Title Participants Abstract "A multicenter record review of in-hospital adverse drug events requiring a higher level of care" "Kristel MARQUET, Neree CLAES, Elke DE TROY, Gaby KOX, Martijn DROOGMANS, Arthur VLEUGELS" "Objective: Adverse drug events (ADEs) are a worldwide concern, particularly when leading to a higher level of care. This study defines a higher level of care as an unplanned (re)admission to an intensive care unit or an intervention by a Medical Emergency Team. The objectives are to describe the incidence and preventability of ADEs leading to a higher level of care, to assess the types of drug involved, and to identify the risk factors. Methods: A three-stage retrospective review was performed in six Belgian hospitals. Patient records were assessed by a trained clinical team consisting of a nurse, a physician, and a clinical pharmacist. Descriptive statistics, univariate, and multiple logistic regressions were used. Results: In this study, 830 patients were detected for whom a higher level of care had been needed. In 160 (19.3%) cases, an ADE had occurred; 134 (83.8%) of these were categorized as preventable adverse drug events (pADEs). The overall incidence rate of patients transferred to a higher level of care because of a pADE was 33.9 (95% CI: 28.5-39.3) per 100,000 patient days at risk. Antibiotics and antithrombotic agents accounted both for one-fifth of all pADEs. Multivariate analysis indicated American Society of Anaesthesiologists physical status score as a risk factor for pADEs. Conclusions: The high number of pADE with patient harm shows that there is a need for structural improvement of pharmacotherapeutic care. Detection of these pADEs can be the basis for the implementation of these improvements." "Adverse drug events in intensive care units" "Simon Seynaeve, Walter Verbrugghe, Brigitte Claes, Dirk Vandenplas, Dirk Reyntiens, Philippe Jorens" "Background Adverse drug events are considered determinants of patient safety and quality of care. Objective To assess the characteristics of adverse drug events in patients admitted to an intensive care unit and determine the impact of severity of illness and nursing workload on the prevalence of the events. Methods A cross-sectional survey based on retrospective analysis of a high-quality patient data management system for a university-based intensive care unit was used. The prevalence of adverse drug events was measured by using a validated global trigger tool adapted for the critical care environment. Severity was determined by using a validated algorithm. Disease severity and nursing workload were assessed by using validated scoring systems. An investigator blinded to the study and a panel of experts assessed putative serious adverse drug events for each drug taken. Characteristics of patients with and without adverse drug events were compared by using univariate and stepwise multivariate logistic regression. Results During 175 of 1009 intensive care unit days screened, 230 adverse drug events occurred in 79 patients. The most common events were hypoglycemia, prolonged activated partial thromboplastin time, and hypokalemia. Of the adverse events, 96% were classified as causing temporary harm and 4% as causing complications. Both mean severity of disease and nursing workload were significantly higher on days when 1 or more adverse drug events occurred. Conclusion Adverse drug events were common in intensive care unit patients and were associated with illness severity and nursing workload." "Dose-Related Adverse Drug Events in Neonates: Recognition and Assessment" "Karel Allegaert" "The efficacy and safety of a drug is dose or exposure related, and both are used to assess the benefit-risk balance of a given drug and ultimately to decide on the specific drug license, including its dose and indication(s). Unfortunately, both efficacy and safety are much more difficult to establish in neonates, resulting in very few drugs licensed for use in this vulnerable population. This review will focus on dose-related adverse events in neonates. Besides the regulatory classification on seriousness, adverse event assessment includes aspects related to signal detection, causality, and severity. Disentangling confounders from truly dose-related adverse drug events remains a major challenge, as illustrated for drug-induced renal impairment, drug-induced liver injury, and neurodevelopmental outcome. Causality assessment, using either routine tools (Naranjo algorithm, World Health Organization's Uppsala Monitoring Center causality tool) or a Naranjo algorithm tailored to neonates, still does not sufficiently and reliably document causality in neonates. Finally, very recently, a first neonatal severity-grading tool for neonates has been developed. Following the development of advanced pharmacokinetic approaches and techniques to predict and assess drug exposure, additional efforts are needed to truly and fully assess dose adverse drug events. To further operationalize the recently developed tools on causality and severity, reference databases on a palette of biomarkers and outcome variables and their covariates are an obvious next step. These databases should subsequently be integrated in modeling efforts to truly explore safety outcome, including aspects associated with or caused by drug dose or exposure." "A preliminary investigation into predictive models for adverse drug events" "Jesse Davis" "Adverse drug events are a leading cause of danger and cost in health care. We could reduce both the danger and the cost if we had accurate models to predict, at prescription time for each drug, which patients are most at risk for known adverse reactions to that drug, such as myocardial infarction (MI, or “heart attack”) if given a Cox2 inhibitor, angioedema if given an ACE inhibitor, or bleeding if given an anticoagulant such asWarfarin. We address this task for the specific case of Cox2 inhibitors, a type of non-steroidal anti-inflammatory drug (NSAID) or pain reliever that is easier on the gastrointestinal system than most NSAIDS. Because of the MI adverse drug reaction, some but not all very effective Cox2 inhibitors were removed from the market. Specifically, we use machine learning to predict which patients on a Cox2 inhibitor would suffer an MI. An important issue for machine learning is that we do not know which of these patients might have suffered an MI even without the drug. To begin to make some headway on this important problem, we compare our predictive model for MI for patients on Cox2 inhibitors against a more general model for predicting" "Creatinine Trends to Detect Ibuprofen-Related Maturational Adverse Drug Events in Neonatal Life: A Simulation Study for the ELBW Newborn" "Karel Allegaert, Anne Smits" "Background: Recognizing a change in serum creatinine concentrations is useful to detect a renal adverse drug reaction signal. Assessing and characterizing the nephrotoxic side-effects of drugs in extremely low birth weight (ELBW, ≤1000 g) neonates remain challenging due to the high variability in creatinine in this population. This study aims to investigate and quantify the impact of ibuprofen treatment on kidney function, reflected by serum creatinine. Method: A recently developed dynamical model for serum creatinine was used to simulate creatinine profiles for typical, reference ELBW neonates with varying gestational and postnatal ages whilst being exposed to ibuprofen treatment. Results: The increase of serum creatinine concentrations due to ibuprofen treatment is most apparent during the first week of life. The difference in serum creatinine values between ibuprofen-exposed vs. non-exposed neonates decreases with increasing postnatal age, independent of gestational age. Conclusion: The difference in serum creatinine concentrations between ibuprofen-exposed vs. non-exposed neonates decreases with postnatal age, indicating an increased clearing capacity and resulting in a weak ibuprofen-related adverse drug reaction signal beyond early neonatal life." "Adverse drug events in pediatric intensive care are common, but improvement strategies exist and are effective" "Karel Allegaert" "Demand-driven clustering in relational domains for predicting adverse drug events" "Jesse Davis" "Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real- world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies." "Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events." "Jesse Davis" "Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies." "Predicting adverse drug events from electronic medical records" "Jesse Davis" "Learning from electronic medical records (EMR) poses many challenges from a knowledge representation point of view. This chapter focuses on how to cope with two specific challenges: the relational nature of EMRs and the uncertain dependence between a patient's past and future health status. We discuss three different approaches for allowing standard propositional learners to incorporate relational information. We evaluate these approaches on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication." "SMARTS (Systematic Monitoring of Adverse events Related to TreatmentS): The development of a pragmatic patientcompleted checklist to assess antipsychotic drug side effects" "Jozef Peuskens" "OBJECTIVES: Antipsychotic drug side effects are common and can cause stigmatisation, decreased quality of life, poor adherence, and secondary morbidity and mortality. Systematic assessment of anticipated side effects is recommended as part of good clinical care, but is uncommon in practice and patients may not spontaneously report side effects. We aimed to develop a simple patient-completed checklist to screen systematically for potential antipsychotic side effects. METHODS: The SMARTS checklist was developed over a series of group meetings by an international faculty of 12 experts (including psychiatrists, a general physician and a psychopharmacologist) based on their clinical experience and knowledge of the literature. The emphasis is on tolerability (i.e. assessment of side effects that 'trouble' the patient) as subjective impact of side effects is most relevant to medication adherence. The development took account of feedback from practising psychiatrists in Europe, the Middle East and Africa, a process that contributed to face validity. RESULTS: The SMARTS checklist assesses whether patients are currently 'troubled' by 11 well-established potential antipsychotic side effects. Patients provide their responses to these questions by circling relevant side effects. An additional open question enquires about any other possible side effects. The checklist has been translated into Italian and Turkish. CONCLUSIONS: The SMARTS checklist aims to strike a balance between brevity and capturing the most common and important antipsychotic side effects. It is appropriate for completion by patients prior to a clinical consultation, for example, in the waiting room. It can then form the focus for a more detailed clinical discussion about side effects. It can be used alone or form part of a more comprehensive assessment of antipsychotic side effects including blood tests and a physical examination when appropriate. The checklist assesses current problems and can be used longitudinally to assess change."