Detection of Staphylococcus aureus by 16S rRNA directed in situ hybridisation in a patient with a brain abscess caused by small colony variants.

Kipp F, Ziebuhr W, Becker K, Krimmer V, Höbeta N, Peters G, Von Eiff C. Institute of Medical Microbiology, Hospital and Clinics, University of Münster, Germany. A 45 year old man was admitted to hospital with a right sided facial paralysis and three month history of seizures. Computed tomography showed a left temporal mass including both intracerebral and extracerebral structures. Ten years earlier the patient had undergone a neurosurgical intervention in the same anatomical region to treat a subarachnoid haemorrhage. In tissue samples and pus obtained during neurosurgery, Staphylococcus aureus was detected by a 16S rRNA-directed in situ hybridisation technique. Following long term cultivation, small colony variants (SCV) of methicillin resistant S aureus were identified. The patient was treated successfully with a combination of vancomycin and rifampin followed by prolonged treatment with teicoplanin, with no sign of infection on follow up nine months after discharge. This is the first report in which S aureus SCV have been identified as causative organisms in a patient with brain abscess and in which in situ hybridisation has been used to detect S aureus in a clinical specimen containing SCV. Antimicrobial agents such as rifampin which have intracellular activity should be included in treatment of infections caused by S aureus SCV.

Using Coxian Phase-Type Distributions to Identify Patient Characteristics for Duration of Stay in Hospital

Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their (anticipated) length of stay in hospital. The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Medical Education Podcast: The view from over there: reframing the OSCE through the experience of standardised patient raters

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