Healthcare-associated infections are associated with insufficient dietary intake: an observational cross-sectional study.

BACKGROUND: Indicators to predict healthcare-associated infections (HCAI) are scarce. Malnutrition is known to be associated with adverse outcomes in healthcare but its identification is time-consuming and rarely done in daily practice. This cross-sectional study assessed the association between dietary intake, nutritional risk, and the prevalence of HCAI, in a general hospital population. METHODS AND FINDINGS: Dietary intake was assessed by dedicated dieticians on one day for all hospitalized patients receiving three meals per day. Nutritional risk was assessed using Nutritional Risk Screening (NRS)-2002, and defined as a NRS score ≥ 3. Energy needs were calculated using 110% of Harris-Benedict formula. HCAIs were diagnosed based on the Center for Disease Control criteria and their association with nutritional risk and measured energy intake was done using a multivariate logistic regression analysis. From 1689 hospitalised patients, 1024 and 1091 were eligible for the measurement of energy intake and nutritional risk, respectively. The prevalence of HCAI was 6.8%, and 30.1% of patients were at nutritional risk. Patients with HCAI were more likely identified with decreased energy intake (i.e. ≤ 70% of predicted energy needs) (30.3% vs. 14.5%, P = 0.002). The proportion of patients at nutritional risk was not significantly different between patients with and without HCAI (35.6% vs.29.7%, P = 0.28), respectively. Measured energy intake ≤ 70% of predicted energy needs (odds ratio: 2.26; 95% CI: 1.24 to 4.11, P = 0.008) and moderate severity of the disease (odds ratio: 3.38; 95% CI: 1.49 to 7.68, P = 0.004) were associated with HCAI in the multivariate analysis. CONCLUSION: Measured energy intake ≤ 70% of predicted energy needs is associated with HCAI in hospitalised patients. This suggests that insufficient dietary intake could be a risk factor of HCAI, without excluding reverse causality. Randomized trials are needed to assess whether improving energy intake in patients identified with decreased dietary intake could be a novel strategy for HCAI prevention.

Risk of rupture of small anterior communicating artery aneurysms is similar to posterior circulation aneurysms.

BACKGROUND AND PURPOSE: According to the International Study of Unruptured Intracranial Aneurysms (ISUIA), anterior circulation (AC) aneurysms of <7 mm in diameter have a minimal risk of rupture. It is general experience, however, that anterior communicating artery (AcoA) aneurysms are frequent and mostly rupture at <7 mm. The aim of the study was to assess whether AcoA aneurysms behave differently from other AC aneurysms. METHODS: Information about 932 patients newly diagnosed with intracranial aneurysms between November 1, 2006, and March 31, 2012, including aneurysm status at diagnosis, its location, size, and risk factors, was collected during the multicenter @neurIST project. For each location or location and size subgroup, the odds ratio (OR) of aneurysms being ruptured at diagnosis was calculated. RESULTS: The OR for aneurysms to be discovered ruptured was significantly higher for AcoA (OR, 3.5 [95% confidence interval, 2.6-4.5]) and posterior circulation (OR, 2.6 [95% confidence interval, 2.1-3.3]) than for AC excluding AcoA (OR, 0.5 [95% confidence interval, 0.4-0.6]). Although a threshold of 7 mm has been suggested by ISUIA as a threshold for aggressive treatment, AcoA aneurysms <7 mm were more frequently found ruptured (OR, 2.0 [95% confidence interval, 1.3-3.0]) than AC aneurysms of 7 to 12 mm diameter as defined in ISUIA. CONCLUSIONS: We found that AC aneurysms are not a homogenous group. Aneurysms between 4 and 7 mm located in AcoA or distal anterior cerebral artery present similar rupture odds to posterior circulation aneurysms. Intervention should be recommended for this high-risk lesion group.

Measuring the effectiveness of hospital-acquired infection prevention.

This article deals with data on nosocomial infections acquired in the Geneva University Hospitals. Goal of the work is to derive a model from a hospital-acquired infection (HAI) prevalence survey of year Y and apply them to a prevalence survey of years Y+1, Y+2. This analysis permits to evaluate the effectiveness of preventive measures taken after the prevalence survey in year Y. It also analyzes the robustness of the SVM algorithm on time-variable attributes. The model build on the dataset of year Y gives better results than in a previous study. The application of the model on the Y+1 and Y+2 prevalence surveys shows simultaneously improvements and deteriorations of 5 performance measures. This highlights the effectiveness of prevention and reduces the risk of HAI after the prevalence survey of year Y. We introduce a new method to detect redundancy in a dataset with the SVM algorithm.

Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography.

OBJECTIVE: We investigate the influence of the clinical context of high-resolution computed tomography (HRCT) images of the chest on tissue classification. METHODS AND MATERIALS: 2D regions of interest in HRCT axial slices from patients affected with an interstitial lung disease are automatically classified into five classes of lung tissue. Relevance of the clinical parameters is studied before fusing them with visual attributes. Two multimedia fusion techniques are compared: early versus late fusion. Early fusion concatenates features in one single vector, yielding a true multimedia feature space. Late fusion consisting of the combination of the probability outputs of two support vector machines. RESULTS AND CONCLUSION: The late fusion scheme allowed a maximum of 84% correct predictions of testing instances among the five classes of lung tissue. This represents a significant improvement of 10% compared to a pure visual-based classification. Moreover, the late fusion scheme showed high robustness to the number of clinical parameters used, which suggests that it is appropriate for mining clinical attributes with missing values in clinical routine.

@neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services.

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

Strategies for health data exchange for secondary, cross-institutional clinical research.

Secondary use of health data has a vital role in improving and advancing medical knowledge. While digital health records offer scope for facilitating the flow of data to secondary uses, it remains essential that steps are taken to respect wishes of the patient regarding secondary usage, and to ensure the privacy of the patient during secondary use scenarios. Consent, together with depersonalisation and its related concepts of anonymisation, pseudonymisation, and data minimisation are key methods used to provide this protection. This paper gives an overview of technical, practical, legal, and ethical aspects of secondary data use and discusses their implementation in the multi-institutional @neurIST research project.

Comparative performance analysis of state-of-the-art classification algorithms applied to lung tissue categorization.

In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k-nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar's statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROIs.

Design of a decentralized reusable research database architecture to support data acquisition in large research projects.

The diagnostic and therapeutic processes, as well as the development of new treatments, are hindered by the fragmentation of information which underlies them. In a multi-institutional research study database, the clinical information system (CIS) contains the primary data input. An important part of the money of large scale clinical studies is often paid for data creation and maintenance. The objective of this work is to design a decentralized, scalable, reusable database architecture with lower maintenance costs for managing and integrating distributed heterogeneous data required as basis for a large-scale research project. Technical and legal aspects are taken into account based on various use case scenarios. The architecture contains 4 layers: data storage and access are decentralized at their production source, a connector as a proxy between the CIS and the external world, an information mediator as a data access point and the client side. The proposed design will be implemented inside six clinical centers participating in the @neurIST project as part of a larger system on data integration and reuse for aneurism treatment.

KnowARC: enabling Grid networks for the biomedical research community.

The vast availability of medical patient data in digital format creates the opportunity to use these data in medical informatics research projects. The objective is to improve future care by providing the medical staff with methods for automated data processing, including textual and visual information analysis and retrieval from medical databases. Many medical institutions do not possess a specific research computing infrastructure or the required budget for such an infrastructure to enable processing of these large amounts of data. Still, many institutions have many desktop PCs that could serve for biomedical research during the time they are little used without the need for expensive investments. The KnowARC project aims at building a middleware for such a simple-to-install Grid network. This article reviews requirements for computing Grids in large hospital environments. We use the computing infrastructure in the University Hospitals of Geneva as an example, and then present the solutions that the European Union-funded KnowARC project plans to undertake to solve the current problems. Methods currently employed in common Grid middleware distributions are also reviewed and assessed in relation to the goals of KnowARC. The computing infrastructure at the University Hospitals of Geneva is described as well as the needs and requirements for computing and storage services within this domain. A list of requirements for a Grid middleware to employ in such a challenging environment is developed. Finally, the proposed solutions and ideas of the KnowARC project are described in detail to present the project to a larger community. First proof of concept implementations and test results are described to illustrate how Grid networks are expected to become an important supplier of computational resources, which are required in several domains in biomedical research. A continuous process will be necessary to feed in the requirements of the biomedical domain to developers of Grid middleware to make the outcome meet the specific needs of the biomedical community.

Knowledge acquisition for computation of semantic distance between WHO-ART terms.

Computation of semantic distance between adverse drug reactions terms may be an efficient way to group related medical conditions in pharmacovigilance case reports. Previous experience with ICD-10 on a semantic distance tool highlighted a bottleneck related to manual description of formal definitions in large terminologies. We propose a method based on acquisition of formal definitions by knowledge extraction from UMLS and morphosemantic analysis. These formal definitions are expressed with SNOMED International terms. We provide formal definitions for 758 WHO-ART terms: 321 terms defined from UMLS, 320 terms defined using morphosemantic analysis and 117 terms defined after expert evaluation. Computation of semantic distance (e.g. k-nearest neighbours) was implemented in J2EE terminology services. Similar WHO-ART terms defined by automated knowledge acquisition and ICD terms defined manually show similar behaviour in the semantic distance tool. Our knowledge acquisition method can help us to generate new formal definitions of medical terms for our semantic distance terminology services.

Clustering WHO-ART terms using semantic distance and machine learning algorithms.

WHO-ART was developed by the WHO collaborating centre for international drug monitoring in order to code adverse drug reactions. We assume that computation of semantic distance between WHO-ART terms may be an efficient way to group related medical conditions in the WHO database in order to improve signal detection. Our objective was to develop a method for clustering WHO-ART terms according to some proximity of their meanings. Our material comprises 758 WHO-ART terms. A formal definition was acquired for each term as a list of elementary concepts belonging to SNOMED international axes and characterized by modifier terms in some cases. Clustering was implemented as a terminology service on a J2EE server. Two different unsupervised machine learning algorithms (KMeans, Pvclust) clustered WHO-ART terms according to a semantic distance operator previously described. Pvclust grouped 51% of WHO-ART terms. K-Means grouped 100% of WHO-ART terms but 25% clusters were heterogeneous with k = 180 clusters and 6% clusters were heterogeneous with k = 32 clusters. Clustering algorithms associated to semantic distance could suggest potential groupings of WHO-ART terms that need validation according to the user's requirements.