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INTRODUCTION: Polymyalgia rheumatica (PMR) is a disease of the elderly, associated with increased fracture risk due to glucocorticosteroid (GC) treatment with the additional possible influence of chronic inflammation. Risk factors for fracture in PMR have not been extensively studied. Hip structure analysis (HSA) is a way to measure bone morphology in the hip using dual X-ray absorptiometry (DEXA). It has been used as a predictor of fracture in epidemiological settings. HSA has not been studied in PMR before. OBJECTIVES: The object of this retrospective study was to determine if fracture risk in PMR was associated with densitometry data and to determine the influence, if any, of HSA on that association. METHODS: 714 patients with PMR referred for a bone density estimate at a district general hospital from June 2004 to October 2010 were studied. Demographic data, GC use, alcohol consumption, smoking status, secondary osteoporosis, and fracture history were recorded. Bone mineral density (BMD), Z score, T score, body composition data, and HSA measurements were collected. These were geometric measurements taken from 2-dimensional DEXA images of the hip. Fracture was modelled as an outcome variable using logistic regression models, adjusted for age and sex. And the fit of the model was assessed by comparing the area under the curve (AUC). RESULTS: 714 patients were studied, 532 (75%) were female, and mean age was 70.5 with SD of 8.8. 703 (98%) had been treated with GCs. Lumbar and femoral BMD models were significantly associated with fracture. Right femur OR 0.062 (0.014-0.285), left femur OR 0.098 (0.023-0.412), right femoral neck 0.078 (0.014-0.43), left femoral neck 0.104 (0.022-0.492), L1 0.192 (0.066-0.56), L2 OR 0.138 (0.053-0.358), L3 0.192 (0.079-0.463), and L4 0.243 (0.108-0.544). Cross-sectional area was the only HSA parameter that was associated with fracture OR 0.988 (0.980-0.997). CONCLUSION: L2 association models were strongest. Prospective studies are needed to elucidate whether these factors predict future fracture. GC data were binary, not reflecting dose and duration.
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Background: Antimicrobial overuse causes increased antimicrobial resistance in ICUs; antimicrobial stewardship programmes (ASPs) aim to optimize usage. Following an MDR Acinetobacter baumannii (MRAb) outbreak in 2008, an ASP was implemented at a London ICU, and then continued as a long-term programme. This study aimed to determine long-term changes in antimicrobial prescribing 9â years on. Methods: Data were collected from ICU patients in 2008 immediately before ASP implementation, and thereafter for 6â month cohort periods in 2010-2011, 2012 and 2017. Antimicrobial usage in DDD per 1000 occupied bed days (OBD) were compared. Multivariate linear regression models for antimicrobial days were fitted, adjusting for APACHE II score and patient days. Antimicrobial resistance in Pseudomonas aeruginosa (as an indicator organism) was compared across cohort periods. Findings: Across 400 patients over 9â years, antimicrobial use changed significantly (Pâ<â0.011) and remained lower in all post-ASP cohorts compared with pre-ASP [(2008; 1827â DDD/1000â OBD), (2010; 1264â DDD/1000â OBD), (2012; 1270â DDD/1000â OBD) and (2017; 1566â DDD/1000â OBD)]. There was reduction in usage of all antimicrobial classes except ß-lactams (where there was no significant increase nor decrease, Pâ=â0.178) and aminoglycosides (where there was a significant increase in usage, Pâ<â0.0001). The latter was temporally associated with restrictions on specific carbapenems. There was an increase in carbapenem-resistant P. aeruginosa in 2012 only (Pâ=â0.028) but not subsequently. Conclusions: Following ASP implementation after an outbreak of MRAb, reduced antimicrobial prescribing was maintained 9â years on. We identify several factors influencing successful long-term maintenance of ASPs in ICUs.