance values (>125.07%) and seven variants (CYP2D6*2, *10, *87/A5V, *93/T249P, *E215K, *R25Q and *R440C) exhibited significantly decreased clearance values (from 6.62% to 66.79%) compared to those of the wild-type. Conclusion: Our results suggest that more attention should be given to subjects in the clinic who take fluoxetine and also carry one of these infrequent CYP2D6 alleles or are coadministered drugs containing miltirone.

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

BACKGROUND

Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare.

METHODS

In this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns a knowledge subgraph for each drug-pair to interpret the predicted DDI, where each of the edges is associated with a connection strength indicating the importance of a known DDI or resembling strength between a drug-pair whose connection is unknown. Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities.

RESULTS

Here we show the evaluation results of KnowDDI on two benchmark DDI datasets. Results show that KnowDDI obtains the state-of-the-art prediction performance with better interpretability. We also find that KnowDDI suffers less than existing works given a sparser knowledge graph. This indicates that the propagated drug similarities play a more important role in compensating for the lack of DDIs when the drug representations are less enriched.

CONCLUSIONS

KnowDDI nicely combines the efficiency of deep learning techniques and the rich prior knowledge in biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks, such as protein-protein interactions, drug-target interactions and disease-gene interactions, eventually promoting the development of biomedicine and healthcare.

Retrospective Analysis of Patient-Reported Adverse Drug Reactions in an Italian Allergy Unit: ALLERG-RAF Study

INTRODUCTION

The Italian Medicines Agency indicates that about 5% of hospital admissions are due to adverse drug reactions (ADRs). Several factors are recognized to be associated with an increased risk for ADRs, such as the female gender and polytherapy. The aim of this study was to retrospectively analyze the suspected ADRs reported by patients during the anamnestic interview at the Allergy Unit.

PATIENTS AND METHODS

ALLERG-RAF study is a retrospective analysis of the medical records of patients evaluated in the Allergy Unit of ASST Spedali Civili and the University of Brescia from 2000 to 2016. The inclusion criteria were age ≥18 years and medical consultation requested for suspected ADRs. Data relating to the patient's intrinsic characteristics, the drug supposed to be the cause, and the prescribed pharmacological therapy were collected. Pseudonymized data from each patient were collected in an informatics database.

RESULTS

From 2000 to 2016, 35,817 accesses to the Allergy Unit were made, and 2,171 unique events related to a suspected ADR were collected in 1,840 patients. More than two-thirds of the reports concerned females (70.4%). Antibiotics were involved in the majority of the self-reported suspected ADRs (48.7%), particularly beta-lactams (61.1%). Anti-inflammatory drugs, mainly NSAIDs, were second in incidence and suspected in 25.2% of reports. As a site of ADR manifestation, most of the reported reactions involve the skin. No clinical sequelae were reported.

CONCLUSIONS

Our results underline the importance of patient reporting in pharmacovigilance. Furthermore, gender gap data emphasizes the importance of the gender-specific medicine approach.

Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network

Drug-drug interactions (DDIs) for emerging drugs offer possibilities for treating and alleviating diseases, and accurately predicting these with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. Here we propose EmerGNN, a graph neural network that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The edges of the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.

Risks and benefits of salicylates in food: a narrative review

Salicylates are generally present in plants as part of their defense system against pathogens and environmental stress. Major dietary sources of salicylates were found in spices and herbs, such as curry and paprika (hot powder). Several studies suggest that these natural salicylates offer health benefits in the human body, such as antidiabetic, anticancer, antiviral, and anti-inflammatory properties. However, despite their advantages, salicylates can be harmful to people with allergies, and high doses of salicylates may cause respiratory alkalosis and gastrointestinal bleeding. Additionally, salicylates can interact with certain drugs, such as nonsteroidal anti-inflammatory drugs and warfarin. This narrative review aimed to consolidate recent information on the content of salicylates in food based on the literature, while also highlighting the benefits and risks associated with salicylate consumption in humans. Based on the literature review and analysis of results, it can be concluded that the dietary intake of salicylates in vegetarians can be relatively high, resulting in concentrations of salicylic acid in the blood and urine that are comparable to those observed in patients taking a low dose of aspirin (75 mg). This suggests that a diet rich in salicylates may have potential benefits in preventing and treating some diseases that require low doses of aspirin.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

Opportunities and challenges of pharmacovigilance in special populations: a narrative review of the literature

The relatively new discipline of pharmacovigilance (PV) aims to monitor the safety of drugs throughout their evolution and is essential to discovering new drug risks. Due to their specific and complex physiology, children, pregnant women, and elderly adults are more prone to adverse drug reactions (ADRs). Additionally, the lack of clinical trial data exacerbates the challenges faced with pharmacotherapy in these populations. Elderly patients tend to have multiple comorbidities often requiring more extensive medication, which adds additional challenges for healthcare professionals (HCPs) in delivering safe and effective pharmacotherapy. Clinical trials often have inherent limitations, including insufficient sample size and limited duration of research; as some ADRs are attributed to long-term use of a drug, these may go undetected during the course of the trial. Therefore, the implementation of PV is key to insuring the safe and effective use of drugs in special populations. We conducted a thorough review of the scientific literature on PV systems across the European Union, the United States, and China. Our review focused on basic physiological characteristics, drug use, and PV for specific populations (children, pregnant women, and the elderly). This article aims to provide a reference for the development of follow-up policies and improvement of existing policies as well as provide insight into drug safety with respect to patients of special populations.

In Vitro Evaluation of the Potential for Drug Interactions by Salidroside

Several studies utilizing Rhodiola rosea, which contains a complex mixture of phytochemicals, reported some positive drug-drug interaction (DDI) findings based on in vitro CYP450's enzyme inhibition, MAO-A and MAO-B inhibition, and preclinical pharmacokinetic studies in either rats or rabbits. However, variation in and multiplicity of constituents present in Rhodiola products is a cause for concern for accurately evaluating drug-drug interaction (DDI) risk. In this report, we examined the effects of bioengineered, nature-identical salidroside on the inhibition potential of salidroside on CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 utilizing human liver microsomes, the induction potential of salidroside on CYP1A2, CYP2B6 and CYP3A4 in cryopreserved human hepatocytes, the inhibitory potential of salidroside against recombinant human MAO-A and MAO-B, and the OATP human uptake transport inhibitory potential of salidroside using transfected HEK293-OATP1B1 and OATP1B3 cells. The results demonstrate that the bioengineered salidroside at a concentration exceeding the predicted plasma concentrations of <2 µM (based on 60 mg PO) shows no risk for drug-drug interaction due to CYP450, MAO enzymes, or OATP drug transport proteins. Our current studies further support the safe use of salidroside in combination with other drugs cleared by CYP or MAO metabolism or OATP-mediated disposition.

Frequencies of Combined Dysfunction of Cytochromes P450 2C9, 2C19, and 2D6 in an Italian Cohort: Suggestions for a More Appropriate Medication Prescribing Process

Improper drug prescription is a main cause of both drug-related harms (inefficacy and toxicity) and ineffective spending and waste of the healthcare system's resources. Nowadays, strategies to support an improved, informed prescription process may benefit from the adequate use of pharmacogenomic testing. Using next-generation sequencing, we analyzed the genomic profile for three major cytochromes P450 (CYP2C9, CYP2C19, CYP2D6) and studied the frequencies of dysfunctional isozymes (e.g., poor, intermediate, or rapid/ultra-rapid metabolizers) in a cohort of 298 Italian subjects. We found just 14.8% of subjects with a fully normal set of cytochromes, whereas 26.5% of subjects had combined cytochrome dysfunction (more than one isozyme involved). As improper drug prescription is more frequent, and more burdening, in polytreated patients, since drug-drug interactions also cause patient harm, we discuss the potential benefits of a more comprehensive PGX testing approach to support informed drug selection in such patients.

Epidemiological investigation of emergency infusion adverse drug reactions in Nanjing, China: a prospective cross-sectional study

OBJECTIVE

Little is known about the morbidity and mortality of infusion Adverse drug reactions (ADRs) in the emergency department. We sought to evaluate the epidemiology of emergency infusion ADRs.

MATERIALS AND METHODS

This was a prospective study of infusion ADRs in the emergency infusion unit (EIU) of a tertiary hospital from 1 January 20201 January 2020, to 31 December 2021w31 December 2021. Emergency infusion ADRs were identified as intravenous drug-related ADRs that the causality was determined using the Naranjo algorithm. The incidence, severity and preventability of these ADRs were assessed using other standard criteria.

RESULTS

A total of 327 ADRs were recorded for 320 participants, antibiotics were the class of drugs most commonly involved, and 76.15% of ADRs occurred within the first hour. The most common symptoms observed were skin manifestations, accounting for 46.04% of ADRs. Mild reactions accounted for 85.32% based on the Hartwig and Siegel scale. In 89.30% of the reports, the ADRs were evaluated as not preventable based on the modified Schumock and Thornton scale. The causality and severity of ADRs were related to Charlson Comorbidity Index score and age (P < 0.05).

CONCLUSION

This epidemiological study described the pattern of emergency infusion ADRs in East China in detail. These findings may be useful to compare patterns among different centers.

Opinion paper on the systematic application of integrated bioinformatic tools to actuate routine precision medicine in poly-treated patients

Precision Medicine is a reality in selected medical areas, as oncology, or in excellent healthcare structures, but it is still far to reach million patients who could benefit from this medical concept. Here, we sought to highlight how the time is ripe to achieve horizontal delivery to a significant larger audience of patients, represented by the poly-treated patients. Combination therapies are frequent (especially in the elderly, to treat comorbidities) and are related to decreased drug safety and efficacy, disease's exacerbation, additional treatments, hospitalization. But the recent development and validation of bioinformatic tools, aimed to automatic evaluation and optimization of poly-therapies, according to the unique individual characteristics (including genotype), is ready to change the daily approach to pharmacological prescription.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Potentially clinically significant drug-drug interactions in older patients admitted to the hospital: A cross-sectional study

Background: An international consensus list of potentially clinically significant drug-drug interactions (DDIs) in older people has been recently validated. Our objective was to describe the prevalence and characteristics of drug combinations potentially causing clinically significant DDIs identified in the medication history of older patients admitted to the hospital and the prevalence and characteristics of manifest DDIs-DDIs involved in adverse drug events present at hospital admission, DDIs that contributed to ADE-related hospital admissions, and DDIs involved in drug-related laboratory deviations. Methods: The data were obtained from our previous study that examined the drug-relatedness of hospital admissions to University Hospital Hradec Králové via the department of emergency medicine in the Czech Republic. Patients ≥ 65 years old were included. Drug combinations potentially causing clinically significant DDIs were identified using the international consensus list of potentially clinically significant DDIs in older people. Results: Of the 812 older patients admitted to the hospital, 46% were exposed to drug combinations potentially causing clinically significant DDIs. A combination of medications that affect potassium concentrations accounted for 47% of all drug combinations potentially causing clinically significant DDIs. In 27 cases, potentially clinically significant DDIs were associated with drug-related hospital admissions. In 4 cases, potentially clinically significant DDIs were associated with ADEs that were present at admissions. In 4 cases, the potentially clinically significant DDIs were associated with laboratory deviations. Manifest DDIs that contributed to drug-related hospital admissions most frequently involved antithrombotic agents and central nervous system depressants. Conclusion: The results confirm the findings from the European OPERAM trial, which found that drug combinations potentially causing clinically significant DDIs are very common in older patients. Manifest DDIs were present in 4.3% of older patients admitted to the hospital. In 3.3%, manifest DDIs contributed to drug-related hospital admissions. The difference in the rates of potential and manifest DDIs suggests that if a computerized decision support system is used for alerting potentially clinically significant DDIs in older patients, it needs to be contextualized (e.g., take concomitant medications, doses of medications, laboratory values, and patients' comorbidities into account).