Should all patients with polymyalgia rheumatica have a vascular ultrasound assessment?

There is a growing appreciation that both giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) are closely interrelated conditions that have significant overlap in aetiology, clinical characteristics and treatment regimens. Subclinical GCA in PMR is becoming increasingly recognised, and there is evolving evidence that this may be a more aggressive disease phenotype than PMR. Ultrasound (US) lends itself well as a screening tool for GCA in PMR; it is inexpensive, non-invasive, widely available, lacks ionising radiation, may be performed at the bedside and is recommended by EULAR as a first-line investigation for suspected GCA. There is insufficient evidence to currently recommend that all patients with PMR should have a US assessment for vascular involvement. However, as clinical and laboratory parameters alone do not accurately diagnose patients with subclinical GCA, we suggest that vascular US will be increasingly performed by rheumatologists in practice to identify these patients with PMR, preferably as part of larger prospective outcome studies.

Admission and readmission rate incidences from deprived areas-impact of a classical or multi-dimensional model.

INTRODUCTION: Classical deprivation instruments use a factor analytical approach relying on a smaller number of dimensions, factors or components. Multi-dimensional deprivation models attempt classification in fine detail-even down to street level. METHODS: Single-centre retrospective cohort study using routinely collected aggregated and anonymised data on emergency medical admissions (96,526 episodes in 50,731 patients; 2002-2016). We calculated admission/readmission rate incidences for the 74 small areas within the hospital catchment area. We compared a classical Small Area Health Research Unit (SAHRU) to the multi-dimensional POBAL Haase and Pratschke Deprivation Index for Small Areas (POBAL) deprivation instrument and their deprivation ranks for two Irish censuses (2006/ 2011). RESULTS: There was poor agreement between the instruments of the Deprivation Ranks by Quintile-with agreement in 46 and 42% of small areas for the respective 2006 and 2011 censuses. The classical model (SAHRU) suggested more areas with severe deprivation (Q5 66 and 55%) compared with POBAL (Q5 32 and 24%) from the respective censuses. SAHRU classical instrument had a higher prediction level incidence rate ratio (IRR) 1.48 (95% CI 1.47, 1.49)) compared with POBAL IRR 1.28 (95% CI 1.27, 1.28) and systematically lower estimates of hospital admission and readmission rate incidences. Earlier Census data modelled more powerfully, suggesting a long latency between social circumstances and the ultimate expression of the emergency medical admission. CONCLUSION: Deprivation influences hospital incidence rates for emergency medical admissions and readmissions; instruments focusing at the very small area (individual or street level) have a utility but appear inferior in terms of representing the population risk of environmental/socio-economic factors which seem best approximated at a larger scale.

Social Factors Determine the Emergency Medical Admission Workload.

We related social factors with the annual rate of emergency medical admissions using census small area statistics. All emergency medical admissions (70,543 episodes in 33,343 patients) within the catchment area of St. James's Hospital, Dublin, were examined between 2002 and 2016. Deprivation Index, Single-Parent status, Educational level and Unemployment rates were regressed against admission rates. High deprivation areas had an approximately fourfold (Incidence Rate Ratio (IRR) 4.0 (3.96, 4.12)) increase in annual admission rate incidence/1000 population from Quintile 1(Q1), from 9.2/1000 (95% Confidence Interval (CI): 9.0, 9.4) to Q5 37.3 (37.0, 37.5)). Single-Parent families comprised 40.6% of households (95% CI: 32.4, 49.7); small areas with more Single Parents had a higher admission rate-IRR (Q1 vs. for Q5) of 2.92 (95% CI: 2.83, 3.01). The admission incidence rate was higher for Single-Parent status (IRR 1.50 (95% CI: 1.46, 1.52)) where the educational completion level was limited to primary level (Incidence Rate Ratio 1.45 (95% CI: 1.43, 1.47)). Small areas with higher educational quintiles predicted lower Admission Rates (IRR 0.85 (95% CI: 0.84, 0.86)). Social factors strongly predict the annual incidence rate of emergency medical admissions.

The Relationship Between Social Deprivation and a Weekend Emergency Medical Admission.

BACKGROUND: Deprivation increases admission rates; the specific effect of deprivation with regard to weekend admissions is unknown. METHODS: We calculated annual weekend admission rates for each small area population unit and related these to quintiles of Deprivation Index from 2002-2014. Univariate and multivariable risk estimates were calculated using truncated Poisson regression. RESULTS: There were 30,794 weekend admissions in 16,665 patients. The admission rate was substantially higher for more deprived areas, 12.7 per 1000 (95%CI 9.4, 14.7) vs 4.6 per 1000 (95%CI 3.3, 5.8). More deprived patients admitted at the weekend had a significantly lower 30-day in-hospital mortality (10.3% vs 14.5%, p<0.001). CONCLUSION: Deprivation is a powerful determinant of weekend admissions, however these comprise a group of patients with better outcomes.

Social deprivation, population dependency ratio and an extended hospital episode - Insights from acute medicine.

BACKGROUND: Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; this study aimed to investigate the extent to which Deprivation status and the population Dependency Ratio influenced extended hospital episodes. METHODS: All Emergency Medical admissions (75,018 episodes of 41,728 patients) over 12 years (2002-2013) categorized by quintile of Deprivation Index and Population Dependency Rates (proportion of non-working/working) were evaluated against length of stay (LOS). Patients with an Extended LOS (ELOS), >30 days, were investigated, by Deprivation status, Illness Severity and Co-morbidity status. Univariate and multi-variable risk estimates (Odds Rates or Incidence Rate Ratios) were calculated, using truncated Poisson regression. RESULTS: Hospital episodes with ELOS had a frequency of 11.5%; their median LOS (IQR) was 55.0 (38.8, 97.6) days utilizing 57.6% of all bed days by all 75,018 emergency medical admissions. The Deprivation Index independently predicted the rate of such ELOS admissions; these increased approximately five-fold (rate/1000 population) over the Deprivation Quintiles with model adjusted predicted admission rates of for Q1 0.93 (95% CI: 0.86, 0.99), Q22.63 (95% CI: 2.55, 2.71), Q3 3.84 (95% CI: 3.77, 3.91), Q4 3.42 (95% CI: 3.37, 3.48) and Q5 4.38 (95% CI: 4.22, 4.54). Similarly the Population Dependency Ratio Quintiles (dependent to working structure of the population by small area units) independently predicted extended LOS admissions. CONCLUSION: The admission of patients with an ELOS is strongly influenced by the Deprivation status and the population Dependency Ratio of the catchment area. These factors interact, with both high deprivation and Dependency cohorts having a major influence on the numbers of emergency medical admission patients with an extended hospital episode.

Deprivation index and dependency ratio are key determinants of emergency medical admission rates.

BACKGROUND: Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; there has been debate as to the extent to which deprivation and population structure influences hospital admission rate. METHODS: All emergency medical admissions to an Irish hospital over a 12-year period (2002-2013) categorized by quintile of Deprivation Index and Dependency Ratio (proportion of population <15 or ≥ 65 years) from small area population statistics (SAPS), were evaluated against hospital admission rates. Univariate and multivariable risk estimates (Odds Ratios (OR) or Incidence Rate Ratios (IRR)) were calculated, using logistic or zero truncated Poisson regression as appropriate. RESULTS: 66,861 admissions in 36,214 patients occured during the study period. The Deprivation Index quintile independently predicted the admission rate/1000 population, Q1 9.4 (95%CI 9.2 to 9.7), Q2 16.8 (95%CI 16.6 to 17.0), Q3 33.8 (95%CI 33.5 to 34.1), Q4 29.6 (95%CI 29.3 to 29.8) and Q5 45.4 (95%CI 44.5 to 46.2). Similarly the population Dependency Ratio was an independent predictor of the admission rate with adjusted predicted rates of Q1 20.8 (95%CI 20.5 to 21.1), Q2 19.2 (95%CI 19.0 to 19.4), Q3 27.6 (95%CI 27.3 to 27.9), Q4 43.9 (95%CI 43.5 to 44.4) and Q5 34.4 (95%CI 34.1 to 34.7). A high concurrent Deprivation Index and Dependency Ratio were associated with very high admission rates. CONCLUSION: Deprivation Index and population Dependency Ratio are key determinants of the rate of emergency medical admissions.

Consultant duration of clinical practice as a cost determinant of an emergency medical admission.

BACKGROUND: Little data exists relating years of hospital consultant work experience, from time of consultant certification, and costs incurred for emergency medical patients under their care. We examined the total cost of emergency medical episodes in relation to certified consultant years experience using a database of emergency admissions. METHODS: All emergency admissions (19,295 patients) from January 2008 to December 2012 were studied. Consultants were categorized by total years of certified experience according to four experience categories (< 15, 15-20, > 20 to ≤ 25, and > 25 years). Costs per case calculations included all pay, non-pay, and diagnostic/support infra-structural costs. We used quantile regression analysis to examine the impact of predictor variables on total costs over the predictor distribution and logistic regression on outcomes and costs, adjusting for other major predictors of cost. RESULTS: Major predictors of costs were identified. Quantile regression cost parameter estimates of hospital episode costs decreased with experience; the unit change at the Q25 point of the years experience distribution was - 62 (95 % CI - 87, - 37), - 162 (95 % CI - 203, - 120) at the median, but decreased at the Q75 point to - 340 (95 % CI - 416, - 264). The odds ratio of a hospital episode cost being below the median for each category of consultant experience >15 years qualified were 0.75 (95 % CI 0.68, 0.83), 0.77 (95 % CI 0.70, 0.86), and 0.70 (95 % CI 0.64, 0.78): p < 0.001 for each experience category vs. <15 years qualified. CONCLUSIONS: There appear to be cost advantages to care delivered by certified consultants of >20 years in clinical practice.

Factors predicting the hospital episode costs of emergency medical admissions.

BACKGROUND: Important outcome predictor variables for emergency medical admissions are the Manchester Triage Category, Acute Illness Severity, Chronic Disabling Disease and Sepsis Status. We have examined whether these are also predictors of hospital episode costs. METHODS: All patients admitted as medical emergencies between January 2008 and December 2012 were studied. Costs per case were adjusted by reference to the relative cost weight of each diagnosis related group (DRG) but included all pay costs, non-pay costs and infra-structural costs. We used a multi-variate logistic regression with generalized estimating equations (GEE), adjusted for correlated observations, to model the prediction of outcome (30-day in-hospital mortality) and hospital costs above or below the median. We used quantile regression to model total episode cost prediction over the predictor distribution (quantiles 0.25, 0.5 and 0.75). RESULTS: The multivariate model, using the above predictor variables, was highly predictive of an in-hospital death-AUROC of 0.91 (95% CI: 0.90, 0.92). Variables predicting outcome similarly predicted hospital episode cost; however predicting costs above or below the median yielded a lower AUROC of 0.73 (95% CI: 0.73, 0.74). Quantile regression analysis showed that hospital episode costs increased disproportionately over the predictor distribution; ordinary regression estimates of hospital episode costs over estimated the costs for low risk and under estimated those for high-risk patients. CONCLUSION: Predictors of outcome also predict costs for emergency medical admissions; however, due to costing data heteroskedasticity and the non-linear relationship between dependant and predictor variables, the hospital episode costs are not as easy to predict based on presentation status.

Hyponatraemia in Emergency Medical Admissions-Outcomes and Costs.

Healthcare systems in the developed world are struggling with the demand of emergency room presentations; the study of the factors driving such demand is of fundamental importance. From a database of all emergency medical admissions (66,933 episodes in 36,271 patients) to St James' Hospital, Dublin, Ireland, over 12 years (2002 to 2013) we have explored the impact of hyponatraemia on outcomes (30 days in-hospital mortality, length of stay (LOS) and costs). Identified variables, including Acute Illness Severity, Charlson Co-Morbidity and Chronic Disabling Disease that proved predictive univariately were entered into a multivariable logistic regression model to predict the bivariate of 30 days in-hospital survival. A zero truncated Poisson regression model assessed LOS and episode costs and the incidence rate ratios were calculated. Hyponatraemia was present in 22.7% of episodes and 20.3% of patients. The 30 days in-hospital mortality rate for hyponatraemic patients was higher (15.9% vs. 6.9% p < 0.001) and the LOS longer (6.3 (95% CI 2.9, 12.2) vs. 4.0 (95% CI 1.5, 8.2) p < 0.001). Both parameters worsened with the severity of the initial sodium level. Hospital costs increased non-linearly with the severity of initial hyponatraemia. Hyponatraemia remained an independent predictor of 30 days in-hospital mortality, length of stay and costs in the multi-variable model.

Increased body mass index in ankylosing spondylitis is associated with greater burden of symptoms and poor perceptions of the benefits of exercise.

OBJECTIVE: Increased body mass index (BMI) in patients with ankylosing spondylitis (AS) is associated with a greater burden of symptoms and poor perceptions of the benefits of exercise. In AS, the effect of obesity on disease characteristics and exercise perceptions is unknown. We evaluated the prevalence of obesity in AS, to assess the attitudes of patients toward exercise and to evaluate the effect of obesity on symptoms and disease activity. METHODS: Demographic data and disease characteristics were collected from 46 patients with AS. Disease activity, symptomatology, and functional disability were examined using standard AS questionnaires. BMI was calculated. Comorbidity was analyzed using the Charlson Comorbidity Index. Patients' attitudes toward exercise were assessed using the Exercise Benefits and Barriers Scale (EBBS). We compared the disease characteristics, perceptions regarding exercise, and functional limitations in those who were overweight to those who had a normal BMI. RESULTS: The mean BMI in the group was 27.4; 67.5% of subjects were overweight or obese. There was a statistically significant difference between those who were overweight and those with a normal BMI regarding their perceptions of exercise (EBBS 124.7 vs 136.6, respectively), functional limitation (Bath AS Functional Index 4.7 vs 2.5, Health Assessment Questionnaire 0.88 vs 0.26), and disease activity (Bath AS Disease Activity Index 4.8 vs 2.9). There was no difference between the groups in terms of their comorbid conditions or other demographic variables. CONCLUSION: The majority of patients in this AS cohort were overweight. They had a greater burden of symptoms, worse perceptions regarding the benefits of exercise, and enhanced awareness of their barriers to exercising. This is of particular concern in a disease where exercise plays a crucial role.