Davide Luciani

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

IntroductionCritical 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.MethodsIn 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.ResultsElectrophysiological 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.ConclusionA 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.

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

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.

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 models 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.

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.

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.

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

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.

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

Abstract 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.

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 experts 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 patients 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.

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.