Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

BioPARR:A software system for estimating the rupture potential index for abdominal aortic aneurysms

An abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that, if left untreated, can expand until rupture. Despite ongoing efforts, an efficient tool for accurate estimation of AAA rupture risk is still not available. Furthermore, a lack of standardisation across current approaches and specific obstacles within computational workflows limit the translation of existing methods to the clinic. This paper presents BioPARR (Biomechanics based Prediction of Aneurysm Rupture Risk), a software system to facilitate the analysis of AAA using a finite element analysis based approach. Except semi-automatic segmentation of the AAA and intraluminal thrombus (ILT) from medical images, the entire analysis is performed automatically. The system is modular and easily expandable, allows the extraction of information from images of different modalities (e.g. CT and MRI) and the simulation of different modelling scenarios (e.g. with/without thrombus). The software uses contemporary methods that eliminate the need for patient-specific material properties, overcoming perhaps the key limitation to all previous patient-specific analysis methods. The software system is robust, free, and will allow researchers to perform comparative evaluation of AAA using a standardised approach. We report preliminary data from 48 cases

Capture the fracture: a best practice framework and global campaign to break the fragility fracture cycle

Summary The International Osteoporosis Foundation (IOF) Capture the Fracture Campaign aims to support implementation of Fracture Liaison Services (FLS) throughout the world. Introduction FLS have been shown to close the ubiquitous secondary fracture prevention care gap, ensuring that fragility fracture sufferers receive appropriate assessment and intervention to reduce future fracture risk. Methods Capture the Fracture has developed internationally endorsed standards for best practice, will facilitate change at the national level to drive adoption of FLS and increase awareness of the challenges and opportunities presented by secondary fracture prevention to key stakeholders. The Best Practice Framework (BPF) sets an international benchmark for FLS, which defines essential and aspirational elements of service delivery. Results The BPF has been reviewed by leading experts from many countries and subject to beta-testing to ensure that it is internationally relevant and fit-for-purpose. The BPF will also serve as a measurement tool for IOF to award ‘Capture the Fracture Best Practice Recognition’ to celebrate successful FLS worldwide and drive service development in areas of unmet need. The Capture the Fracture website will provide a suite of resources related to FLS and secondary fracture prevention, which will be updated as new materials become available. A mentoring programme will enable those in the early stages of development of FLS to learn from colleagues elsewhere that have achieved Best Practice Recognition. A grant programme is in development to aid clinical systems which require financial assistance to establish FLS in their localities. Conclusion Nearly half a billion people will reach retirement age during the next 20 years. IOF has developed Capture the Fracture because this is the single most important thing that can be done to directly improve patient care, of both women and men, and reduce the spiralling fracture-related care costs worldwide.</p

Serum procalcitonin for the early recognition of nosocomial infection in the critically ill patients: a preliminary report

<p>Abstract</p> <p>Background</p> <p>The usefulness of procalcitonin (PCT) measurement in critically ill medical patients with suspected nosocomial infection is unclear. The aim of the study was to assess PCT value for the early diagnosis of bacterial nosocomial infection in selected critically ill patients.</p> <p>Methods</p> <p>An observational cohort study in a 15-bed intensive care unit was performed. Seventy patients with either proven (n = 47) or clinically suspected but not confirmed (n = 23) nosocomial infection were included. Procalcitonin measurements were obtained the day when the infection was suspected (D0) and at least one time within the 3 previous days (D-3 to D0). Patients with proven infection were compared to those without. The diagnostic value of PCT on D0 was determined through the construction of the corresponding receiver operating characteristic (ROC) curve. In addition, the predictive value of PCT variations preceding the clinical suspicion of infection was assessed.</p> <p>Results</p> <p>PCT on D0 was the best predictor of proven infection in this population of ICU patients with a clinical suspicion of infection (AUROCC = 0.80; 95% CI, 0.68–0.91). Thus, a cut-off value of 0.44 ng/mL provides sensitivity and specificity of 65.2% and 83.0%, respectively. Procalcitonin variation between D-1 and D0 was calculated in 45 patients and was also found to be predictive of nosocomial infection (AUROCC = 0.89; 95% CI, 0.79–0.98) with a 100% positive predictive value if the +0.26 ng/mL threshold value was applied. Comparable results were obtained when PCT variation between D-2 and D0, or D-3 and D0 were considered. In contrast, CRP elevation, leukocyte count and fever had a poor predictive value in our population.</p> <p>Conclusion</p> <p>PCT monitoring could be helpful in the early diagnosis of nosocomial infection in the ICU. Both absolute values and variations should be considered and evaluated in further studies.</p

Multicenter Evaluation of a Novel Surveillance Paradigm for Complications of Mechanical Ventilation

Ventilator-associated pneumonia (VAP) surveillance is time consuming, subjective, inaccurate, and inconsistently predicts outcomes. Shifting surveillance from pneumonia in particular to complications in general might circumvent the VAP definition's subjectivity and inaccuracy, facilitate electronic assessment, make interfacility comparisons more meaningful, and encourage broader prevention strategies. We therefore evaluated a novel surveillance paradigm for ventilator-associated complications (VAC) defined by sustained increases in patients' ventilator settings after a period of stable or decreasing support.We assessed 600 mechanically ventilated medical and surgical patients from three hospitals. Each hospital contributed 100 randomly selected patients ventilated 2-7 days and 100 patients ventilated >7 days. All patients were independently assessed for VAP and for VAC. We compared incidence-density, duration of mechanical ventilation, intensive care and hospital lengths of stay, hospital mortality, and time required for surveillance for VAP and for VAC. A subset of patients with VAP and VAC were independently reviewed by a physician to determine possible etiology.Of 597 evaluable patients, 9.3% had VAP (8.8 per 1,000 ventilator days) and 23% had VAC (21.2 per 1,000 ventilator days). Compared to matched controls, both VAP and VAC prolonged days to extubation (5.8, 95% CI 4.2-8.0 and 6.0, 95% CI 5.1-7.1 respectively), days to intensive care discharge (5.7, 95% CI 4.2-7.7 and 5.0, 95% CI 4.1-5.9), and days to hospital discharge (4.7, 95% CI 2.6-7.5 and 3.0, 95% CI 2.1-4.0). VAC was associated with increased mortality (OR 2.0, 95% CI 1.3-3.2) but VAP was not (OR 1.1, 95% CI 0.5-2.4). VAC assessment was faster (mean 1.8 versus 39 minutes per patient). Both VAP and VAC events were predominantly attributable to pneumonia, pulmonary edema, ARDS, and atelectasis.Screening ventilator settings for VAC captures a similar set of complications to traditional VAP surveillance but is faster, more objective, and a superior predictor of outcomes

Computational classifiers for predicting the short-term course of Multiple sclerosis

The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). METHODS: We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. RESULTS: We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. CONCLUSIONS: The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability

Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

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