Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a "One Health" perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.

Statistical Guidelines for Handling Missing Data in Traumatic Brain Injury Clinical Research.

Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

A probabilistic network for the diagnosis of acute cardiopulmonary diseases.

In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented in detail. A panel of expert physicians collaborated to specify the qualitative part, which is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables into univariate conditional distributions. The quantitative part, which is a set of parametric models defining these univariate conditional distributions, was estimated following the Bayesian paradigm. In particular, we exploited an original reparameterization of Beta and categorical logistic regression models to elicit the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital records via Markov chain Monte Carlo simulation. Refinement was iteratively performed until the probabilistic network provided satisfactory concordance index values for several acute diseases and reasonable diagnosis for six fictitious patient cases. The probabilistic network can be employed to perform medical diagnosis on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167 patient findings.

Benefit-risk assessment in a post-market setting: a case study integrating real-life experience into benefit-risk methodology.

PURPOSE: Difficulties may be encountered when undertaking a benefit-risk assessment for an older product with well-established use but with a benefit-risk balance that may have changed over time. This case study investigates this specific situation by applying a formal benefit-risk framework to assess the benefit-risk balance of warfarin for primary prevention of patients with atrial fibrillation. METHODS: We used the qualitative framework BRAT as the starting point of the benefit-risk analysis, bringing together the relevant available evidence. We explored the use of a quantitative method (stochastic multi-criteria acceptability analysis) to demonstrate how uncertainties and preferences on multiple criteria can be integrated into a single measure to reduce cognitive burden and increase transparency in decision making. RESULTS: Our benefit-risk model found that warfarin is favourable compared with placebo for the primary prevention of stroke in patients with atrial fibrillation. This favourable benefit-risk balance is fairly robust to differences in preferences. The probability of a favourable benefit-risk for warfarin against placebo is high (0.99) in our model despite the high uncertainty of randomised clinical trial data. In this case study, we identified major challenges related to the identification of relevant benefit-risk criteria and taking into account the diversity and quality of evidence available to inform the benefit-risk assessment. CONCLUSION: The main challenges in applying formal methods for medical benefit-risk assessment for a marketed drug are related to outcome definitions and data availability. Data exist from many different sources (both randomised clinical trials and observational studies), and the variability in the studies is large.

Balancing benefit and risk of medicines: a systematic review and classification of available methodologies.

BACKGROUND: The need for formal and structured approaches for benefit-risk assessment of medicines is increasing, as is the complexity of the scientific questions addressed before making decisions on the benefit-risk balance of medicines. We systematically collected, appraised and classified available benefit-risk methodologies to facilitate and inform their future use. METHODS: A systematic review of publications identified benefit-risk assessment methodologies. Methodologies were appraised on their fundamental principles, features, graphical representations, assessability and accessibility. We created a taxonomy of methodologies to facilitate understanding and choice. RESULTS: We identified 49 methodologies, critically appraised and classified them into four categories: frameworks, metrics, estimation techniques and utility survey techniques. Eight frameworks describe qualitative steps in benefit-risk assessment and eight quantify benefit-risk balance. Nine metric indices include threshold indices to measure either benefit or risk; health indices measure quality-of-life over time; and trade-off indices integrate benefits and risks. Six estimation techniques support benefit-risk modelling and evidence synthesis. Four utility survey techniques elicit robust value preferences from relevant stakeholders to the benefit-risk decisions. CONCLUSIONS: Methodologies to help benefit-risk assessments of medicines are diverse and each is associated with different limitations and strengths. There is not a 'one-size-fits-all' method, and a combination of methods may be needed for each benefit-risk assessment. The taxonomy introduced herein may guide choice of adequate methodologies. Finally, we recommend 13 of 49 methodologies for further appraisal for use in the real-life benefit-risk assessment of medicines.

Differential diagnosis of pulmonary embolism in outpatients with non-specific cardiopulmonary symptoms.

Most cardiopulmonary diseases share at least one symptom with pulmonary embolism (PE). The aim of this study was to identify the most common acute causes of dyspnea, chest pain, fainting or palpitations, which diagnostic procedures were performed and whether clinicians investigate them appropriately. An Italian multicenter collaboration gathered 17,497 Emergency Department (ED) records of patients admitted from January 2007 to June 2007 in six hospitals. A block random sampling procedure was applied to select 800 hospitalised patients. Results of the overall 17,497 patients were obtained by weighting sampled cases according to the probability of the randomisation block variables in the whole population. The case-mix of enrolled patients was assessed in terms of cardiopulmonary symptoms, and the prevalence of acute disorders. The actual performance of procedures was compared with a measure of their accuracy as expected in the most common clinical presentations. PE occurred in less than 4% of patients with cardiopulmonary symptoms. Acute heart failure, pneumonia and chronic obstructive pulmonary disease exacerbation were the most likely diagnoses in patients with dyspnea. Acute myocardial infarction was present in roughly 10% of patients with chest pain. Atrial fibrillation was the prevalent diagnosis in patients with palpitations. Echocardiography, computed tomographic pulmonary angiography, perfusion lung scan, D-dimer test and B-type natriuretic peptide were performed less than expected from their accuracy. Diagnostic strategies, starting from non-specific symptoms and coping with the eventuality of PE, are likely to benefit from an increased awareness of the examination's accuracy in discriminating among several competing hypotheses, rather than in testing the single PE suspicion.

From networks of protein interactions to networks of functional dependencies.

BACKGROUND: As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation). However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. RESULTS: Reasoning that topological features (e.g., clusters of highly inter-connected proteins) might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations), based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud) or biological processes (e.g., cell budding) of the model organism S. cerevisiae. CONCLUSIONS: The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms.

Automated interviews on clinical case reports to elicit directed acyclic graphs.

OBJECTIVE: Setting up clinical reports within hospital information systems makes it possible to record a variety of clinical presentations. Directed acyclic graphs (Dags) offer a useful way of representing causal relations in clinical problem domains and are at the core of many probabilistic models described in the medical literature, like Bayesian networks. However, medical practitioners are not usually trained to elicit Dag features. Part of the difficulty lies in the application of the concept of direct causality before selecting all the causal variables of interest for a specific patient. We designed an automated interview to tutor medical doctors in the development of Dags to represent their understanding of clinical reports. METHODS: Medical notions were analyzed to find patterns in medical reasoning that can be followed by algorithms supporting the elicitation of causal Dags. Clinical relevance was defined to help formulate only relevant questions by driving an expert's attention towards variables causally related to nodes already inserted in the graph. Key procedural features of the proposed interview are described by four algorithms. RESULTS: The automated interview comprises questions on medical notions, phrased in medical terms. The first elicitation session produces questions concerning the patient's chief complaints and the outcomes related to diseases serving as diagnostic hypotheses, their observable manifestations and risk factors. The second session focuses on questions that refine the initial causal paths by considering syndromes, dysfunctions, pathogenic anomalies, biases and effect modifiers. A case study concerning a gastro-enterological problem and one dealing with an infected patient illustrate the output produced by the algorithms, depending on the answers provided by the doctor. CONCLUSIONS: The proposed elicitation framework is characterized by strong consistency with medical background and by a progressive introduction of relevant medical topics. Revision and testing of the subjectively elicited Dag is performed by matching the collected answers with the evidence included in accepted sources of biomedical knowledge.

Calibration belt for quality-of-care assessment based on dichotomous outcomes.

Prognostic models applied in medicine must be validated on independent samples, before their use can be recommended. The assessment of calibration, i.e., the model's ability to provide reliable predictions, is crucial in external validation studies. Besides having several shortcomings, statistical techniques such as the computation of the standardized mortality ratio (SMR) and its confidence intervals, the Hosmer-Lemeshow statistics, and the Cox calibration test, are all non-informative with respect to calibration across risk classes. Accordingly, calibration plots reporting expected versus observed outcomes across risk subsets have been used for many years. Erroneously, the points in the plot (frequently representing deciles of risk) have been connected with lines, generating false calibration curves. Here we propose a methodology to create a confidence band for the calibration curve based on a function that relates expected to observed probabilities across classes of risk. The calibration belt allows the ranges of risk to be spotted where there is a significant deviation from the ideal calibration, and the direction of the deviation to be indicated. This method thus offers a more analytical view in the assessment of quality of care, compared to other approaches.

Food fried in extra-virgin olive oil improves postprandial insulin response in obese, insulin-resistant women.

The benefits of low glycemic load (GL) diets on clinical outcome in several metabolic and cardiovascular diseases have extensively been demonstrated. The GL of a meal can be affected by modulating the bioavailability of carbohydrates or by changing food preparation. We investigated the effect on plasma glucose and insulin response in lean and obese women of adding raw or fried extra-virgin olive oil to a carbohydrate-containing meal. After an overnight fast, 12 obese insulin-resistant women (body mass index [BMI], 32.8 ± 2.2 kg/m(2)) and five lean subjects (BMI, 22.2 ± 1.2 kg/m(2)) were randomly assigned to receive two different meals (designated A and B). Meal A was composed of 60 g of pasta made from wheat flour and 150 g of grilled courgettes with 25 g of uncooked oil. Meal B included 15 g of oil in the 150 g of deep-fried courgettes and 10 g of oil in the 60 g of stir-fried pasta. Both meals included 150 g of apple. Blood samples were collected at baseline and every 30 minutes over a 3-hour post-meal period and were tested for levels of glucose, insulin, C-peptide, and triglycerides. The area under the curve (AUC) values were calculated. In obese women the AUCs for C-peptide were significantly higher after meal A than after meal B at 120 minutes (W [Wilcoxon sign rank test] = 27.5, P = .0020), 150 minutes (W = 26.5, P = .0039), and 180 minutes (W = 26.5, P = .0039). No differences were found in lean subjects. This study demonstrated that in obese, insulin-resistant women, food fried in extra-virgin olive oil significantly reduced both insulin and C-peptide responses after a meal.

Outcome of children admitted to adult intensive care units in Italy between 2003 and 2007.

BACKGROUND: Centralisation of critically ill children to paediatric intensive care units is supported by a strong rationale, but evidence is not overwhelming. OBJECTIVE: To compare the outcome of children admitted to adult intensive care units (ICUs) in Italy between 2003 and 2007 with that of children admitted to paediatric intensive care units (PICUs) in Italy between 1994 and 1995. METHODS: Prospective, multicenter cohort study and historical controls. Risk of ICU mortality was assessed with the PRISM score in both study and historical control groups. Descriptive statistics, standardized mortality ratios (SMRs) with their 95% confidence intervals, and the calibration plots were reported. RESULTS: A total of 1,265 children admitted to 124 adult ICUs between 2003 and 2007 were compared with an historical control group formed by 1,533 children admitted to 26 PICUs between 1994 and 1995. The PRISM score slightly underestimated hospital deaths for low-risk patients in both groups. The overall SMR was 1.11 (95% CI 0.91-1.31) for adult ICUs and 1.04 (95% CI: 0.88-1.19) for PICUs. CONCLUSIONS: The level of care provided nowadays to children admitted to adult ICUs in Italy is similar to that provided by Italian PICUs 10 years earlier. On the other hand, there is evidence that Italian PICUs have improved the level of care in the same period. These findings, if confirmed, suggest a better quality of care for children admitted to PICUs as compared to adult ICUs and support the indication, when possible, of early referral to more specialized units in countries where paediatric intensive care is not centralised.

Bayes pulmonary embolism assisted diagnosis: a new expert system for clinical use.

BACKGROUND: The diagnosis of pulmonary embolism demands flexible decision models, both for the presence of clinical confounders and for the variability of local diagnostic resources. As Bayesian networks fully meet this requirement, Bayes Pulmonary embolism Assisted Diagnosis (BayPAD), a probabilistic expert systems focused on pulmonary embolism, was developed. METHODS: To quantitatively validate and improve BayPAD, the system was applied to 750 patients from a prospective study done in an Italian tertiary hospital where the true pulmonary embolism status was confirmed using pulmonary angiography or ruled out with a lung scan. The proportion of correct diagnoses made by BayPAD (accuracy) and the correctness of the pulmonary embolism probabilities predicted by the model (calibration) were calculated. The calibration was evaluated according to the Cox regression-calibration model. RESULTS: Before refining the model, accuracy was 88.6%. Once refined, accuracy was 97.2% and 98%, respectively, in the training and validation samples. According to Cox analysis, calibration was satisfactory, despite a tendency to exaggerate the effect of the findings on the probability of pulmonary embolism. The lack of some investigations (like Spiral computed tomographic scan and Lower limbs doppler ultrasounds) in the pool of available data often prevents BayPAD from reaching the diagnosis without invasive procedures. CONCLUSIONS: BayPAD offers clinicians a flexible and accurate strategy to diagnose pulmonary embolism. Simple to use, the system performs case-based reasoning to optimise the use of resources available within a particular hospital. Bayesian networks are expected to have a prominent role in the clinical management of complex diagnostic problems in the near future.

Simplified electrophysiological evaluation of peripheral nerves in critically ill patients: the Italian multi-centre CRIMYNE study.

INTRODUCTION: Critical illness myopathy and/or neuropathy (CRIMYNE) is frequent in intensive care unit (ICU) patients. Although complete electrophysiological tests of peripheral nerves and muscles are essential to diagnose it, they are time-consuming, precluding extensive use in daily ICU practice. We evaluated whether a simplified electrophysiological investigation of only two nerves could be used as an alternative to complete electrophysiological tests. METHODS: In this prospective, multi-centre study, 92 ICU patients were subjected to unilateral daily measurements of the action potential amplitude of the sural and peroneal nerves (compound muscle action potential [CMAP]). After the first ten days, complete electrophysiological investigations were carried out weekly until ICU discharge or death. At hospital discharge, complete neurological and electrophysiological investigations were performed. RESULTS: Electrophysiological signs of CRIMYNE occurred in 28 patients (30.4%, 95% confidence interval [CI] 21.9% to 40.4%). A unilateral peroneal CMAP reduction of more than two standard deviations of normal value showed the best combination of sensitivity (100%) and specificity (67%) in diagnosing CRIMYNE. All patients developed the electrophysiological signs of CRIMYNE within 13 days of ICU admission. Median time from ICU admission to CRIMYNE was six days (95% CI five to nine days). In 10 patients, the amplitude of the nerve action potential dropped progressively over a median of 3.0 days, and in 18 patients it dropped abruptly within 24 hours. Multi-organ failure occurred in 21 patients (22.8%, 95% CI 15.4% to 32.4%) and was strongly associated with CRIMYNE (odds ratio 4.58, 95% CI 1.64 to 12.81). Six patients with CRIMYNE died: three in the ICU and three after ICU discharge. Hospital mortality was similar in patients with and without CRIMYNE (21.4% and 17.2%; p = 0.771). At ICU discharge, electrophysiological signs of CRIMYNE persisted in 18 (64.3%) of 28 patients. At hospital discharge, diagnoses in the 15 survivors were critical illness myopathy (CIM) in six cases, critical illness polyneuropathy (CIP) in four, combined CIP and CIM in three, and undetermined in two. CONCLUSION: A peroneal CMAP reduction below two standard deviations of normal value accurately identifies patients with CRIMYNE. These should have full neurological and neurophysiological evaluations before discharge from the acute hospital.

Immunonutrition in septic patients: a philosophical view of the current situation.

BACKGROUND & AIMS: Two different ways of thinking pervaded the history of science: rationalism and empiricism. In theory, these two paradigms are not necessarily in conflict. In practice, there has always been tension between them. The coming of evidence-based medicine put empiricism in a privileged position, but empiricism without a rationalistic guide could even be usefulness. The aim of this work is to present the tension between the rational reasons to administer immunonutrients to patients with sepsis and the controversial empirical evidence stemming from clinical trials. METHODS: We reviewed the literature on immunonutrition in sepsis from the rationalist and the empiricist perspectives. RESULTS: The large body of evidence for positive effects of immunonutrients in experimental models and the contradictory results from clinical trials make the discussion on immunonutrition in sepsis a typical example where the conflict between rationalism and empiricism hampered the advancement of knowledge and the implementation of new effective therapies into clinical practice. CONCLUSIONS: Future research projects involving immunonutrients should be based on robust knowledge of basic mechanisms of action to be properly addressed in clinical trials.

Lupus anticoagulants are stronger risk factors for thrombosis than anticardiolipin antibodies in the antiphospholipid syndrome: a systematic review of the literature.

To formally establish the risk of lupus anticoagulants and anticardiolipin antibodies for arterial and venous thrombosis, we ran a MEDLINE search of the literature from 1988 to 2000. Studies were selected for their case-control (11), prospective (9), cross-sectional (3), and ambispective (2) design. They provided or enabled us to calculate the odds ratio with 95% confidence interval (CI) of lupus anticoagulants and/or anticardiolipin antibodies for thrombosis in 4184 patients and 3151 controls. Studies were grouped according to the antibody investigated. Five studies compared lupus anticoagulants with anticardiolipin antibodies: the odds ratio with 95% CI of lupus anticoagulants for thrombosis was always significant. None of them found anticardiolipin antibodies were associated with thrombosis. Four studies analyzed only lupus anticoagulants: the odds ratio with 95% CI was always significant. The risk of lupus anticoagulants was independent of the site and type of thrombosis, the presence of systemic lupus erythematosus, and the coagulation tests employed to detect them. Sixteen studies served to assess 28 associations between anticardiolipin antibodies and thrombosis: the odds ratio with 95% CI was significant in 15 cases. Anticardiolipin titer correlated with the odds ratio of thrombosis. In conclusion, the detection of lupus anticoagulants and, possibly, of immunoglobulin G (IgG) anticardiolipin antibodies at medium or high titers helps to identify patients at risk for thrombosis. However, to take full advantage of the conclusions provided by the available evidence, there is an urgent need to harmonize investigational methods.

Top-down costing: problems in determining staff costs in intensive care medicine.

OBJECTIVE: To describe the activities carried out by the staff of Italian ICUs and to quantify the amount of working time devoted to ICU patients. DESIGN AND SETTING: Prospective, observational, multicenter study in 110 ICUs to report the non-ICU-related activities performed by ICU staff, together with the time such activities require. Of the 110 ICUs 80 participated in the project. MEASUREMENTS AND RESULTS: We found substantial variation in the number of activities carried out and in the working time allocated to such activities. Considering the differences in the number of employees, their salaries, and the amount of time spent performing various activities, it was found that the personnel cost for ICU activity was 83.4% (range 55-100%) of the total personnel costs. CONCLUSIONS: Given the wide variation in the number of activities performed and in the proportion of working time spent performing non-ICU related activities, data comparing costs between different ICUs should be interpreted with caution.