Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Comparison of koala LPCoLN and human strains of Chlamydia pneumoniae highlights extended genetic diversity in the species

Background Chlamydia pneumoniae is a widespread pathogen causing upper and lower respiratory tract infections in addition to a range of other diseases in humans and animals. Previous whole genome analyses have focused on four essentially clonal (> 99% identity) C. pneumoniae human genomes (AR39, CWL029, J138 and TW183), providing relatively little insight into strain diversity and evolution of this species. Results We performed individual gene-by-gene comparisons of the recently sequenced C. pneumoniae koala genome and four C. pneumoniae human genomes to identify species-specific genes, and more importantly, to gain an insight into the genetic diversity and evolution of the species. We selected genes dispersed throughout the chromosome, representing genes that were specific to C. pneumoniae, genes with a demonstrated role in chlamydial biology and/or pathogenicity (n = 49), genes encoding nucleotide salvage or amino acid biosynthesis proteins (n = 6), and extrachromosomal elements (9 plasmid and 2 bacteriophage genes). Conclusions We have identified strain-specific differences and targets for detection of C. pneumoniae isolates from both human and animal origin. Such characterisation is necessary for an improved understanding of disease transmission and intervention.

The Chronic Heart-Failure Assistance by Telephone (CHAT) Study: Assessment of Telephone Support for Vulnerable Patients with Chronic Disease

Aim: To determine whether telephone support using an evidence-based protocol for chronic heart failure (CHF) management will improve patient outcomes and will reduce hospital readmission rates in patients without access to hospital-based management programs. Methods: The rationale and protocol for a cluster-design randomised controlled trial (RCT) of a semi-automated telephone intervention for the management of CHF, the Chronic Heart-failure Assistance by Telephone (CHAT) Study is described. Care is coordinated by trained cardiac nurses located in Heartline, the national call center of the National Heart Foundation of Australia in partnership with patients’ general practitioners (GPs). Conclusions: The CHAT Study model represents a potentially cost-effective and accessible model for the Australian health system in caring for CHF patients in rural and remote areas. The system of care could also be readily adapted for a range of chronic diseases and health systems. Key words: chronic disease management; chronic heart failure; integrated health care systems; nursing care, rural health services; telemedicine; telenursing