Management of Acute and Recurrent Gout: A Clinical Practice Guideline From the American College of Physicians

Description: The American College of Physicians (ACP) developed this guideline to present the evidence and provide clinical recommendations on the management of gout. Methods: Using the ACP grading system, the committee based these recommendations on a systematic review of randomized, controlled trials; systematic reviews; and large observational studies published between January 2010 and March 2016. Clinical outcomes evaluated included pain, joint swelling and tenderness, activities of daily living, patient global assessment, recurrence, intermediate outcomes of serum urate levels, and harms. Target Audience and Patient Population: The target audience for this guideline includes all clinicians, and the target patient population includes adults with acute or recurrent gout. Recommendation 1: ACP recommends that clinicians choose corticosteroids, nonsteroidal anti-inflammatory drugs (NSAIDs), or colchicine to treat patients with acute gout. (Grade: strong recommendation, high-quality evidence). Recommendation 2: ACP recommends that clinicians use low-dose colchicine when using colchicine to treat acute gout. (Grade: strong recommendation, moderate-quality evidence). Recommendation 3: ACP recommends against initiating long-term urate-lowering therapy in most patients after a first gout attack or in patients with infrequent attacks. (Grade: strong recommendation, moderate-quality evidence). Recommendation 4: ACP recommends that clinicians discuss benefits, harms, costs, and individual preferences with patients before initiating urate-lowering therapy, including concomitant prophylaxis, in patients with recurrent gout attacks. (Grade: strong recommendation, moderate-quality evidence)

Advice about diet and smoking for people with or at risk of age-related macular degeneration: a cross-sectional survey of eye care professionals in the UK.

BACKGROUND: In the absence of a cure, there has been considerable interest in attempts to prevent or reduce the progression of age-related macular degeneration (AMD) by targeting particular modifiable risk factors. The aim of this study was to conduct a cross-sectional survey of the current practice of UK eye care professionals in relation to advice given on diet and other lifestyle modifications for patients with or at risk of AMD. METHODS: Optometrists and ophthalmologists on the membership databases of professional organisations for the two professions were invited to participate in an online survey. The survey was open for 12 weeks between July and September 2012. RESULTS: A total of 1,468 responses were received (96.3% from optometrists and 3.7% from ophthalmologists). The response rate of those receiving the invitation was 16.2% (1,414/8735) for optometrists and 6% (54/1460) for ophthalmologists. A majority of respondents reported that they frequently provide dietary advice to patients with established AMD (67.9%) and those at risk of AMD (53.6%). Typical advice consisted of a recommendation to eat plenty of leafy green vegetables and eat more oily fish. The decision to recommend nutritional supplements was based on the risk of progression to advanced AMD, with approximately 93% of respondents recommending supplementation in a patient with advanced AMD in one eye. However for the majority, the type of supplement recommended did not comply with current best research evidence, based on the findings of the Age-related Eye Disease Study (AREDS). Only one in three optometrists regularly assessed smoking status and advised on smoking cessation. CONCLUSIONS: Within a large sample of eye care professionals, consisting predominantly of optometrists, who responded to a cross-sectional survey, there was active engagement in providing nutritional advice to patients with or at risk of AMD. However, the results demonstrate a need to raise awareness of the evidence underpinning the use of nutritional supplements together with an increased involvement in targeted smoking cessation

A framework for feature extraction from hospital medical data with applications in risk prediction

Background: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Results: Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). Conclusions: The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks

Outcomes of polytrauma patients with diabetes mellitus.

BACKGROUND: The impact of diabetes mellitus in patients with multiple system injuries remains obscure. This study was designed to increase knowledge of outcomes of polytrauma in patients who have diabetes mellitus. METHODS: Data from the Trauma Audit and Research Network was used to identify patients who had suffered polytrauma during 2003 to 2011. These patients were filtered to those with known outcomes, then separated into those with diabetes, those known to have other co-morbidities but not diabetes and those known not to have any co-morbidities or diabetes. The data were analyzed to establish if patients with diabetes had differing outcomes associated with their diabetes versus the other groups. RESULTS: In total, 222 patients had diabetes, 2,558 had no past medical co-morbidities (PMC), 2,709 had PMC but no diabetes. The diabetic group of patients was found to be older than the other groups (P <0.05). A higher mortality rate was found in the diabetic group compared to the non-PMC group (32.4% versus 12.9%), P <0.05). Rates of many complications including renal failure, myocardial infarction, acute respiratory distress syndrome, pulmonary embolism and deep vein thrombosis were all found to be higher in the diabetic group. CONCLUSIONS: Close monitoring of diabetic patients may result in improved outcomes. Tighter glycemic control and earlier intervention for complications may reduce mortality and morbidity

Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation

Aims/hypothesis: Hospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia. Methods: We used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score (‘base’ model). In the second model, we added to the ‘base’ model the 20 most common medical conditions and applied a stepwise backward selection of variables (‘disease’ model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics. Results: In derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models. Conclusions/interpretation: This simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia