Reining in equine metabolic syndrome : a gluttony of challenges

We humans are complicated creatures. Despite remarkable intellect, a fearsome ability to push boundaries and superior survival mechanisms, we are at times our own worst enemy. Metabolic syndrome continues to be a premier health problem in developed, and now increasingly in undeveloped, nations. It is spreading across the planet like an infectious disease and is costing us millions. Metabolic disease remains an important focus both for medical research and for governments desperate to ease the burden on already over-taxed health systems. Unlike some previous worldwide health epidemics, obesity-related diseases will require more than a single, silver bullet. A simple vaccine or treatment cannot overcome a lack of education, awareness and in some cases sheer determination; the human element of these diseases. Undeniably, these ‘human elements’ also complicate our ability, as veterinarians, to effectively manage the growing incidence of equine obesity and metabolic disease...

Risk modelling in quality clinical registries : Monitoring lesion treatment failure rate in percutaneous coronary interventions

Aims: This paper describes the development of a risk adjustment (RA) model predictive of individual lesion treatment failure in percutaneous coronary interventions (PCI) for use in a quality monitoring and improvement program. Methods and results: Prospectively collected data for 3972 consecutive revascularisation procedures (5601 lesions) performed between January 2003 and September 2011 were studied. Data on procedures to September 2009 (n = 3100) were used to identify factors predictive of lesion treatment failure. Factors identified included lesion risk class (p < 0.001), occlusion type (p < 0.001), patient age (p = 0.001), vessel system (p < 0.04), vessel diameter (p < 0.001), unstable angina (p = 0.003) and presence of major cardiac risk factors (p = 0.01). A Bayesian RA model was built using these factors with predictive performance of the model tested on the remaining procedures (area under the receiver operating curve: 0.765, Hosmer–Lemeshow p value: 0.11). Cumulative sum, exponentially weighted moving average and funnel plots were constructed using the RA model and subjectively evaluated. Conclusion: A RA model was developed and applied to SPC monitoring for lesion failure in a PCI database. If linked to appropriate quality improvement governance response protocols, SPC using this RA tool might improve quality control and risk management by identifying variation in performance based on a comparison of observed and expected outcomes.