Recurrent Treatment of Refractory Acute Cellular Rejection with Alemtuzumab after Lung Transplantation.

We present an exceptional case of a lung transplant recipient successfully treated by multiple courses of alemtuzumab for refractory acute cellular rejection. The patient experienced multiple episodes of acute cellular rejection following the transplantation procedure. Alemtuzumab was initiated as third-line rejection treatment and was repeated six times. Each treatment course resulted in complete recovery of the pulmonary function and depletion of T- and B-lymphocytes and NK cells. The onset of rejection was consistently preceded by the recovery of NK cells, while T- and B-lymphocytes remained depleted. This suggests a rejection process mediated by NK cells. This case contributes to recent research findings suggesting that NK cells play a significant role in acute cellular rejection in lung transplant recipients and stresses the importance to further investigate the role of NK cells in rejection. Furthermore, it demonstrates that acute cellular rejection following lung transplantation can be repeatedly managed by treatment with alemtuzumab. DATA AVAILABILITY STATEMENT: The authors confirm that the data supporting the findings of this study are available within the article.

Inpatients' information needs about medication: A narrative systematic literature review.

OBJECTIVE: To provide an overview of inpatients' information needs about medication, including the best moment to provide this information, how, by whom and what patient characteristics influence these needs. METHODS: A systematic literature review was conducted. Studies that reported the information needs from inpatients about medication were included from Medline and Embase. The Crowe critical appraisal tool (CCAT) was used to assess the quality of the studies. RESULTS: Initially, 710 records were retrieved from Medline and Embase. After the forward search, another 609 records were screened and in total, 26 articles were included. The CCAT scores ranged from 17 to 34 points on a 40 point scale and two articles received 0 points. CONCLUSION: Inpatients main needs about medicine information are information about adverse and beneficial effects of medication, and general rules about how to take medication. Preferably, this information is printed and provided at the time of prescribing by a physician that already has a relationship with the patient. The most recent studies show that patients are open to the use of modern technology. PRACTICE IMPLICATIONS: This review provides a starting point for providing medicine information to inpatients. Further research should focus on patient characteristics influencing these information needs.

Real-time monitoring of drug laboratory test interactions: a proof of concept.

OBJECTIVES: For the correct interpretation of test results, it is important to be aware of drug-laboratory test interactions (DLTIs). If DLTIs are not taken into account by clinicians, erroneous interpretation of test results may lead to a delayed or incorrect diagnosis, unnecessary diagnostic testing or therapy with possible harm for patients. A DLTI alert accompanying a laboratory test result could be a solution. The aim of this study was to test a multicentre proof of concept of an electronic clinical decision support system (CDSS) for real-time monitoring of DLTIs. METHODS: CDSS was implemented in three Dutch hospitals. So-called 'clinical rules' were programmed to alert medical specialists for possible DLTIs based on laboratory test results outside the reference range in combination with prescribed drugs. A selection of interactions from the DLTI database of the Dutch society of clinical chemistry and laboratory medicine were integrated in 43 clinical rules, including 24 tests and 25 drugs. During the period of one month all generated DTLI alerts were registered in the laboratory information system. RESULTS: Approximately 65 DLTI alerts per day were detected in each hospital. Most DLTI alerts were generated in patients from the internal medicine and intensive care departments. The most frequently reported DLTI alerts were potassium-proton pump inhibitors (16%), potassium-beta blockers (11%) and creatine kinase-statins (11%). CONCLUSIONS: This study shows that it is possible to alert for potential DLTIs in real-time with a CDSS. The CDSS was successfully implemented in three hospitals. Further research must reveal its usefulness in clinical practice.

Dynamics of the QTc interval over a 24-h dose interval after start of intravenous ciprofloxacin or low-dose erythromycin administration in ICU patients.

QTc interval prolongation is an adverse effect associated with the use of fluoroquinolones and macrolides. Ciprofloxacin and erythromycin are both frequently prescribed QTc-prolonging drugs in critically ill patients. Critically ill patients may be more vulnerable to developing QTc prolongation, as several risk factors can be present at the same time. Therefore, it is important to know the QTc-prolonging potential of these drugs in the intensive care unit (ICU) population. The aim of this study was to assess the dynamics of the QTc interval over a 24-hour dose interval during intravenous ciprofloxacin and low-dose erythromycin treatment. Therefore, an observational study was performed in ICU patients (≥18 years) receiving ciprofloxacin 400 mg t.i.d. or erythromycin 100 mg b.i.d. intravenously. Continuous ECG data were collected from 2 h before to 24 h after the first administration. QT-analyses were performed using high-end holter software. The effect was determined with a two-sample t-test for clustered data on all QTc values. A linear mixed model by maximum likelihood was applied, for which QTc values were assessed for the available time intervals and therapy. No evident effect over time on therapy with ciprofloxacin and erythromycin was observed on QTc time. There was no significant difference (p = 0.22) in QTc values between the ciprofloxacin group (mean 393 ms) and ciprofloxacin control group (mean 386 ms). The erythromycin group (mean 405 ms) and erythromycin control group (mean 404 ms) neither showed a significant difference (p = 0.80). In 0.6% of the registrations (1.138 out of 198.270 samples) the duration of the QTc interval was longer than 500 ms. The index groups showed slightly more recorded QTc intervals over 500 ms. To conclude, this study could not identify differences in the QTc interval between the treatments analyzed.

Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey.

OBJECTIVES: Knowledge of possible drug-laboratory test interactions (DLTIs) is important for the interpretation of laboratory test results. Failure to recognize these interactions may lead to misinterpretation, a delayed or erroneous diagnosis, or unnecessary extra diagnostic tests or therapy, which may harm patients. The aim of this multicentre survey was to evaluate the clinical value of DLTI alerts. METHODS: A survey was designed with six predefined clinical cases selected from the clinical laboratory practice with a potential DLTI. Physicians from several departments, including internal medicine, cardiology, intensive care, surgery and geriatrics in six participating hospitals were recruited to fill in the survey. The survey addressed their knowledge of DLTIs, motivation to receive an alert and opinion on the potential influence on medical decision making. RESULTS: A total of 210 physicians completed the survey. Of these respondents 93% had a positive attitude towards receiving DLTI alerts; however, the reported value differed per case and per respondent's background. In each clinical case, medical decision making was influenced as a consequence of the reported DLTI message (ranging from 3 to 45% of respondents per case). CONCLUSIONS: In this multicentre survey, most physicians stated DLTI messages to be useful in laboratory test interpretation. Medical decision making was influenced by reporting DLTI alerts in each case. Alerts should be adjusted according to the needs and preferences of the receiving physicians.

The use of a clinical decision support tool to assess the risk of QT drug-drug interactions in community pharmacies.

INTRODUCTION: The handling of drug-drug interactions regarding QTc-prolongation (QT-DDIs) is not well defined. A clinical decision support (CDS) tool will support risk management of QT-DDIs. Therefore, we studied the effect of a CDS tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. METHODS: An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of three months were included. The impact of the use of a CDS tool to support the handling of QT-DDIs was studied. For each QT-DDI, handling of the QT-DDI and patient characteristics were extracted from the pharmacy information system. Primary outcome was the proportion of QT-DDIs with an intervention. Secondary outcomes were the type of interventions and the time associated with handling QT-DDIs. Logistic regression analysis was used to analyse the primary outcome. RESULTS: Two hundred and forty-four QT-DDIs pre-CDS tool and 157 QT-DDIs post-CDS tool were included. Pharmacists intervened in 43.0% and 35.7% of the QT-DDIs pre- and post-CDS tool respectively (odds ratio 0.74; 95% confidence interval 0.49-1.11). Substitution of interacting agents was the most frequent intervention. Pharmacists spent 20.8 ± 3.5 min (mean ± SD) on handling QT-DDIs pre-CDS tool, which was reduced to 14.9 ± 2.4 min (mean ± SD) post-CDS tool. Of these, 4.5 ± 0.7 min (mean ± SD) were spent on the CDS tool. CONCLUSION: The CDS tool might be a first step to developing a tool to manage QT-DDIs via a structured approach. Improvement of the tool is needed in order to increase its diagnostic value and reduce redundant QT-DDI alerts. PLAIN LANGUAGE SUMMARY: The use of a tool to support the handling of QTc-prolonging drug interactions in community pharmacies Introduction: Several drugs have the ability to cause heart rhythm disturbances as a rare side effect. This rhythm disturbance is called QTc-interval prolongation. It may result in cardiac arrest. For health care professionals, such as physicians and pharmacists, it is difficult to decide whether or not it is safe to proceed treating a patient with combinations of two or more of these QT-prolonging drugs. Recently, a tool was developed that supports the risk management of these QT drug-drug interactions (QT-DDIs).Methods: In this study, we studied the effect of this tool on the proportion of QT-DDIs for which an intervention was considered by pharmacists. An intervention study was performed using a pre- and post-design in 20 community pharmacies in The Netherlands. All QT-DDIs that occurred during a before- and after-period of 3 months were included.Results: Two hundred and forty-four QT-DDIs pre-implementation of the tool and 157 QT-DDIs post-implementation of the tool were included. Pharmacists intervened in 43.0% of the QT-DDIs before the tool was implemented and in 35.7% after implementation of the tool. Substitution of one of the interacting agents was the most frequent intervention. Pharmacists spent less time on handling QT-DDIs when the tool was used.Conclusion: The clinical decision support tool might be a first step to developing a tool to manage QT-DDIs via a structured approach.

Comparison of two algorithms to support medication surveillance for drug-drug interactions between QTc-prolonging drugs.

BACKGROUND: QTc-prolongation is an independent risk factor for developing life-threatening arrhythmias. Risk management of drug-induced QTc-prolongation is complex and digital support tools could be of assistance. Bindraban et al. and Berger et al. developed two algorithms to identify patients at risk for QTc-prolongation. OBJECTIVE: The main aim of this study was to compare the performances of these algorithms for managing QTc-prolonging drug-drug interactions (QT-DDIs). MATERIALS AND METHODS: A retrospective data analysis was performed. A dataset was created from QT-DDI alerts generated for in- and outpatients at a general teaching hospital between November 2016 and March 2018. ECGs recorded within 7 days of the QT-DDI alert were collected. Main outcomes were the performance characteristics of both algorithms. QTc-intervals of > 500 ms on the first ECG after the alert were taken as outcome parameter, to which the performances were compared. Secondary outcome was the distribution of risk scores in the study cohort. RESULTS: In total, 10,870 QT-DDI alerts of 4987 patients were included. ECGs were recorded in 26.2 % of the QT-DDI alerts. Application of the algorithms resulted in area under the ROC-curves of 0.81 (95 % CI 0.79-0.84) for Bindraban et al. and 0.73 (0.70-0.75) for Berger et al. Cut-off values of ≥ 3 and ≥ 6 led to sensitivities of 85.7 % and 89.1 %, and specificities of 60.8 % and 44.3 % respectively. CONCLUSIONS: Both algorithms showed good discriminative abilities to identify patients at risk for QTc-prolongation when using ≥ 2 QTc-prolonging drugs. Implementation of digital algorithms in clinical decision support systems could support the risk management of QT-DDIs.

Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice.

BACKGROUND: The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. METHODS: A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated. RESULTS: The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. CONCLUSIONS: A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. TRIAL REGISTRATION: No trial registration, MEC-2015-368.

The Development of Practice Recommendations for Drug-Disease Interactions by Literature Review and Expert Opinion.

BACKGROUND: Drug-disease interactions negatively affect the benefit/risk ratio of drugs for specific populations. In these conditions drugs should be avoided, adjusted, or accompanied by extra monitoring. The motivation for many drug-disease interactions in the Summary of Product Characteristics (SmPC) is sometimes insufficiently supported by (accessible) evidence. As a consequence the translation of SmPC to clinical practice may lead to non-specific recommendations. For the translation of this information to the real world, it is necessary to evaluate the available knowledge about drug-disease interactions, and to formulate specific recommendations for prescribers and pharmacists. The aim of this paper is to describe a standardized method how to develop practice recommendations for drug-disease interactions by literature review and expert opinion. METHODS: The development of recommendations for drug-disease interactions will follow a six-step plan involving a multidisciplinary expert panel (1). The scope of the drug-disease interaction will be specified by defining the disease and by describing relevant effects of this drug-disease interaction. Drugs possibly involved in this drug-disease interaction are selected by checking the official product information, literature, and expert opinion (2). Evidence will be collected from the official product information, guidelines, handbooks, and primary literature (3). Study characteristics and outcomes will be evaluated and presented in standardized reports, including preliminary conclusions on the clinical relevance and practice recommendations (4). The multidisciplinary expert panel will discuss the reports and will either adopt or adjust the conclusions (5). Practice recommendations will be integrated in clinical decision support systems and published (6). The results of the evaluated drug-disease interactions will remain up-to-date by screening new risk information, periodic literature review, and (re)assessments initiated by health care providers. ACTIONABLE RECOMMENDATIONS: The practice recommendations will result in advices for specific DDSI. The content and considerations of these DDSIs will be published and implemented in all Clinical Decision Support Systems in the Netherlands. DISCUSSION: The recommendations result in professional guidance in the context of individual patient care. The professional will be supported in the decision making in concerning pharmacotherapy for the treatment of a medical problem, and the clinical risks of the proposed medication in combination with specific diseases.

Improving medication safety in the Intensive Care by identifying relevant drug-drug interactions - Results of a multicenter Delphi study.

PURPOSE: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU. MATERIALS AND METHODS: We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting. RESULTS: In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting. CONCLUSIONS: A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue.

Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology.

BACKGROUND: The effectiveness of the clinical decision support systems (CDSSs) is hampered by frequent workflow interruptions and alert fatigue because of alerts with little or no clinical relevance. In this paper, we reported a methodology through which we applied knowledge from the clinical context and the international recommendations to develop a potential drug-drug interaction (pDDI) CDSS in the field of kidney transplantation. METHODS: Prescriptions of five nephrologists were prospectively recorded through non-participatory observations for two months. The Medscape multi-drug interaction checker tool was used to detect pDDIs. Alongside the Stockley's drug interactions reference, our clinicians were consulted with respect to the clinical relevance of detected pDDIs. We performed semi-structured interviews with five nephrologists and one informant nurse. Our clinically relevant pDDIs were checked with the Dutch "G-Standard". A multidisciplinary team decided the design characteristics of pDDI-alerts in a CDSS considering the international recommendations and the inputs from our clinical context. Finally, the performance of the CDSS in detecting DDIs was evaluated iteratively by a multidisciplinary research team. RESULTS: Medication data of 595 patients with 788 visits were collected and analyzed. Fifty-two types of interactions were most common, comprising 90% of all pDDIs. Among them 33 interactions (comprising 77% of all pDDIs) were rated as clinically relevant and were included in the CDSS's knowledge-base. Of these pDDIs, 73% were recognized as either pseudoduplication of drugs or not a pDDI when checked with the Dutch G-standard. Thirty-three alerts were developed and physicians were allowed to customize the appearance of pDDI-alerts based on a proposed algorithm. CONCLUSION: Clinical practice contexts should be studied to understand the complexities of clinical work and to learn the type, severity and frequency of pDDIs. In order to make the alerts more effective, clinicians' points of view concerning the clinical relevance of pDDIs are critical. Moreover, flexibility should be built into a pDDI-CDSS to allow clinicians to customize the appearance of pDDI-alerts based on their clinical context.

Diagnostic error as a result of drug-laboratory test interactions.

Background Knowledge of possible drug-laboratory test interactions (DLTIs) is important for the interpretation of laboratory test results. Test results may be affected by physiological or analytical drug effects. Failure to recognize these interactions may lead to misinterpretation of test results, a delayed or erroneous diagnosis or unnecessary extra tests or therapy, which may harm patients. Content Thousands of interactions have been reported in the literature, but are often fragmentarily described and some papers even reported contradictory findings. How can healthcare professionals become aware of all these possible interactions in their individual patients? DLTI decision support applications could be a good solution. In a literature search, only four relevant studies have been found on DLTI decision support applications in clinical practice. These studies show a potential benefit of automated DLTI messages to physicians for the interpretation of laboratory test results. All physicians reported that part of the DLTI messages were useful. In one study, 74% of physicians even sometimes refrained from further additional examination. Summary and outlook Unrecognized DLTIs potentially cause diagnostic errors in a large number of patients. Therefore, efforts to avoid these errors, for example with a DLTI decision support application, could tremendously improve patient outcome.

Impact of interactions between drugs and laboratory test results on diagnostic test interpretation - a systematic review.

Intake of drugs may influence the interpretation of laboratory test results. Knowledge and correct interpretation of possible drug-laboratory test interactions (DLTIs) is important for physicians, pharmacists and laboratory specialists. Laboratory results may be affected by analytical or physiological effects of medication. Failure to take into account the possible unintended influence of drug use on a laboratory test result may lead to incorrect diagnosis, incorrect treatment and unnecessary follow-up. The aim of this review is to give an overview of the literature investigating the clinical impact and use of DLTI decision support systems on laboratory test interpretation. Particular interactions were reported in a large number of articles, but they were fragmentarily described and some papers even reported contradictory findings. To provide an overview of information that clinicians and laboratory staff need to interpret test results, DLTI databases have been made by several groups. In a literature search, only four relevant studies have been found on DLTI decision support applications for laboratory test interpretation in clinical practice. These studies show a potential benefit of automated DLTI messages to physicians for the correct interpretation of laboratory test results. Physicians reported 30-100% usefulness of DLTI messages. In one study 74% of physicians sometimes even refrained from further additional examination. The benefit of decision support increases when a refined set of clinical rules is determined in cooperation with health care professionals. The prevalence of DLTIs is high in a broad range of combinations of laboratory tests and drugs and these frequently remain unrecognized.

QTc prolongation during ciprofloxacin and fluconazole combination therapy: prevalence and associated risk factors.

AIM(S): Ciprofloxacin and fluconazole combination therapy is frequently used as prophylaxis for, and treatment of, infections in patients with haematological malignancies. However, both drugs are known to prolong the heart rate-corrected QT (QTc) interval, which is a serious risk factor for torsade de pointes (TdP). Therefore, the aim of the current study was to assess the prevalence of QTc prolongation during ciprofloxacin and fluconazole use. The secondary objective was to determine associated risk factors of QTc prolongation in these patients. METHODS: A prospective observational study was performed in patients admitted to the Erasmus University Medical Centre and treated with ciprofloxacin and fluconazole. A 12-lead electrocardiogram (ECG) was recorded at the estimated time to peak concentration (Tmax ) for the last added drug. The main outcome was the proportion of patients with QTc prolongation during treatment. Data on the following potential risk factors were collected: patient characteristics, serum electrolyte levels, dosage of ciprofloxacin and fluconazole, renal and liver function and concomitant use of other QTc-prolonging drugs and cytochrome P450 3A4 inhibitors. RESULTS: A total of 170 patients were included, of whom 149 (87.6%) were treated for haematological malignancies. The prevalence of QTc prolongation was 4.7%. No risk factors were found to be associated with QTc prolongation. The QTc interval increased by 10.7 ms [95% confidence interval (CI) 7.2, 14.1 ms] during ciprofloxacin and fluconazole combination therapy. CONCLUSION: The prevalence of QTc prolongation in patients using ciprofloxacin and fluconazole is low compared with the prevalence in the general population, which varies from 5% to 11%. In addition, no risk factors were found. Given the low prevalence, routine ECG monitoring in patients on this therapy should be reconsidered.

Clinical reasoning in the context of active decision support during medication prescribing.

OBJECTIVE: Describe and analyze reasoning patterns of clinicians responding to drug-drug interaction alerts in order to understand the role of patient-specific information in the decision-making process about the risks and benefits of medication therapy. Insights could be used to inform the design of decision-support interventions. METHODS: Thirty-two clinicians working with five EHRs in two countries completed sets of six medication orders each and responded to high- and low-severity drug-drug interaction alerts while verbalizing their thoughts in a standard think-aloud protocol. Tasks were recorded and analyzed to describe reasoning patterns about patient-risk assessment and strategies to avoid or mitigate it. RESULTS: We observed a total of 171 prescribing decisions. Clinicians actively sought to reduce risk when responding to high-severity alerts, mostly by monitoring patients and making dose adjustments (52 alerts, 40%). In contrast, they routinely left prescriptions unchanged after low-severity alerts when they felt confident that patients would tolerate the drug combination and that treatment benefits outweighed the risks (30 alerts, 71%). Clinicians used similar reasoning patterns regardless of the EHR used and differences in alert design. DISCUSSION: Clinicians conceptualized risk as a complex set of interdependent tradeoffs specific to individual patients and had a tendency not to follow advice they considered of low clinical value. Omission of patient-specific data, which was not shown in alerts or included in trigger logic, may have contributed to the constancy of reasoning and to similarities in risk-control strategies we observed despite significant differences in interface design and system function. CONCLUSION: Declining an alert suggestion was preceded by sometimes brief but often complex reasoning, prioritizing different aspects of care quality and safety, especially when the perceived risk was higher. Clinicians believed that the risk indicated in drug-drug interaction alerts needs to be interpreted as one factor in the broader context of care, specific to a patient.

Best practice strategies to safeguard drug prescribing and drug administration: an anthology of expert views and opinions.

BACKGROUND: While evidence on implementation of medication safety strategies is increasing, reasons for selecting and relinquishing distinct strategies and details on implementation are typically not shared in published literature. OBJECTIVE: We aimed to collect and structure expert information resulting from implementing medication safety strategies to provide advice for decision-makers. SETTING: Medication safety experts with clinical expertise from thirteen hospitals throughout twelve European and North American countries shared their experience in workshop meetings, on-site-visits and remote structured interviews. METHODS: We performed an expert-based, in-depth assessment of implementation of best-practice strategies to improve drug prescribing and drug administration. MAIN OUTCOME MEASURES: Workflow, variability and recommended medication safety strategies in drug prescribing and drug administration processes. RESULTS: According to the experts, institutions chose strategies that targeted process steps known to be particularly error-prone in the respective setting. Often, the selection was channeled by local constraints such as the e-health equipment and critically modulated by national context factors. In our study, the experts favored electronic prescribing with clinical decision support and medication reconciliation as most promising interventions. They agreed that self-assessment and introduction of medication safety boards were crucial to satisfy the setting-specific differences and foster successful implementation. CONCLUSION: While general evidence for implementation of strategies to improve medication safety exists, successful selection and adaptation of a distinct strategy requires a thorough knowledge of the institute-specific constraints and an ongoing monitoring and adjustment of the implemented measures.

Concomitant lamotrigine use is associated with decreased efficacy of the ketogenic diet in childhood refractory epilepsy.

PURPOSE: Anti-epileptic drugs (AEDs) and the ketogenic diet (KD) are often used concomitantly in children with refractory epilepsy. It has been hypothesised that certain AEDs may interfere with KD. The purpose of this study was to elucidate relationships between efficacy of KD and use of specific AEDs. METHODS: A retrospective study was performed in 71 children with refractory epilepsy starting the KD between 2008 and 2014 in Erasmus University Hospital Sophia Children's Hospital. Efficacy of the KD (defined as 50% seizure reduction) was evaluated after three months of treatment and related to the AEDs used. RESULTS: The KD was successful after three months in 61% of the children (N=71). Efficacy was significantly reduced if children (n=16) used lamotrigine (31%) at diet initiation or in the course of the diet, compared to other antiepileptic drugs (69%) (p=0.006). In comparison to children using other antiepileptic drugs, the percentage of children that had adequate ketosis was significantly reduced in case of lamotrigine use (p=0.049). CONCLUSION: Lamotrigine treatment during KD is associated with a decreased efficacy of the KD.

[A baby with digoxin toxicity]. / Een zuigeling met een digoxine-intoxicatie.

Accidental poisoning or overdoses occur frequently in children. These are difficult to recognise because young children cannot communicate their symptoms; this means that specific symptoms can be missed, which can delay the diagnosis. A 5-month-old boy was accidently given a tenfold dose of digoxin for 5 days. He developed feeding difficulties, vomiting, weight loss, elevated urea and creatinine levels, hyponatraemia, hyperkalaemia and ECG abnormalities. The digoxin plasma concentration was 7.6 µg/l. The patient was given digoxin antibodies, following which the digoxin concentration was < 0.3 µg/l; 12 hours later the digoxin concentration was 3.1 µg/l as a result of redistribution; 2 days after the administration of digoxin antibodies the plasma concentration was within the therapeutic range.

Recommendations to improve the usability of drug-drug interaction clinical decision support alerts.

OBJECTIVE: To establish preferred strategies for presenting drug-drug interaction (DDI) clinical decision support alerts. MATERIALS AND METHODS: A DDI Clinical Decision Support Conference Series included a workgroup consisting of 24 clinical, usability, and informatics experts representing academia, health information technology (IT) vendors, healthcare organizations, and the Office of the National Coordinator for Health IT. Workgroup members met via web-based meetings 12 times from January 2013 to February 2014, and two in-person meetings to reach consensus on recommendations to improve decision support for DDIs. We addressed three key questions: (1) what, how, where, and when do we display DDI decision support? (2) should presentation of DDI decision support vary by clinicians? and (3) how should effectiveness of DDI decision support be measured? RESULTS: Our recommendations include the consistent use of terminology, visual cues, minimal text, formatting, content, and reporting standards to facilitate usability. All clinicians involved in the medication use process should be able to view DDI alerts and actions by other clinicians. Override rates are common but may not be a good measure of effectiveness. DISCUSSION: Seven core elements should be included with DDI decision support. DDI information should be presented to all clinicians. Finally, in their current form, override rates have limited capability to evaluate alert effectiveness. CONCLUSION: DDI clinical decision support alerts need major improvements. We provide recommendations for healthcare organizations and IT vendors to improve the clinician interface of DDI alerts, with the aim of reducing alert fatigue and improving patient safety.