A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction

The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is of great significance for drug development and health. However, most recent studies have limited the drug-drug interaction prediction task to identifying interactions between substructures, overlooking molecular hierarchical information. Moreover, the extracted substructures in these methods are always restricted to have the same number of atoms as contained in the molecular graph, which does not align with real-world facts. In this study, a molecular fragment representation learning framework for drug-drug interaction prediction is introduced. Initially, a fragment extraction module is designed to acquire a series of molecular fragments. Subsequently, to capture more comprehensive features, molecular hierarchical information is effectively integrated, enabling drug-drug interaction prediction by identifying pairwise interactions between molecular fragments of each drug. Comprehensive evaluations demonstrate that the proposed method achieved state-of-the-art performance in both DrugBank and Twosides datasets, particularly achieving an improved accuracy of over 20% for unseen drugs in both two datasets. Furthermore, case studies and visual analysis confirm that the proposed method can accurately identify crucial substructures influencing the interactions, which are basically consistent with functional group structures in reality. In conclusion, this method not only enhances the performance of drug-drug interaction prediction but also offers high interpretability. Source code is freely available at https://github.com/kennysyp/MFR-DDI .

The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis

Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.

Comparing Important and Well-documented Potential Drug–Drug Interactions between Emergency, Medical, and Surgical ICUs of a Respiratory Referral Center

Introduction

Drug–drug interaction (DDI) is one of the major healthcare challenges in intensive care units (ICUs). The prevalence of DDIs and interacting drug pairs may vary between different types of ICUs. This study aimed to compare the frequency and nature of important and well-documented potential DDIs (pDDIs) in three types of ICUs.

Materials and methods

A prospective study was conducted in medical (M), surgical (S), and emergency (E) ICUs of a tertiary referral center for respiratory diseases. A pharmacist checked the patients’ files three days in a week for 6 months. The pDDIs were identified using the Lexi-Interact database. Interactions with a severity rating of D (modify regimen) and X (avoid combination) and with a reliability rating of good and excellent were considered important and well-documented. These pDDIs were evaluated in terms of drug combinations, mechanisms of interaction, and clinical management.

Results

One hundred eighty-nine patients admitted to MICU, SICU, and EICU were included in the study. The percentage of patients who experienced at least one important and well-documented pDDI was 18.8% in MICU, 11.1% in SICU, and 11.8% in EICU. The most common drug pairs causing important and well-documented interactions were atracurium + hydrocortisone in MICU, meropenem + valproic acid in MICU and EICU, and aspirin + warfarin in SICU.

Conclusion

The current study shows different frequency and nature of pDDIs between three types of ICUs. We recommend conducting similar studies in other settings to develop evidence-based guidance on clinically relevant pDDIs in different types of ICUs.

How to cite this article

Hosseinpoor Z, Farzanegan B, Baniasadi S. Comparing Important and Well-documented Potential Drug–Drug Interactions between Emergency, Medical, and Surgical ICUs of a Respiratory Referral Center. Indian J Crit Care Med 2022;26(5):574–578.

On the road to explainable AI in drug-drug interactions prediction: A systematic review

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.

Metabolomics reveals the role of PPARα in Tripterygium Wilfordii-induced liver injury

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium glycosides tablets (TGT) and Tripterygium wilfordii tablets (TWT) have been used to treat autoimmune diseases clinically, however, the side effects of TWT are higher than TGT, especially for hepatotoxicity.

THE AIM OF THE STUDY

This study aims to determine the mechanism of TWT-induced liver injury.

MATERIALS AND METHODS

We performed metabolomic analysis of samples from mice with liver injury induced by TGT and TWT. Ppara-null mice were used to determine the role of PPARα in TWT-induced liver injury.

RESULTS

The results indicated that TWT induced the accumulation of medium- and long-chain carnitines metabolism, which was associated with the disruption of PPARα-IL6-STAT3 axis. PPARα agonists fenofibrate could reverse the liver injury from TWT and TP/Cel, and its protective role could be attenuated in Ppara-null mice. The toxicity difference of TWT and TGT was due to the different ratio of triptolide (TP) and celastrol (Cel) in the tablet in which TP/Cel was lower in TWT than TGT. The hepatotoxicity induced by TP and Cel also inhibited PPARα and upregulated IL6-STAT3 axis, which could be alleviated following by PPARα activation.

CONCLUSIONS

These results indicated that PPARα plays an important role in the hepatotoxicity of Tripterygium wilfordii, and PPARα activation may offer a promising approach to prevent hepatotoxicity induced by the preparations of Tripterygium wilfordii.

Heterogeneity in the identification of potential drug-drug interactions in the intensive care unit: A systematic review, critical appraisal, and reporting recommendations

Patients admitted to the intensive care unit (ICU) are frequently exposed to potential drug‐drug interactions (pDDIs). However, reported frequencies of pDDIs in the ICU vary widely between studies. This can be partly explained by significant variation in their methodological approach. Insight into methodological choices affecting pDDI frequency would allow for improved comparison and synthesis of reported pDDI frequencies. This study aimed to evaluate the association between methodological choices and pDDI frequency and formulate reporting recommendations for pDDI frequency studies in the ICU. The MEDLINE database was searched to identify papers reporting pDDI frequency in ICU patients. For each paper, the pDDI frequency and methodological choices such as pDDI definition and pDDI knowledge base were extracted, and the risk of bias was assessed. Each paper was categorized as reporting a low, medium, or high pDDI frequency. We sought associations between methodological choices and pDDI frequency group. Based on this comparison, reporting recommendations were formulated. Analysis of methodological choices showed significant heterogeneity between studies, and 65% of the studies had a medium to high risk of bias. High risk of bias, small sample size, and use of drug prescriptions instead of administrations were related to a higher pDDI frequency. The findings of this review may support researchers in designing a reliable methodology assessing pDDI frequency in ICU patients. The reporting recommendations may contribute to standardization, comparison, and synthesis of pDDI frequency studies, ultimately improving knowledge about pDDIs in and outside the ICU setting.

The Prevalence of Drug-Drug Interactions with Antiretroviral Therapy in Human Immunodeficiency Virus-Infected Patients in the Intensive Care Unit

Background: Persons living with human immunodeficiency virus (HIV) (PWH) on antiretroviral therapy (ART) are frequently admitted to the intensive care unit (ICU). Persons living with HIV on ART may be at higher risk for potential drug-drug interactions (pDDIs) due to polypharmacy in the ICU. We determined the prevalence of pDDI with ART in critically ill PWH. Objectives: The primary outcome was prevalence of pDDI between ART and ICU medications. Secondary outcomes included pDDI per ICU admission, pDDI severity, ICU, and hospital length of stay (LOS). Methods: A single-center, retrospective cohort evaluating PWH ≥ 18 years old admitted to the ICU for > 24 hours who received ART during ICU admission, between January 2013 and 2015 at a tertiary care hospital in the United States. Each ICU admission was counted as a separate encounter. Medication databases and chart review were used to identify pDDI. Results: We included 77 PWH encounters; mean age was 55 ± 9 years and 65% were male. We identified 208 pDDIs among 53/77 (68.8%), with a mean 4 ± 2 pDDI per ICU admission. Antipsychotics (20%), analgesics (20%), and anti-lipemics (11%) were the most common ICU medications with ART-related pDDI. Of the pDDI, 64% were major, 24% moderate, and 12% contraindicated. Median ICU and hospital LOS were 4 days (IQR: 3-5) and 11 days (IQR: 7-31), respectively. Conclusion: Most PWH had at least one pDDI during ICU admission. Collaborations among pharmacists, intensivists, and infectious disease/HIV specialists to develop effective, actionable strategies, such as electronic health record alerts, could reduce pDDIs for PWH on ART in the ICU.

Real-life drug-drug and herb-drug interactions in outpatients taking oral anticancer drugs: comparison with databases

PURPOSE

Due to polypharmacy and the rising popularity of complementary and alternative medicines (CAM), oncology patients are particularly at risk of drug-drug interactions (DDI) or herb-drug interactions (HDI). The aims of this study were to assess DDI and HDI in outpatients taking oral anticancer drug.

METHOD

All prescribed and non-prescribed medications, including CAM, were prospectively collected by hospital pharmacists during a structured interview with the patient. DDI and HDI were analyzed using four interaction software programs: Thériaque^®, Drugs.com^®, Hédrine, and Memorial Sloan Kettering Cancer Center (MSKCC) database. All detected interactions were characterized by severity, risk and action mechanism. The need for pharmaceutical intervention to modify drug use was determined on a case-by-case basis.

RESULTS

294 patients were included, with a mean age of 67 years [55-79]. The median number of chronic drugs per patient was 8 [1-29] and 55% of patients used at least one CAM. At least 1 interaction was found for 267 patients (90.8%): 263 (89.4%) with DDI, 68 (23.1%) with HDI, and 64 (21.7%) with both DDI and HDI. Only 13% of the DDI were found in Thériaque^® and Drugs.com^® databases, and 125 (2.5%) were reported with similar level of risk on both databases. 104 HDI were identified with only 9.5% of the interactions found in both databases. 103 pharmaceutical interventions were performed, involving 61 patients (20.7%).

CONCLUSION

Potentially clinically relevant drug interaction were frequently identified in this study, showing that several databases and structured screening are required to detect more interactions and optimize medication safety.

Comparing Potential Drug-Drug Interactions in Companion Animal Medications Using Two Electronic Databases

Multiple-drug prescriptions can cause drug–drug interactions (DDIs), which increase risks associated with healthcare in veterinary medicine. Moreover, many human medicines are used in canine patients under the responsibility of veterinarians and may cause severe problems due to off-label use. Currently, many electronic databases are being used as tools for potential DDI prediction, for example, Micromedex and Drugs.com, which may benefit the prediction of potential DDIs for drugs used in canine. The purpose of this study was to examine different abilities for the identification of potential DDIs in companion animal medicine, especially in canine patients, by Micromedex and Drugs.com. Micromedex showed 429 pairs of potential DDIs, while Drugs.com showed 842 pairs of potential DDIs. The analysis comparing results between the two databases showed 139 pairs (12.28%) with the same severity and 993 pairs (87.72%) with different severities. The major mechanisms of contraindicated and major potential DDIs were cytochrome P450 induction–inhibition and QT interval prolongation. Veterinarians should interpret potential DDIs from several databases with caution and keep in mind that the results might not be reliable due to differences in sensitivity to drugs, drug-metabolizing enzymes, and elimination pathway between animals and humans.

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.

Drug-drug interactions in neonatal intensive care: A prospective cohort study

BACKGROUND

To characterize the prevalence and profile of drug-drug interactions (DDIs), the drugs most related to major DDIs and risk factors of their prescription in a neonatal intensive care unit (NICU).

METHODS

Neonates admitted to a NICU who had at least one medication prescribed and a hospital stay >24 h were included in a prospective cohort study (August 2017 to July 2018). All medications prescribed during the hospitalization were collected from all neonates (n = 220), with the screening for DDIs. Prevalence and type of DDIs was identified. Network analysis was used to identify the drugs more implicated with DDIs. Logistic regression was used for the analysis of risk factors (p < 0.05).

RESULTS

Over 70% of neonates were exposed to DDIs and 29% were exposed to major DDIs. The network analysis identified furosemide, fentanyl, aminophylline and fluconazole as most implicated with DDI, fentanyl was especially associated with major DDIs. The number of drugs (OR 1.60, p < 0.01), caesarean delivery (OR 2.68, p < 0.05), gestational age (OR 1.03, p < 0.01) and APGAR score (OR 0.78, p < 0.01) were identified as risk factors for exposure to DDI.

CONCLUSION

Neonates in intensive care have a high exposure to DDIs and the occurrence of major DDIs is related specifically to the prescription of fentanyl. The number of prescribed drugs, gestational age, cesarean delivery and low APGAR score in the first minute were identified as risk factors for DDIs in NICU.

Assessment of Physician's Knowledge of Potential Drug-Drug Interactions: An Online Survey in China

Background: Drug interactions are the most common preventable cause of adverse drug reaction, which may result in drug toxicity or undesired therapeutic effect with harmful outcomes to patients. Given the rising use of combination therapies, the main objectives of this study were to estimate the degree to which physicians can identify potential drug-drug interactions (PDDIs) correctly and to describe the common source of information used by physicians when they need to check PDDIs. Methods: A cross-sectional survey utilizing a self-administered online questionnaire was conducted among physicians in China. Participants were asked to classify 20 drug pairs as "no interaction," "may be used together with monitoring," "contraindication," and "not sure." We also collected data on the physician's source of information and altitude toward the PDDIs. An ordinary least square regression model was performed to investigate the potential predictors of PDDI knowledge. Results: Eligible questionnaires were obtained from 618 physicians. The respondents classified correctly 6.7 out of 20 drug pairs, or 33.4% of the drug interactions investigated. The number of drug pairs recognized by respondents was ranged from 0 to 16. The percentage of physicians who recognized specific drug pairs ranged from 8.3% for no interactions between conjugated estrogens and raloxifene, to 64.0% for the interaction between dopamine and phenytoin. When the respondents want to check PDDI information, the most commonly used source of information was package inserts (n = 572, 92.6%), followed by the Internet or mobile Apps (n = 424, 68.6%), consultation with clinical pharmacists (n = 384, 62.1%), medical textbooks (n = 374, 60.5%), knowledge base in Chinese (n = 283, 45.8%), and other physicians (n = 366, 59.2%). In the multiple regression analysis, the significant predictors of a higher number of recognized drug pairs were years of practice and altitudes toward PDDIs. Conclusion: In this online survey accessing physician's ability to detect PDDIs, less than half of the drug pairs were recognized, indicating unsatisfactory level of knowledge about the clinically significant drug interactions. Continuing education and accessible electronic database can help physicians detecting PDDIs and improve drug safety.

Clinically relevant potential drug-drug interactions in intensive care patients: A large retrospective observational multicenter study

PURPOSE

Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all pDDIs are clinically relevant. Clinical decision support systems (CDSSs) warning for irrelevant pDDIs could result in alert fatigue and overlooking important signals. Therefore, our aim was to describe the frequency of clinically relevant pDDIs (crpDDIs) to enable tailoring of CDSSs to the ICU setting.

MATERIALS & METHODS

In this multicenter retrospective observational study, we used medication administration data to identify pDDIs in ICU admissions from 13 ICUs. Clinical relevance was based on a Delphi study in which intensivists and hospital pharmacists assessed the clinical relevance of pDDIs for the ICU setting.

RESULTS

The mean number of pDDIs per 1000 medication administrations was 70.1, dropping to 31.0 when considering only crpDDIs. Of 103,871 ICU patients, 38% was exposed to a crpDDI. The most frequently occurring crpDDIs involve QT-prolonging agents, digoxin, or NSAIDs.

CONCLUSIONS

Considering clinical relevance of pDDIs in the ICU setting is important, as only half of the detected pDDIs were crpDDIs. Therefore, tailoring CDSSs to the ICU may reduce alert fatigue and improve medication safety in ICU patients.

Prevalence of drug interactions in hospitalised elderly patients: a systematic review

BACKGROUND

The prevalence of drug-drug interactions (DDIs) in hospital settings is variable, and elderly patients are considered a high risk population for DDIs. There are no systematic reviews describing the prevalence of DDIs in hospitalised elderly patients.

OBJECTIVES

To assess and summarise the available data on the prevalence of DDIs in hospitalised elderly patients and to describe which drugs, drug classes and drug combinations are most commonly involved in DDIs.

DATA SOURCE

A systematic electronic literature search was conducted on Medline/PubMed, Embase, Lilacs, SciElo, Web of Science, Cinahl, Scopus, Cochrane, OpenGrey, Capes Thesis Bank, OasisBR, OpenAire and abstracts from scientific events, without limitation on language or period of publication. Study selection was completed on 21 September 2018.

STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS

Original observational studies that reported the prevalence of actual or potential DDIs during hospitalisation in patients aged 60 years or older were included. The main outcome measure was prevalence of DDIs and number of DDIs per patient. Subgroup analysis was performed in studies that reported the prevalence of DDIs in geriatric units.

STUDY APPRAISAL AND SYNTHESIS METHODS

Study quality was assessed using the Agency for Healthcare Research and Quality methodological checklist for cross sectional and prevalence studies.

RESULTS

34 studies were included, involving 9577 patients. The prevalence of DDIs ranged from 8.34% to 100%. In studies conducted in geriatric units, the prevalence ranged from 80.5% to 90.5%. The number of DDIs per patient ranged from 1.2 to 30.6. Single drugs most commonly involved in DDIs were furosemide, captopril, warfarin and dipyrone. Drug classes mostly involved were potassium sparing diuretics and angiotensin converting enzyme inhibitors.

LIMITATIONS

The main limitation is the heterogeneity between the included studies that precluded a meta-analysis. Several different methods were used to identify DDIs, majorly, and potential DDIs. Few studies have reported measures to control the quality of the collected data.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

The prevalence of DDIs ranged widely, and the variation may reflect differences in the conditions of the elderly patients and level of attention (or complexity of care), as well as methodological differences, especially the methods and/or software used to identify DDIs.

SYSTEMATIC REVIEW REGISTRATION NUMBER

CRD42018096720.

Integrating a pharmacist into an anaesthesiology and critical care department: Is this worthwhile?

Background Operating rooms and Intensive Care Units are places where an optimal management of drugs and medical devices is required. Objective To evaluate the impact of a dedicated pharmacist in an academic Anaesthesiology and Critical Care Department. Setting This study was conducted in the Anaesthesiology and Critical Care Department of Grenoble University Hospital. Method Between November 2013 and June 2017, the drug-related problems occurring in three Intensive Care Units and their corrections by a full-time clinical pharmacist were analyzed using a structured order review instrument. Pharmaceutical costs in the Anaesthesiology and Critical Care Department were analyzed over a 7 year period (2010-2016), during which automated dispensing systems and recurrent meetings to review indications of medications and medical devices were implemented in the department. Main outcome measure Analysis of two issues: correcting drug-related problems and containing pharmaceutical costs. Results A total of 324 drug-related problems were identified. The most frequent problem concerned anti-infective agents (45%), and this was mainly due to the over-dosage of drugs (30%). Dosage adjustments were the most frequent interventions performed by the pharmacist (43%). Over the 7 year period, pharmaceutical costs decreased by 9% (€365,469), while the care activity of the department increased by 55% (+ 12,022 surgical procedures and + 1424 admissions in the ICU). Conclusion Integrating a pharmacist into the Anaesthesiology and Critical Care Department was associated with interventions to correct drug-related problems and containing pharmaceutical costs. Pharmacists should play a central role in such medical environments, to optimize the use of drugs and medical devices.

The usability aspects of medication-related decision support systems in the inpatient setting: A systematic review

Effort has been made to study the effect of medication-related clinical decision support systems in the inpatient setting; however, there is not much known about the usability of these systems. The goal of this study is to systematically review studies that focused on the usability aspects such as effectiveness, efficiency, and satisfaction of these systems. We systematically searched relevant articles in Scopus, Embase, and PubMed from 1 January 2000 to 1 January 2016, and found 22 articles. Based on Van Welie’s usability model, we categorized usability aspects in terms of usage indicators and means. Our results showed that evidence was mainly found for effectiveness and efficiency. They showed positive results in the usage indicators errors and safety and performance speed. The means warnings and adaptability also had mostly positive results. To date, the effects satisfaction of clinical decision support system remains understudied. Aspects such as memorability, learnability, and consistency require more attention.

Prescription of proton pump inhibitors in older adults with complex polytherapy

OBJECTIVES

Prescription of proton pump inhibitors (PPIs) may be a source of potentially clinically relevant drug-drug interactions (DDIs) and related complications for elderly patients with complex polytherapy at discharge from hospital. The aim of the study was to identify, through the analysis of hospital discharge records, the co-administrations (PPIs + one or more drugs potentially generating DDIs) hypothetically leading to severe consequences according to the literature and online databases. Subsequently, alternatives to PPIs were evaluated for the treatment of gastric acidity and ulcers.

METHODS

The medical records of 1288 patients, discharged from a geriatric ward at the Città della Salute e della Scienza Hospital in Turin from January 2012 to December 2013, were collected in an Excel database for analysis of DDIs using the literature and online sources such as Micromedex. RESULTS : Six hundred and sixty-three of the 1288 clinical folders had a PPI prescription. A list of 18 drugs considered potentially hazardous and able to trigger a DDI when co-administrated with PPIs was drafted; the frequencies of the co-prescriptions of each PPI with one of the listed drugs were esomeprazole 65.38%, lansoprazole 52.87%, omeprazole 48.19% and pantoprazole 37.11%. An analysis of these co-prescriptions, according to Micromedex classification, gave a percentage of major interactions of 11.01% over 663 clinical folders including a PPI.

CONCLUSIONS

This study provides a collection of potentially hazardous drug associations and helpful suggestions to improve the quality of prescriptions for elderly patients and strengthens the case for synergic work between doctors and pharmacists in the wards.

Usability aspects of medication-related decision support systems in the outpatient setting: A systematic literature review

In this study, we evaluated the usability aspects of medication-related clinical decision support systems in the outpatient setting. Articles published between 2000 and 2016 in Scopus, PubMed and EMBASE were searched and classified into three usability aspects: Effectiveness, Efficiency and Satisfaction. Using Van Welie et al.’s usability model, we categorized usability aspects in terms of usage indicators and means. Out of the 1999 articles, 24 articles met the selection criteria of which the main focus was on reducing inappropriate medication, prescription rate and prescription errors. Evidence could mainly be found for Effectiveness and showed high rates of positive results in reducing medication errors. To date, the effects of Efficiency and Satisfaction of clinical decision support systems regarding medication prescription remain understudied. Usability aspects such as memorability, learnability, adaptability, shortcuts and consistency require more attention. Studies are needed for better insight into the user model and to design a knowledge/task model for clinical decision support systems regarding medication prescription.

Characterization of potential drug-drug interactions in patients hospitalized in the intensive care unit of a tertiary hospital in Bogotá

Introduction: Drug-drug interactions occur more frequently in intensive care units than in other services. However, in Colombia, there are few studies on this problem in critically ill patients. Objectives: To characterize potential drug-drug interactions generated from prescriptions during hospitalization in an intensive care unit and to determine factors associated with their onset. Materials and methods: A retrospective cohort was assembled with patients hospitalized in an intensive care unit for a seven-month period. The daily prescription was assessed for potential drugdrug interactions using the Lexicomp® program. We calculated the incidence of interactions, classified them by type, severity, and level of documentation, and evaluated the factors associated with their onset using logistic regression. Results: The proportion of patients with at least one interaction was 84% while 87% had more than one interaction; the median was six interactions per patient. The most frequent was fentanyl and midazolam (23%). Moderate interactions were the most frequent by severity (77.6%) and by documentation (52.6%). The most common index and precipitating drugs were midazolam (12%) and fentanyl (10.6%), respectively. Age (OR=3.1) and the number of drugs (OR=11.8) were associated with the occurrence of interactions. Conclusions: Given their high frequency and potential negative impact, the systematic monitoring of prescriptions in intensive care units to detect interactions is important. Such monitoring contributes to the rational use of medicines and to improve the quality of care.

Patient safety climate in general public hospitals in China: differences associated with department and job type based on a cross-sectional survey

OBJECTIVE

This study analysed differences in the perceived patient safety climate among different working departments and job types in public general hospitals in China.

DESIGN

Cross-sectional survey.

SETTING

Eighteen tertiary hospitals and 36 secondary hospitals from 10 areas in Shanghai, Hubei Province and Gansu Province, China.

PARTICIPANTS

Overall, 4753 staff, including physicians, nurses, medical technicians and managers, were recruited from March to June 2015.

MAIN OUTCOME MEASURE

The Patient Safety Climate in Healthcare Organisations (PSCHO) tool and the percentages of 'problematic responses' (PPRs) were used as outcome measures. Multivariable two-level random intercept models were applied in the analysis.

RESULTS

A total of 4121 valid questionnaires were collected. Perceptions regarding the patient safety climate varied among departments and job types. Physicians responded with relatively more negative evaluations of 'organisational resources for safety', 'unit recognition and support for safety efforts', 'psychological safety', 'problem responsiveness' and overall safety climate. Paediatrics departments, intensive care units, emergency departments and clinical auxiliary departments require more attention. The PPRs for 'fear of blame and punishment' were universally significantly high, and the PPRs for 'fear of shame' and 'provision of safe care' were remarkably high, especially in some departments. Departmental differences across all dimensions and the overall safety climate primarily depended on job type.

CONCLUSIONS

The differences suggest that strategies and measures for improving the patient safety climate should be tailored by working department and job type.

Risk factors for potential drug-drug interactions in intensive care unit patients

PURPOSE

To determine risk factors for each severity-based category of potential drug-drug interactions (DDIs) encountered at intensive care unit (ICU) patients.

METHODS

This was a retrospective cohort analysis of patients treated at the ICU of the Clinical Center Kragujevac, a public tertiary care hospital in Kragujevac, Serbia. Three interaction checkers were used to reveal drug-drug interactions: Medscape, Epocrates and Micromedex.

RESULTS

The study included 201 patients, 66.19±16.11 years of age. Average number of DDIs per patient ranged from 10.49±8.80 (Micromedex) to 29.43±21.51 (Medscape). Antiarrhythmic or anticonvulsant drug prescription, Charlson Comorbidity Index, male sex, length of hospitalization, number of drugs or therapeutic groups prescribed and surgery increased the risk of DDIs in ICU patients, while presence of delirium or dementia and transfer from emergency department to ICU protected against.

CONCLUSIONS

The rate of the DDIs in ICU patients at a tertiary care hospital is high, and adversely influenced by number of drugs or drug groups prescribed per patient, antiarrhythmic or anticonvulsant drug prescription, comorbidities, length of hospitalization and surgery. On the other hand, presence of cognitive deficit and transfer from emergency department to ICU protect ICU patients from the DDIs.

Clinically relevant potential drug-drug interactions among outpatients: A nationwide database study

BACKGROUND

Adverse drug events due to drug-drug interactions (DDIs) represent a considerable public health burden, also in Slovenia. A better understanding of the most frequently occurring potential DDIs may enable safer pharmacotherapy and minimize drug-related problems.

OBJECTIVES

The aim of this study was to evaluate the prevalence and predictors of potential DDIs among outpatients in Slovenia.

METHODS

An analysis of potential DDIs was performed using health claims data on prescription drugs from a nationwide database. The Lexi-Interact Module was used as the reference source of interactions. The influence of patient-specific predictors on the risk of potential clinically relevant DDIs was evaluated using logistic regression model.

RESULTS

The study population included 1,179,803 outpatients who received 15,811,979 prescriptions. The total number of potential DDI cases identified was 3,974,994, of which 15.6% were potentially clinically relevant. Altogether, 9.3% (N = 191,213) of the total population in Slovenia is exposed to clinically relevant potential DDIs, and the proportion is higher among women and the elderly. After adjustment for cofactors, higher number of medications and older age are associated with higher odds of clinically relevant potential DDIs. The burden of DDIs is highest with drug combinations that increase risk of bleeding, enhance CNS depression or anticholinergic effects or cause cardiovascular complications.

CONCLUSION

The current study revealed that 1 in 10 individuals in the total Slovenian population is exposed to clinically relevant potential DDIs yearly. Taking into account the literature based conservative estimate that approximately 1% of potential DDIs result in negative health outcomes, roughly 1800 individuals in Slovenia experience an adverse health outcome each year as a result of clinically relevant potential interactions alone.

Potential drug-drug interactions in pediatric patients admitted to intensive care unit of Khyber Teaching Hospital, Peshawar, Pakistan: A cross-sectional study

PURPOSE

To investigate frequencies, levels, clinical relevance and predictors of potential drug-drug interactions (pDDIs) in pediatric intensive care unit (PICU).

METHODS

Case notes of 411 patients were reviewed for pDDIs through Micromedex. Frequencies, levels and clinical relevance of pDDIs were reported. Logistic regression was applied to calculate the odds-ratios for predictors of pDDIs.

RESULTS

We recorded pDDIs in 59.4% patients. Major-pDDIs were found in 34.5% patients. Total 990 pDDIs were identified, of which, 37.8% were of moderate-severity and 30.6% of major-severity. Patient's case notes of top-ten pDDIs showed presence of signs/symptoms such as fever, jaundice, vomiting, anorexia, tachycardia, drowsiness, & lethargy; and abnormalities in labs such as total leukocytes count, blood urea nitrogen, alanine aminotransferase, & potassium-level. Odds of exposure to major-pDDIs were significantly higher in patients aged 6-12years (p=0.008); hospital stay of ≥7days (p=0.05); and ≥11 prescribed medicines (p<0.001).

CONCLUSION

Substantial numbers of patients in PICU are exposed to pDDIs. Major-pDDIs are of particular concern. Timely identification of pDDIs, preferably with computerized source, is crucial point for their management. Monitoring of clinically relevant parameters and identification of various predictors are needed to minimize or prevent the associated negative consequences of pDDIs.

Comparative analysis of three drug-drug interaction screening systems against probable clinically relevant drug-drug interactions: a prospective cohort study

PURPOSE

Drug-drug interaction (DDI) screening systems report potential DDIs. This study aimed to find the prevalence of probable DDI-related adverse drug reactions (ADRs) and compare the clinical usefulness of different DDI screening systems to prevent or warn against these ADRs.

METHODS

A prospective cohort study was conducted in patients urgently admitted to medical departments. Potential DDIs were checked using Complete Drug Interaction®, Lexicomp® Online™, and Drug Interaction Checker®. The study team identified the patients with probable clinically relevant DDI-related ADRs on admission, the causality of which was assessed using the Drug Interaction Probability Scale (DIPS). Sensitivity, specificity, and positive and negative predictive values of screening systems to prevent or warn against probable DDI-related ADRs were evaluated.

RESULTS

Overall, 50 probable clinically relevant DDI-related ADRs were found in 37 out of 795 included patients taking at least two drugs, most common of them were bleeding, hyperkalemia, digitalis toxicity, and hypotension. Complete Drug Interaction showed the best sensitivity (0.76) for actual DDI-related ADRs, followed by Lexicomp Online (0.50), and Drug Interaction Checker (0.40). Complete Drug Interaction and Drug Interaction Checker had positive predictive values of 0.07; Lexicomp Online had 0.04. We found no difference in specificity and negative predictive values among these systems.

CONCLUSION

DDI screening systems differ significantly in their ability to detect probable clinically relevant DDI-related ADRs in terms of sensitivity and positive predictive value.

A Prospective Analysis of Drug Interactions in Patients of Intensive Cardiac Care Unit

INTRODUCTION

Drug-Drug Interaction (DDI) is a serious concern in cardiac patients due to polypharmacy.

AIM

The present study was aimed to identify the potential DDI among hospitalized cardiac patients and evaluate the mechanism and severity of such interactions.

MATERIALS AND METHODS

A prospective observational study was conducted in intensive cardiac care unit of a tertiary care hospital for six months. Patients aged 18 years and above and taking two or more drugs were included in the study. Medscape drug interaction checker was used to identify and analyze the pattern of potential DDI.

RESULTS

Out of 500 patients, most of the patients were male (78.4%) in the age group of 50-60 years (31%). The most common diagnosis was acute coronary syndrome (57.2%). Out of total 2849 DDI, 2194 (77.01%) were pharmacodynamic, 586 (20.57%) were pharmacokinetic in nature while 69 (2.42%) drug pairs interacted by unknown mechanism. Majority of drug interactions were significant {2031 (71.29%)} in nature followed by minor {725(25.45%)} while serious drug interactions were observed in only 93 (3.26%) drug pairs. A positive correlation was observed between patient's age and number of drugs prescribed (r=0.178, p<0.001), number of drugs prescribed and potential Drug-Drug Interaction (pDDI) (r= 0.788, p<0.001) and between patient's age and pDDI (r=0.338, p<0.001).

CONCLUSION

The risk of pDDI was more commonly observed in elderly male patients particularly with antiplatelet drugs like low dose aspirin and clopidogrel.

Adverse Drug Reactions in the Intensive Care Unit

Adverse drug reactions (ADRs) are undesirable effects of medications used in normal doses [1]. ADRs can occur during treatment in an intensive care unit (ICU) or result in ICU admissions. A meta-analysis of 4139 studies suggests the incidence of ADRs among hospitalized patients is 17% [2]. Because of underreporting and misdiagnosis, the incidence of ADRs may be much higher and has been reported to be as high as 36% [3]. Critically ill patients are at especially high risk because of medical complexity, numerous high-alert medications, complex and often challenging drug dosing and medication regimens, and opportunity for error related to the distractions of the ICU environment [4]. Table 1 summarizes the ADRs included in this chapter.

Drug-drug interactions in the intensive care unit: Do they really matter?

PURPOSE

To describe prevalence and patterns of potential drug-drug interactions (pDDIs) in the intensive care unit (ICU), occurrence of adverse drug events (ADEs), and agreement between different compendia and intensivists' perceptions.

METHODS

A cross-sectional study. Drug profiles of all adult patients from 2 academic ICUs were screened on day 3 upon admission. We identified pDDIs using 3 compendia (Stockley's, Micromedex, and Epocrates) and documented their mechanism of action, clinical consequences, severity, level of evidence, and management. Medical records were searched to identify ADEs potentially related to major pDDIs. Agreement between information sources (compendia, intensivists) was evaluated.

RESULTS

We identified 1120 pDDIs among 275 patients. Median number of pDDIs per patient was 3.0 (interquartile range, 1-6), with 79% of patients presenting with at least 1 pDDI. Major pDDIs were detected in 18% of patients, with potentially related to ADEs in 4% of patients. Only 13% of all pDDIs were documented simultaneously in all 3 compendia. Different information sources (compendia, clinicians) showed "no" to "fair" agreement.

CONCLUSIONS

Potential drug-drug interactions occurred in most ICU patients, contrasting with low rates of potentially related ADEs, which may have been underestimated. Sources of information are inconsistent, challenging the identification of pDDIs.

Epidemiology of Polypharmacy and Potential Drug-Drug Interactions Among Pediatric Patients in ICUs of U.S. Children's Hospitals

OBJECTIVES

Polypharmacy is common in hospitalized children in the United States and has been identified as a major risk factor for exposure to potential drug-drug interactions. Little is known about the characteristics and prevalence of exposure of pediatric patients to polypharmacy and potential drug-drug interactions in PICUs.

DESIGN

Retrospective cohort study using the Pediatric Health Information System database.

SETTING

Forty-two freestanding children's hospitals throughout the United States.

PATIENTS

A total of 54,549 patients less than 18 years old cared for in PICUs in 2011. Patients in neonatal ICUs were not included.

MEASUREMENTS AND MAIN RESULTS

PICU patients were on average exposed to 10 distinct drugs each hospital day and to 20 drugs cumulatively during their hospitalization. Seventy-five percent of patients were exposed to greater than or equal to one potential drug-drug interaction regardless of severity level, 6% to greater than or equal to one contraindicated potential drug-drug interaction, 69% to greater than or equal to one major potential drug-drug interaction, 57% to greater than or equal to one moderate potential drug-drug interaction, 19% to greater than or equal to one minor potential drug-drug interaction. Potential drug-drug interaction exposures were significantly associated with specific diagnoses (p < 0.001), presence of complex chronic conditions (p < 0.001), increasing number of total distinct drugs used (p < 0.001), increasing length of stay in PICU (p < 0.001), and white race (p < 0.001).

CONCLUSIONS

Many PICU patients are exposed to substantial polypharmacy and potential drug-drug interactions. Future research should identify the risk of adverse drug events following specific potential drug-drug interaction exposures, especially the risk of adverse drug events due to multiple potential drug-drug interaction exposures, and determine the probability and magnitude of the actual harm (if any) for each specific potential drug-drug interaction, especially for multiple potential drug-drug interaction exposures.

Potential drug-drug interactions in a medical intensive care unit of a university hospital

BACKGROUND/AIM

Drug-drug interactions (DDIs) can impact patient safety. Occurrence of clinically important DDIs is higher for intensive care unit (ICU) patients. This observational study aimed to evaluate the potential DDIs in medical ICU patients of a university hospital.

MATERIALS AND METHODS

The Medical Pharmacology Department organized consultation reports for ICU patients in order to detect the DDIs. To focus on clinically important DDIs, interactions in the C, D, or X risk rating categories of the Lexi-Interact online database were analyzed. Frequency and clinical risk rating categories of DDIs were detected. Relationship between number of prescriptions and DDIs were assessed. The most frequent drug/drug groups were identified.

RESULTS

Of 101 ICU patients, 45.5% were found to have DDIs. We detected 125 C (72.2%), 37 D (21.4%), and 11 X (6.4%) risk category interactions. A statistically significant increase in the number of DDIs was shown with the number of prescriptions (P = 0.002). The most frequent DDIs were between agents acting on the cardiovascular system and corticosteroids (12.8%).

CONCLUSION

Results of this study show that pharmacological consultation plays a critical role in the recognition of DDIs for improvement of medication management and effective therapeutic endpoints without any adverse or toxic reactions.

Drug-Drug Interactions Among Hospitalized Children Receiving Chronic Antiepileptic Drug Therapy

OBJECTIVES

Children treated with chronic medications are at risk of drug-drug interactions (DDIs) when hospitalized with an acute illness and prescribed new medications. We aimed to measure the prevalence of potential DDIs (pDDIs) among hospitalized children treated with antiepileptic drugs (AEDs) and to evaluate the impact of computerized physician order entry (CPOE) on pDDIs.

METHODS

We analyzed a national sample of pediatric hospitalizations from 2005 to 2012 associated with administration of an AED and identified those prescribed a second medication with risk of a DDI. The prevalence of hospitalizations associated with a pDDI was calculated for each AED. We identified the drugs most commonly implicated in pDDIs and factors associated with pDDIs. Rates of pDDIs were measured in pre- and post-CPOE implementation periods.

RESULTS

A pDDI was identified in 181 380 (41.7%) hospitalizations associated with the use of an AED, with 117 880 (27.1%) classified as severe pDDIs. AEDs most often implicated with a pDDI were phenobarbital, valproic acid, and phenytoin. Hospitalizations with pDDIs were associated with increased length of stay and a greater number of medications, ICU admissions, and operating room procedures. The implementation of CPOE did not result in a change in the rate of pDDIs (42.7% before versus 40.8% after; P = .48).

CONCLUSIONS

Children treated with AEDs are at risk of pDDIs while hospitalized. The use of CPOE has not been associated with a significant decrease in the rate of pDDIs. Additional investigation to better define the impact of pDDIs and to advance development of clinical decision support within CPOE systems is warranted.

Important drug classes associated with potential drug-drug interactions in critically ill patients: highlights for cardiothoracic intensivists

BACKGROUND

Patients in the intensive care unit (ICU) are more prone to drug-drug interactions (DDIs). The software and charts that indicate all interactions may not be proper for clinical usage. This study aimed to identify the main drug classes associated with clinically significant DDIs in cardiothoracic ICU and categorize DDIs to make cardiothoracic intensivists aware of safe medication usage.

METHODS

This prospective study was conducted over 6 months in a cardiothoracic ICU of a university-affiliated teaching hospital. The presence of potential drug-drug interactions (pDDIs) was assessed by a clinical pharmacologist using Lexi-Interact database. Clinically significant pDDIs were defined according to severity and reliability rating. Interacting drug classes, mechanisms, and recommendations were identified for each interaction.

RESULTS

From 1780 administered drugs, 496 lead to major (D) and contraindicated (X) interactions. Nine drug classes were responsible for D and/or X interactions with excellent (E) and/or good (G) reliability. Anti-infective agents (45.87 %) were the main drug classes that caused clinically significant pDDIs followed by central nervous system drugs (14.67 %). Azole antifungals as the most interacting antimicrobial agents precipitated metabolism inhibition of CYP3A substrates.

CONCLUSIONS

Clinically significant pDDIs as potential patient safety risks were prevalent in critically ill patients. The findings from current study help to improve knowledge and awareness of clinicians in this area and minimize adverse events due to pDDIs.

Drug interaction databases in medical literature: transparency of ownership, funding, classification algorithms, level of documentation, and staff qualifications. A systematic review

PURPOSE

It is well documented that drug-drug interaction databases (DIDs) differ substantially with respect to classification of drug-drug interactions (DDIs). The aim of this study was to study online available transparency of ownership, funding, information, classifications, staff training, and underlying documentation of the five most commonly used open access English language-based online DIDs and the three most commonly used subscription English language-based online DIDs in the literature.

METHODS

We conducted a systematic literature search to identify the five most commonly used open access and the three most commonly used subscription DIDs in the medical literature. The following parameters were assessed for each of the databases: Ownership, classification of interactions, primary information sources, and staff qualification. We compared the overall proportion of yes/no answers from open access databases and subscription databases by Fisher's exact test-both prior to and after requesting missing information.

RESULTS

Among open access DIDs, 20/60 items could be verified from the webpage directly compared to 24/36 for the subscription DIDs (p = 0.0028). Following personal request, these numbers rose to 22/60 and 30/36, respectively (p < 0.0001). For items within the "classification of interaction" domain, proportions were 3/25 versus 11/15 available from the webpage (P = 0.0001) and 3/25 versus 15/15 (p < 0.0001) available upon personal request.

CONCLUSION

Available information on online available transparency of ownership, funding, information, classifications, staff training, and underlying documentation varies substantially among various DIDs. Open access DIDs had a statistically lower score on parameters assessed.

Drug-drug interaction software in clinical practice: a systematic review

PURPOSE

Several electronic databases which report the prevalence of drug-drug interactions (DDIs) are used as a tool for evaluation of potentially harmful DDIs. The aim of our review was to evaluate the usability and appropriateness of commercially available electronic databases which assess the prevalence of potential DDIs.

METHODS

The systematic electronic literature search was conducted with the following search terms: "database" AND "software," and "drug-drug interactions" AND "database," and the inclusion and exclusion criteria were applied in order to identify the publications of interest.

RESULTS

A total of 3766 papers were identified by systematic search. After applying inclusion and exclusion criteria, 38 publications were included in the analysis. The most commonly used software in the included studies was Micromedex® Drug-Reax, for which some authors argue to be the most reliable due to highest sensitivity. It gives information about clinical consequences of DDIs, classifies underlying mechanism and onset of the adverse outcome (either rapid, or delayed) as well as severity (such as minor, moderate, or major), and provides the level of evidence which supports this information. This data is also provided by Drug Interaction Facts®, Lexi-Interact®, and Pharmavista®. A small number of studies which compared assessment of DDIs with electronic database and the clinician's assessment showed large discrepancy in number and relevance of detected DDIs. The overlap was in some cases as low as 11 %.

CONCLUSION

The deficiency of clinical relevance of detected DDIs should be addressed in the upcoming research as it would provide more relevant information to the prescribers' in clinical practice.

Clinical relevancy and risks of potential drug-drug interactions in intensive therapy

Purpose

Evaluate the potential Drug–Drug Interactions (pDDI) found in prescription orders of adult Intensive Care Unit (ICU) of a Brazilian public health system hospital; quantify and qualify the pDDI regarding their severity and risks to the critical patient, using the database from Micromedex®.

Methods

Prospective study (January–December of 2011) collecting and evaluating 369 prescription orders (convenient sampling), one per patient.

Results

During the study 1844 pDDIs were identified and distributed in 405 pairs (medication A × medication B combination). There was an average of 5.00 ± 5.06 pDDIs per prescription order, the most prevalent being moderate and important interactions, present in 74% and 67% of prescription orders, respectively. In total, there were 9 contraindicated, 129 important and 204 moderate pDDIs. Among them 52 had as management recommendation to “avoid concomitant use” or “suspension of medication”, while 306 had as recommendation “continuous and adequate monitoring”.

Conclusion

The high number of pDDIs found in the study combined with the evaluation of the clinical relevancy of the most frequent pDDIs in the ICU shows that moderate and important interactions are highly incident. As the majority of them demand monitoring and adequate management, being aware of these interactions is major information for the safe and individualized risk management.

Critical care nephrology: could it be a model of multidisciplinarity in ICU nowadays for other sub-specialities - the jury is out

Emergency and critical care medicine have grown into robust self-supporting disciplines with an increasing demand for dedicated highly-skilled physicians. In the past, “core” specialists were asked to offer bedside advice in acute care wards. In the same regard, critical care medicine and nephrology have been fighting but finally emerged altogether with the concept of critical care nephrology almost 20 years ago. Indeed, polyvalence is no longer a valid option in modern critical care. Uniting forces between disciplines represents the only way to cope with the increasing complexity and cumulating knowledge in the critical care setting. For this reason, the wide array of upcoming acute care sub-specialities must be committed to unrestricted growth and development. This will require competent manpower, a well-designed technical framework, and sufficient financial support. The worldwide success of critical care nephrology proves the feasibility for this concept.

Approaches to decreasing medication and other care errors in the ICU

PURPOSE OF REVIEW

The very complex process of intensive care is accompanied by a not unexpected accumulation of risk for error and adverse events. The present review addresses strategies to decrease care errors in several domains of daily intensive care practice.

RECENT FINDINGS

Strategies to decrease care errors now focus on a systematic approach by identifying latent system failures and change the design of the care process in such a way that inevitable human errors are prevented or their consequences are mitigated. Recent examples refer to the standardization of processes, adaptation to cognitive limitations of human beings, optimization of working conditions, and the increasing use of supporting information technologies. The development of a safety climate constitutes a key element and apparently contributes to reduction of medical errors in ICUs.

SUMMARY

The present review discusses recent approaches aimed to decrease care errors in ICUs. A growing body of evidence demonstrates that a system based approach with the change of process characteristics and the development of a safety climate is most essential in the effort to increase patient safety.

Analysis of potential drug-drug interactions in medical intensive care unit patients

OBJECTIVE

To describe the frequency and type of potential drug-drug interactions (pDDIs) in a general intensive care unit (ICU) and to make recommendations to improve the management of these pDDIs.

DESIGN

Retrospective observational study.

SETTING

General ICU of a tertiary care hospital.

SUBJECTS

All patients admitted for more than 24 hours between May 2009 and December 2010 who were prescribed at least one medication.

MEASUREMENT AND MAIN RESULTS

Based on the G-Standaard, the Dutch national drug database, pDDIs were identified and classified into categories of potential clinical outcome and management advice. In total, 35,784 medication episodes were identified, resulting in 2887 pDDIs (8.1%). These 2887 pDDIs occurred in 1659 patients for a mean frequency of 1.7 (95% confidence interval [CI] 1.6-1.9) pDDIs per patient. Overall, 54% of the patients experienced at least one pDDI with pDDIs present during 27% of all ICU admission days. All pDDIs could be reconstructed using 81 of the 358 (23%) relevant unique pDDI pairs described in the G-Standaard. The most frequently occurring potential clinical consequence was an increased risk of side effects or toxicity (91% of the pDDIs) such as electrolyte disturbances and masking of hypoglycemia. The most important advised management strategy was monitoring (81%), consisting of monitoring of laboratory values (52%), clinical monitoring of toxicity or effectiveness (48%), or monitoring of physical parameters such as electrocardiogram and blood pressure (11%).

CONCLUSION

Potential drug-drug interactions occur in 54% of all ICU patients, which is two times more than the rate seen in patients on general wards. A limited set of 20 pDDI pairs is responsible for more than 90% of all pDDIs. Therefore, it is worthwhile to develop guidelines for the management of these specific pDDIs. As the vast majority of the interactions can be managed by monitoring, advanced clinical decision support systems linking laboratory data to prescription data may be an effective risk management strategy.