Aerial dissemination of Clostridium difficile spores

Background: Clostridium difficile-associated diarrhoea (CDAD) is a frequently occurring healthcare-associated infection, which is responsible for significant morbidity and mortality amongst elderly patients in healthcare facilities. Environmental contamination is known to play an important contributory role in the spread of CDAD and it is suspected that contamination might be occurring as a result of aerial dissemination of C. difficile spores. However previous studies have failed to isolate C. difficile from air in hospitals. In an attempt to clarify this issue we undertook a short controlled pilot study in an elderly care ward with the aim of culturing C. difficile from the air. Methods: In a survey undertaken during February (two days) 2006 and March (two days) 2007, air samples were collected using a portable cyclone sampler and surface samples collected using contact plates in a UK hospital. Sampling took place in a six bedded elderly care bay (Study) during February 2006 and in March 2007 both the study bay and a four bedded orthopaedic bay (Control). Particulate material from the air was collected in Ringer's solution, alcohol shocked and plated out in triplicate onto Brazier's CCEY agar without egg yolk, but supplemented with 5 mg/L of lysozyme. After incubation, the identity of isolates was confirmed by standard techniques. Ribotyping and REP-PCR fingerprinting were used to further characterise isolates. Results: On both days in February 2006, C. difficile was cultured from the air with 23 samples yielding the bacterium (mean counts 53 – 426 cfu/m3 of air). One representative isolate from each of these was characterized further. Of the 23 isolates, 22 were ribotype 001 and were indistinguishable on REP-PCR typing. C. difficile was not cultured from the air or surfaces of either hospital bay during the two days in March 2007. Conclusion: This pilot study produced clear evidence of sporadic aerial dissemination of spores of a clone of C. difficile, a finding which may help to explain why CDAD is so persistent within hospitals and difficult to eradicate. Although preliminary, the findings reinforce concerns that current C. difficile control measures may be inadequate and suggest that improved ward ventilation may help to reduce the spread of CDAD in healthcare facilities

Bacterial contamination of inanimate surfaces and equipment in the intensive care unit

Intensive care unit (ICU)-acquired infections are a challenging health problem worldwide, especially when caused by multidrug-resistant (MDR) pathogens. In ICUs, inanimate surfaces and equipment (e.g., bedrails, stethoscopes, medical charts, ultrasound machine) may be contaminated by bacteria, including MDR isolates. Cross-transmission of microorganisms from inanimate surfaces may have a significant role for ICU-acquired colonization and infections. Contamination may result from healthcare workers' hands or by direct patient shedding of bacteria which are able to survive up to several months on dry surfaces. A higher environmental contamination has been reported around infected patients than around patients who are only colonized and, in this last group, a correlation has been observed between frequency of environmental contamination and culture-positive body sites. Healthcare workers not only contaminate their hands after direct patient contact but also after touching inanimate surfaces and equipment in the patient zone (the patient and his/her immediate surroundings). Inadequate hand hygiene before and after entering a patient zone may result in cross-transmission of pathogens and patient colonization or infection. A number of equipment items and commonly used objects in ICU carry bacteria which, in most cases, show the same antibiotic susceptibility profiles of those isolated from patients. The aim of this review is to provide an updated evidence about contamination of inanimate surfaces and equipment in ICU in light of the concept of patient zone and the possible implications for bacterial pathogen cross-transmission to critically ill patients

Clostridium difficile infection.

Infection of the colon with the Gram-positive bacterium Clostridium difficile is potentially life threatening, especially in elderly people and in patients who have dysbiosis of the gut microbiota following antimicrobial drug exposure. C. difficile is the leading cause of health-care-associated infective diarrhoea. The life cycle of C. difficile is influenced by antimicrobial agents, the host immune system, and the host microbiota and its associated metabolites. The primary mediators of inflammation in C. difficile infection (CDI) are large clostridial toxins, toxin A (TcdA) and toxin B (TcdB), and, in some bacterial strains, the binary toxin CDT. The toxins trigger a complex cascade of host cellular responses to cause diarrhoea, inflammation and tissue necrosis - the major symptoms of CDI. The factors responsible for the epidemic of some C. difficile strains are poorly understood. Recurrent infections are common and can be debilitating. Toxin detection for diagnosis is important for accurate epidemiological study, and for optimal management and prevention strategies. Infections are commonly treated with specific antimicrobial agents, but faecal microbiota transplants have shown promise for recurrent infections. Future biotherapies for C. difficile infections are likely to involve defined combinations of key gut microbiota

Survival and dispersal of a defined cohort of Irish cattle

An understanding of livestock movement is critical to effective disease prevention, control and prediction. However, livestock movement in Ireland has not yet been quantified. This study has sought to define the survival and dispersal of a defined cohort of cattle born in Co. Kerry during 2000. The cohort was observed for a maximum of four years, from January 1, 2000 to December 31, 2004. Beef and dairy animals moved an average 1.31 and 0.83 times, respectively. At study end, 18.8% of the beef animals remained alive on Irish farms, including 6.7% at the farm-of-birth, compared with 48.6% and 27.7% for dairy animals respectively. Beef animals werae dispersed to all Irish counties, but mainly to Cork, Limerick, Tipperary and Galway. Dairy animals mainly moved to Cork, Limerick, and Tipperary, with less animals going to Galway, Meath and Kilkenny. The four-year survival probability was 0.07 (male beef animals), 0.25 (male dairy), 0.38 (female beef), and 0.72 (female dairy). Although there was considerable dispersal, the number of moves per animal was less than expected

Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease

<p>Abstract</p> <p>Background</p> <p>Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.</p> <p>Methods</p> <p>Using a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types.</p> <p>Results</p> <p>Annotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield.</p> <p>Conclusion</p> <p>Challenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.</p

Automatic de-identification of textual documents in the electronic health record: a review of recent research

<p>Abstract</p> <p>Background</p> <p>In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.</p> <p>Methods</p> <p>This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers.</p> <p>Results</p> <p>The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries.</p> <p>Conclusions</p> <p>In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.</p