Evaluation of Potential Drug–Drug Interactions in Adults in the Intensive Care Unit: A Systematic Review and Meta-Analysis

Drug-Drug Interactions and Actual Harm to Hospitalized Patients: A Multicentre Study Examining the Prevalence Pre- and Post-Electronic Medication System Implementation

INTRODUCTION

Drug-drug interactions (DDIs) have potential to cause patient harm, including lowering therapeutic efficacy. This study aimed to (i) determine the prevalence of potential DDIs (pDDIs); clinically relevant DDIs (cDDIs), that is, DDIs that could lead to patient harm, taking into account a patient's individual clinical profile, drug effects and severity of potential harmful outcome; and subsequent actual harm among hospitalized patients and (ii) examine the impact of transitioning from paper-based medication charts to electronic medication management (eMM) on DDIs and patient harms.

METHODS

This was a secondary analysis of the control arm of a controlled pre-post study. Patients were randomly selected from three Australian hospitals. Retrospective chart review was conducted before and after the implementation of an eMM system, without accompanying clinical decision support alerts for DDIs. Harm was assessed by an expert panel.

RESULTS

Of 1186 patient admissions, 70.1% (n = 831) experienced a pDDI, 42.6% (n = 505) a cDDI and 0.9% (n = 11) an actual harm in hospital. Of 15,860 pDDIs identified, 27.0% (n = 4285) were classified as cDDIs. The median number of pDDIs and cDDIs per 10 drugs were 6 [interquartile range (IQR) 2-13] and 0 (IQR 0-2), respectively. In cases where a cDDI was identified, both drugs were 44% less likely to be co-administered following eMM (adjusted odds ratio 0.56, 95% confidence interval 0.46-0.73).

CONCLUSION

Although most patients experienced a pDDI during their hospital stay, less than one-third of pDDIs were clinically relevant. The low prevalence of harm identified raises questions about the value of incorporating DDI decision support into systems given the potential negative impacts of DDI alerts.

Prevalence of polypharmacy and drug interaction in older adults with rheumatic disease

INTRODUCTION/AIM

Older people with rheumatic diseases tend to have a greater number of associated comorbidities, which will require the use of more drugs, increasing the risk of hospitalizations, complications, and drug interactions. In Mexico, there has been an estimated prevalence of polypharmacy of up to 55%, however there are scarce reports on the topic in our elderly population with rheumatic diseases. We aimed to determine the prevalence of polypharmacy and the association of drug interactions in patients treated for rheumatic disease.

METHODS

A retrospective observational study was conducted on patients undergoing treatment for rheumatic diseases who were treated in geriatrics and rheumatology clinics from January to December 2021. The presence of polypharmacy and drug interactions was evaluated using the BOT Plus Pharmacological Surveillance System. The prevalence of polypharmacy and the association of drug interactions were estimated.

RESULTS

We evaluated 320 patients, with a mean age of 67.05±5.8 years, predominantly female (85%). The prevalence of polypharmacy was 68.1% (n=218), of which 214 (98.1%) patients had related drug interactions; 27.1% were severe and 53.2% as moderate interactions. Factors related with increased risk of drug interactions were being exposed to hypertension increased the risk of drug interactions (POR 1.75, 95% CI 1.44-2.14; P<0.001), having osteoarthritis (POR 1.21, 95% CI 1.04-1.42; P=0.032) and thyroid disease (POR 1.45, 95% CI 1.28-1.65; P=0.001). The most prevalent serious interactions were leflunomide-methotrexate in 27 (46.5%) patients and buprenorphine-tramadol in 8 (13.7%).

CONCLUSIONS

A high prevalence of polypharmacy and drug interactions was observed in elderly patients with rheumatic diseases. The main associated factors were comorbidities, particularly high blood pressure, osteoarthritis and thyroid diseases.

The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial

BACKGROUND

Drug-drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations.

METHODS

We implemented a cluster randomised stepped-wedge trial in nine ICUs in the Netherlands. Five ICUs already used potential DDI alerts. Patients aged 18 years or older admitted to the ICU with at least two drugs administered were included. Our intervention was an adapted CDSS, only providing alerts for potential DDIs considered as high risk. The intervention was delivered at the ICU level and targeted physicians. We hypothesised that showing only relevant alerts would improve CDSS effectiveness and lead to a decreased number of administered high-risk drug combinations. The order in which the intervention was implemented in the ICUs was randomised by an independent researcher. The primary outcome was the number of administered high-risk drug combinations per 1000 drug administrations per patient and was assessed in all included patients. This trial was registered in the Netherlands Trial Register (identifier NL6762) on Nov 26, 2018, and is now closed.

FINDINGS

In total, 10 423 patients admitted to the ICU between Sept 1, 2018, and Sept 1, 2019, were assessed and 9887 patients were included. The mean number of administered high-risk drug combinations per 1000 drug administrations per patient was 26·2 (SD 53·4) in the intervention group (n=5534), compared with 35·6 (65·0) in the control group (n=4353). Tailoring potential DDI alerts to the ICU led to a 12% decrease (95% CI 5-18%; p=0·0008) in the number of administered high-risk drug combinations per 1000 drug administrations per patient, after adjusting for clustering and prognostic factors.

INTERPRETATION

This cluster randomised stepped-wedge trial showed that tailoring potential DDI alerts to the ICU setting significantly reduced the number of administered high-risk drug combinations. Our list of high-risk drug combinations can be used in other ICUs, and our strategy of tailoring alerts based on clinical relevance could be applied to other clinical settings.

FUNDING

ZonMw.

Assessment of frequency and types of drug interactions in intensive care units: a cross-sectional study

Background

Drug interactions can cause adverse reactions, from treatment inefficiency to serious treatment complications in the patient. Due to the complexity of drug therapy and the simultaneous use of several drugs and different drug groups, patients hospitalized in intensive care units are exposed to more drug interactions. Therefore, this study was conducted to investigate the frequency of drug interactions in patients hospitalized in the ICU.

Methods

In this cross-sectional study, the files of 300 patients hospitalized in the ICU were examined. Drug interactions were determined using Lexicomp software and the book drug iteration facts. Data analysis was done using SPSS 21 software.

Findings

The findings showed that there were a total of 1121 cases of interference. Two hundred thirty-one (77%) patients had moderate interference, 94 (31.3%) patients had mild interference, and 67 patients (22.3%) had severe interference. One hundred eight patients had B-type interference, 223 C-type interference, 116 D-type interference, and 6 X-type interference, so most of the interactions are C-type interference. One hundred eighty-six patients had pharmacokinetic interference and 201 patients had pharmacodynamics interference. The highest interaction was between two drugs, heparin and aspirin with 58 cases.

Conclusion

This study highlights the alarming frequency and types of drug interactions observed in ICU. The high prevalence of drug interactions emphasizes the need for improved medication management and vigilance in these critical care settings. Polypharmacy and certain drug combinations were identified as major contributing factors to the occurrence of drug interactions, which calls for regular medication reviews and cautious prescribing practices.

An update on drug-drug interactions for care of the acutely ill in the era of COVID-19

PURPOSE

To provide key pharmacological concepts underlying drug-drug interactions (DDIs), a decision-making framework, and a list of DDIs that should be considered in the context of contemporary acutely ill patients with COVID-19.

SUMMARY

DDIs are frequently encountered in the acutely ill. The implications of DDIs include either increased risk of drug toxicity or decreased effectiveness, which may have severe consequences in the acutely ill due to lower physiological and neurocognitive reserves in these patients. In addition, an array of additional therapies and drug classes have been used for COVID-19 that were not typically used in the acute care setting. In this update on DDIs in the acutely ill, we provide key pharmacological concepts underlying DDIs, including a discussion of the gastric environment, the cytochrome P-450 (CYP) isozyme system, transporters, and pharmacodynamics in relation to DDIs. We also provide a decision-making framework that elucidates the identification of DDIs, risk assessment, selection of alternative therapies, and monitoring. Finally, important DDIs pertaining to contemporary acute care clinical practice related to COVID-19 are discussed.

CONCLUSION

Interpreting and managing DDIs should follow a pharmacologically based approach and a systematic decision-making process to optimize patient outcomes.

Acute liver injury in COVID-19 patients hospitalized in the intensive care unit: Narrative review

In recent years, humanity has been confronted with a global pandemic due to coronavirus disease 2019 (COVID-19), which has caused an unprecedented health and economic crisis worldwide. Apart from the respiratory symptoms, which are considered the principal manifestations of COVID-19, it has been recognized that COVID-19 constitutes a systemic inflammatory process affecting multiple organ systems. Across the spectrum of organ involvement in COVID-19, acute liver injury (ALI) has been gradually gaining increasing attention by the international scientific community. COVID-19 associated liver impairment can affect a considerable proportion of COVID-19 patients and seems to correlate with the severity of the disease course. Indeed, COVID-19 patients hospitalized in the intensive care unit (ICU) run a greater risk of developing ALI due to the severity of their clinical condition and in the context of multi-organ failure. The putative pathophysiological mechanisms of COVID-19 induced ALI in ICU patients remain poorly understood and appear to be multifactorial in nature. Several theories have been proposed to explain the occurrence of ALI in the ICU setting, such as hypoperfusion and ischemia due to hemodynamic instability, passive liver congestion as a result of congestive heart failure, ischemia-reperfusion injury, hypoxia due to respiratory failure, mechanical ventilation itself, sepsis and septic shock, cytokine storm, endotheliitis with concomitant coagulopathy, drug-induced liver injury, parenteral nutrition and direct cytopathic viral effect. It should be noted that no specific therapy for COVID-19 induced ALI exists. Therefore, the therapeutic approach lies in preventive measures and is exclusively supportive once ALI ensues. The aim of the current review is to scrutinize the existing evidence on COVID-19 associated ALI in ICU patients, explore its clinical implications, shed light on the underlying pathophysiological mechanisms and propose potential therapeutic approaches. Ongoing research on the particular scientific field will further elucidate the pathophysiology behind ALI and address unresolved issues, in the hope of mitigating the tremendous health consequences imposed by COVID-19 on ICU patients.

Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.

Prevalence of potential drug - drug interactions in the cardiothoracic intensive care unit patients in a Chinese tertiary care teaching hospital

Background

With an increasing number of reviews describing clinically significant drug–drug interactions (DDIs), the scope and severity of interactions involving commonly used drugs in cardiothoracic intensive care units (CCUs) remain unclear. This study aims to identify risk factors and determine the incidence of potential DDIs in intensive care units.

Methods

DDIs were identified based on the profile of the prescribed drug and classified according to the Micromedex drug interaction database. Potential risk factors associated with DDIs have been identified.

Results

A total of 3193 medication episodes were evaluated, and 680 DDIs (21.3%) were found. A total of 203 patients were recruited into the study, with an average of 3.4 DDIs per patient [95% confidence interval (3.2 − 3.6)]. A total of 84.2% of the patients experienced at least one DDI. Anticoagulant and antiplatelet agents were involved in 33.5% (228/680) of the potential drug − drug interactions in the CCU. Univariate analysis and multiple logistic regression analysis showed that the age of the patient and the number of medications prescribed were significantly correlated with the occurrence of DDIs. In multiple linear regression analysis, the number of DDIs had a significant correlation only with the number of prescription drugs.

Conclusions

A high prevalence of DDIs was observed, especially in intensive care units without pharmacist intervention and computerized drug monitoring systems, highlighting the need for active surveillance to prevent potential adverse events.

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.

Clinically Significant Cytochrome P450-Mediated Drug-Drug Interactions in Children Admitted to Intensive Care Units

OBJECTIVES

Children admitted to intensive care units (ICUs) often require multiple medications due to the complexity and severity of their disease, which put them at an increased risk for drug interactions. This study examined cytochrome P450-mediated drug-drug interactions (DDIs) based on the Pediatric Intensive Care (PIC) database, with the aim of analyzing the incidence of clinically significant potential drug-drug interactions (pDDIs) and exploring the occurrence of actual adverse reactions.

METHODS

The Lexicomp database was used to screen cytochrome P450-mediated DDI pairings with good levels of reliability and clear clinical phenotypes. Patients exposed to the above drug pairs during the same period were screened in the PIC database. The incidence of clinically significant pDDIs was calculated, and the occurrence of adverse reactions was explored based on laboratory measurements.

RESULTS

In total, 84 (1.21%) of 6920 children who used two or more drugs were exposed to at least one clinically significant pDDI. All pDDIs were based on CYP3A4, with nifedipine + voriconazole (39.60%) being the most common drug pair, and the most frequent being the J02 class of drugs. Based on laboratory measurements, 15 adverse reactions were identified in 12 patients.

CONCLUSIONS

Clinically significant cytochrome P450-mediated pDDIs existed in the children admitted to ICUs, and some of the pDDIs led to adverse clinical outcomes. The use of clinical decision support systems can guide clinical medication use, and clinical monitoring of patients' needs has to be enhanced.

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.

Unraveling the Worth of a Clinical Pharmacist

Krishna B. Unraveling the Worth of a Clinical Pharmacist. Indian J Crit Care Med 2021;25(11):1215-1216.

Clinically Relevant Interactions with Anti-Infectives on Intensive Care Units-A Multicenter Delphi Study

Patients in intensive care units (ICUs) are at high risk of drug–drug interactions (DDIs) due to polypharmacy. Little is known about type and frequency of DDIs within German ICUs. Clinical pharmacists’ interventions (PI) recorded in a national database (ADKA-DokuPIK) were filtered for ICU patients. Binary DDIs involving ≥1 anti-infective agent with >1 database entry were selected. A modified two-step Delphi process with a group of senior hospital pharmacists was employed to evaluate selected DDIs for clinical relevance by using a five-point scale and to develop guidance for clinical practice. In total, 16,173 PI were recorded, including 1836 (11%) DDIs in the ICU setting. Of the latter, 41% (756/1836) included ≥1 anti-infective agent, 32% (590/1836) were binary DDIs, and 25% (455/1836) were listed at least twice. This translates into 88 different DDIs, 74% (65/88) of which were rated as being clinically relevant by our expert panel. The majority of DDIs (76% [67/88]) included macrolides, antifungals, or fluoroquinolones. This percentage was even higher in DDIs being rated as clinically relevant by the experts (85% [55/65]). It is noted that an inter-professional discussion and approach is needed in the individual patient management of DDIs. The guidance developed might be a tool for decision support.

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.

Nephrotoxin Stewardship

Drugs are the third leading cause of acute kidney injury (AKI) in critically ill patients. Nephrotoxin stewardship ensures a structured and consistent approach to safe medication use and prevention of patient harm. Comprehensive nephrotoxin stewardship requires coordinated patient care management strategies for safe medication use, ensuring kidney health, and avoiding unnecessary costs to improve the use of nephrotoxins, renally eliminated drugs, and kidney disease treatments. Implementing nephrotoxin stewardship reduces medication errors and adverse drug events, prevents or reduces severity of drug-associated AKI, prevents progression to or worsening of chronic kidney disease, and alleviates financial burden on the health care system.

Clinical significance of potential drug-drug interactions in a pediatric intensive care unit: A single-center retrospective study

Despite the high prevalence of potential drug-drug interactions in pediatric intensive care units, their clinical relevance and significance are unclear. We assessed the characteristics and risk factors of clinically relevant potential drug-drug interactions to facilitate their efficient monitoring in pediatric intensive care units. This retrospective cohort study reviewed the medical records of 159 patients aged <19 years who were hospitalized in the pediatric intensive care unit at Seoul National University Hospital (Seoul, Korea) for ≥3 days between August 2019 and February 2020. Potential drug-drug interactions were screened using the Micromedex Drug-Reax® system. Clinical relevance of each potential drug-drug interaction was reported with official terminology, magnitude of severity, and causality, and the association with the patient's clinical characteristics was assessed. In total, 115 patients (72.3%) were exposed to 592 potential interactions of 258 drug pairs. In 16 patients (10.1%), 22 clinically relevant potential drug-drug interactions were identified for 19 drug pairs. Approximately 70% of the clinically relevant potential drug-drug interactions had a severity grade of ≥3. Exposure to potential drug-drug interactions was significantly associated with an increase in the number of administrated medications (6-7 medications, p = 0.006; ≥8, p<0.001) and prolonged hospital stays (1-2 weeks, p = 0.035; ≥2, p = 0.049). Moreover, clinically relevant potential drug-drug interactions were significantly associated with ≥8 prescribed drugs (p = 0.019), hospitalization for ≥2 weeks (p = 0.048), and ≥4 complex chronic conditions (p = 0.015). Most potential drug-drug interactions do not cause clinically relevant adverse outcomes in pediatric intensive care units. However, because the reactions that patients experience from clinically relevant potential drug-drug interactions are often very severe, there is a medical need to implement an appropriate monitoring system for potential drug-drug interactions according to the pediatric intensive care unit characteristics.

[Pharmacological/pharmaceutical counseling in intensive care medicine]

Adequate pharmacotherapy in critically ill patients is challenging for many clinicians. Disease-related pathophysiological changes often lead to complex therapy strategies including many intensive care treatments (e.g. invasive ventilation, renal replacement therapy). These measures often influence drug prescribing and dosing. Therefore, in organ dysfunction such as renal and liver impairment reduced drug elimination has to be considered by adapting drug dosing towards elimination rates. Moreover, as intensive care medicine often includes the use of multiple drugs the risk for drug-drug interactions increases. The current article gives an overview about the complexity of individual pharmacotherapy in intensive care units whilst providing information for its clinical implementation.

Identifying and Characterizing Serious Adverse Drug Reactions Associated With Drug-Drug Interactions in a Spontaneous Reporting Database

Background: Drug-drug interactions (DDIs) are an important cause of adverse drug reactions (ADRs). In literature most of studies focus only on potential DDIs, while detailed data on serious ADRs associated with DDIs are limited. Our aim is to identify and characterize serious ADRs caused by DDIs using a spontaneous reporting database.

Methods: All serious ADR reports, not related to vaccines and with a “definite”, “probable” or “possible” causality assessment, inserted into the National Pharmacovigilance database from Veneto Region (January 1, 2015 to May 31, 2020) were analyzed. A list of drug pairs was created by selecting the reports containing at least two suspected or concomitant drugs. We verified which drug pairs potentially interacted according to the online version of DRUGDEX^® system. For each potential DDI we controlled whether the ADR description in the report corresponded to the interaction effect as described in Micromedex. A detailed characterization of all serious reports containing an occurring DDI was performed.

Results: In the study period a total of 31,604 reports of suspected ADRs from the Veneto Region were identified, of which 2,195 serious reports (6.9% of all ADR reports) containing at least two suspected or concomitant drugs were analyzed. We identified 1,208 ADR reports with at least one potential DDI (55.0% of 2,195) and 381 reports (17.4% of 2,195 reports) with an occurring ADR associated with a DDI. The median age of patients and the number of contraindicated or major DDIs were significantly higher in reports with an occurring DDI. Warfarin was the most frequently reported interacting drug and the most common ADRs were gastrointestinal or cerebral hemorrhagic events. The proton pump inhibitors/warfarin, followed by platelet aggregation inhibitors/warfarin were the drug-drug combinations most frequently involved in ADRs caused by DDIs. The highest proportion of fatal reports was observed with platelet aggregation inhibitors/warfarin and antidepressants/warfarin.

Conclusion: Our findings showed that about one-third of patients exposed to a potential DDI actually experienced a serious ADR. Furthermore, our study confirms that a spontaneous reporting database could be a valuable resource for identifying and characterizing ADRs caused by DDIs and the drugs leading to serious ADRs and deaths.

Severe Potential Drug-Drug Interactions and the Increased Length of Stay of Children in Intensive Care Unit

Children are exposed to drug-drug interactions (DDI) risks due to their organism’s complexity and the need for several medicines prescriptions in pediatric intensive care units (PICU). This study aimed to assess the prevalence of potential DDIs in a Brazilian PICU. We carried out a cross-sectional study at a pediatric teaching hospital from Rio de Janeiro (Brazil) over one year. Potential DDIs (pDDIs) between prescribed medicines for hospitalized children in PICU (n = 143) were analyzed according to severity using Micromedex^®. Sex, age group, number of drugs prescribed, vasoactive amines use (a proxy of clinical complexity), and the PICU length of stay were summarized using descriptive statistics. Association between the PICU length stay, and variables sex, age, clinical condition complexity, number of drugs prescribed, and severity of pDDI were examined by univariate and multiple linear regression. Seventy percent of patients aged three days to 14 years old were exposed at least one potential DDIs during PICU stay. Two hundred eighty-four different types of pDDIs were identified, occurring 1,123 times. Nervous system drugs were implicated in 55% of the interactions, and fentanyl (10%) was most involving in pDDIs. Most pDDIs were classified as higher severity (56.2%), with reasonable documentation (64.6%) and unspecified onset time (63.8%). Worse clinical condition, ten or more drugs prescribed, and most severe pDDIs were associated with a longer PICU length of stay. Multiple linear regression analysis showed an increase of 9.83 days (95% confidence interval: 3.61–16.05; p = 0.002) in the PICU length of stay in children with major or contraindicated pDDIs. The results of this research may support the monitoring and prevention of pDDIs related to adverse events in children in intensive care and the design and conduction of new studies.

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.

Clinical Decision Support and Implications for the Clinician Burnout Crisis

OBJECTIVES

This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout.

METHODS

We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria.

RESULTS

Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS.

CONCLUSIONS

CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

Potential drug-drug interactions in ICU patients: a retrospective study

Objectives A "potential drug-drug interaction" (pDDI) is the possibility one drug has to alter the effects of another when both are administered simultaneously. Intensive care unit (ICU) patients are especially prone to these pDDIs. This study aimed to determine the frequency and severity of pDDIs during the hospitalization of patients in the ICU. Methods This study was conducted retrospectively in three hospitals, including both governmental and non-governmental hospitals in Nablus, Palestine, over the course of six months; starting in January 2018 and ending in June 2018. The sample size included 232 ICU patients, and medications prescribed during the hospitalization of these patients were evaluated for pDDIs using the drugs.com application. Results A total of 167 patients (72%) were found to have at least one pDDI, while the total number of pDDIs in the study was 422, resulting in an average of 1.82 pDDIs per patient. Out of the total identified pDDIs, 41 interactions (9.7%) were major interactions, 281 (66.6%) were moderate interactions and 100 (23.7%) were minor interactions. The past medical history of these patients showed that many had hypertension (29%), diabetes mellitus (25%) and ischemic heart disease (10%). A serious combination, enoxaparin and aspirin, was found in six patients. Furthermore, as the number of administered drugs increased, the number of interactions increased as well. Conclusions The pDDIs are common in ICU patients. The most common and clinically most important pDDIs require special attention. Polypharmacy significantly increases the number and level of pDDIs, especially in patients with multiple chronic illnesses. Adequate knowledge regarding the most common pDDIs is necessary to enable healthcare professionals to implement ICU strategies that ensure patient safety.

The management of anti-infective agents in intensive care units: the potential role of a 'fast' pharmacology

INTRODUCTION

Patients in intensive care units (ICU) are often developing severe infections in which are associated with significant mortality rates. A number of novel technologies for the rapid microbiological diagnosis of these infections have been developed, introducing the era of 'fast microbiology.' Treatment of bacterial and fungal infections in ICU is however complicated by alterations in the pharmacokinetics of antimicrobial agents.

AREAS COVERED

We review novel pharmacologic tools that can be used to optimize anti-infective therapies and patient management in ICU. A MEDLINE Pubmed search for articles published from January 1995 to 2019 was completed matching the terms pharmacokinetics and pharmacology with antimicrobial agents and ICU or critically ill patients. Moreover, additional studies were identified from the reference list of retrieved articles.

EXPERT OPINION

Several tools are in development for the full automation of the analytical methods used for the quantification of antimicrobial concentrations within a few hours after sample collection. Ad hoc software with adaptive feedback is also available for appropriate dose adjustments based on both individual patient covariate data and therapeutic drug monitoring (TDM) data when available. The application of these technological improvements in the clinical practice should open the way to a 'fast pharmacology' at the bedside.