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NT-proB-type natriuretic peptide (NT-proBNP) is a frequently utilized test in congestive cardiac failure. There is little data on its utility in unselected emergency medical admissions.This study aims to investigate the clinical utility and prognostic value of NT-proBNP in emergency medical admissions and to determine whether such testing influenced downstream investigations and length of stay (LOS).We report on NT-proBNP tests performed in emergency medical admissions in a 2005/2006 and subsequent 7-year (2014-2020) retrospective cohort. We assessed 30-day in-hospital mortality with a multivariable logistic regression model. The utilization of procedures/services was related to LOS with zero-truncated Poisson regression.There were 64,212 admissions in 36,252 patients. Patients with a NT-proBNP test were significantly older at 75.3 years vs. 63.0 years and had longer LOS -9.4 days vs. 4.9 days. They had higher acute illness severity and comorbidity scores. Thirty-day in-hospital mortality was higher in those with a NT-proBNP test (8.8%) vs. no request (3.2%). NT-proBNP test level was prognostic in univariate - OR 2.87 (2.61, 3.15), and multivariate analyses - OR 1.40 (1.26, 1.56). Higher NT-proBNP levels predicted higher 30-day in-hospital mortality. Multivariable thirty-day in-hospital mortality was 3.8% (3.6%, 3.9%) for those without a test, increasing to 4.9% (4.7%, 5.2%) for ≥ 250 ng/L and 5.8% (5.8%, 6.3%) for ≥ 3000 ng/L. LOS was linearly related to the total number of procedures/services performed.NT-proBNP is prognostic in emergency medical admissions. Downstream resource utilization differed following an NT-proBNP test; this may reflect different case complexity or the 'uncertainty' surrounding such admissions.

Background The COVID-19 pandemic has put considerable strain on healthcare systems. Aim To investigate the effect of the COVID-19 pandemic on 30-day in-hospital mortality, length of stay (LOS) and resource utilization in acute medical care. Methods We compared emergency medical admissions to a single secondary care centre during 2020 to the preceding 18 years (2002–2019). We investigated 30-day in-hospital mortality with a multiple variable logistic regression model. Utilization of procedures/services was related to LOS with zero truncated Poisson regression. Results There were 132,715 admissions in 67,185 patients over the 19-year study. There was a linear reduction in 30-day in-hospital mortality over time; over the most recent 5 years (2016–2020), there was a relative risk reduction of 36%, from 7.9 to 4.3% with a number needed to treat of 27.7. Emergency medical admissions increased 18.8% to 10,452 in 2020 with COVID-19 admissions representing 3.5%. 18.6% of COVID-19 cases required ICU admission with a median stay of 10.1 days (IQR 3.8, 16.0). COVID-19 was a significant univariate predictor of 30-day in-hospital mortality, 18.5% (95%CI: 13.9, 23.1) vs. 3.0% (95%CI: 2.7, 3.4)—OR 7.3 (95%CI: 5.3, 10.1). ICU admission was the dominant outcome predictor—OR 12.4 (95%CI: 7.7, 20.1). COVID-19 mortality in the last third of 2020 improved—OR 0.64 (95%CI: 0.47, 0.86). Hospital LOS and resource utilization were increased. Conclusion A diagnosis of COVID-19 was associated with significantly increased mortality and LOS but represented only 3.5% of admissions and did not attenuate the established temporal decline in overall in-hospital mortality.

Aim To investigate whether a specific (SP) or non-specific (NSP) clinical presentation, predicts prognosis and in-hospital resource utilization in emergency medical admissions. Methods We studied admissions over 5 years (2015–2019) and classified the symptom presentation as SP or NSP. The predictive capacity of the NSP category was related to 30-day in-hospital mortality with a multivariable logistic regression model. Utilization of procedures/services was related to hospital length of stay (LOS) with zero truncated Poisson regression. Results There were 39,776 admissions in 23,995 patients. A NSP occurred in 18.2% of our top 20 clinical presentations; the top five being shortness of breath (12.8%), ‘unwell’ (7.1%), collapse (4.1%), abdominal pain (3.6%) and headache (2.7%). Baseline demographic characteristics were similar and unrelated to type of presentation; the model adjusted mortality by SP 4.0% (95% CI: 3.8%, 4.2%) or NSP 3.9% (95% CI: 3.5%, 4.4%) was identical. LOS was a dependant quantitative function of procedures/services undertaken; for the top two presentations of shortness of breath (SP) or unwell (NSP) there was no relationship between a SP or NSP presentation and hospital utilization of procedures/services or LOS. Conclusion Our data suggest no utility for a categorisation of presentations as specific or non-specific in terms of provision of prognostic information nor as an indicator of the pattern of hospital investigation or LOS.

Aim : To investigate whether excessive high-sensitivity cardiac troponin T (hscTnT) testing, in non-cardiac presentations, increases hospital length of stay (LOS) by driving down-stream investigations. Methods : We report on all hscTnT tests in emergency medical admissions, performed over a 9-year period between 2011-2019. Troponin testing frequency in different risk cohorts was determined and related to 30-day in-hospital mortality with a multivariable logistic regression model adjusted for other outcome predictors. Downstream utilization of procedures/services was related to LOS with zero truncated Poisson regression. Results : There were 66,475 admissions in 36,518 patients. hscTnT was tested in 24.4% of admissions, more frequently in the elderly (>70 years 33.4%, >80 years 35.9%), cardiovascular presentations (33.6%) and in those with high comorbidity (42.2%), and reduced in those with neurologic presentations (20%). A hscTnT request predicted increased 30-day in-hospital mortality OR 3.33 (95% CI: 3.06, 3.64). The univariate odds ratio (OR) of hscTnT test result was 1.45 (95% CI: 1.42, 1.49) and was semi-quantative with worsening outcomes as hscTnT increased. It remained predictive in the fully adjusted model OR 1.17 (95% CI: 1.09, 1.26). LOS was linearly related to the number of procedures/services performed. hscTnT testing did not increase LOS or number of procedures/services Conclusion : A clinical request for hscTnT testing is prognostic and risk categorises. Subsequent resource utilization, if increased, appears an epiphenomenon related to risk categorisation, rather than being driven by inappropriate hscTnT testing.

A bstract Background We examine the ability of pre-existing measures of Forced Expiratory Volume in 1 s (FEV1), and Diffusion Capacity for Carbon Monoxide (DLCO) to determine the subsequent 30-day mortality outcome following unselected acute medical admission. Methods Between 2002 and 2017, we studied all emergency medical admissions (106,586 episodes in 54,928 patients) of whom 8071 were classified as respiratory. We employed logisitic multiple variable regression models to evaluate the ability of FEV1 or DLCO to predict the 30-day hospital mortality outcome. Results The 30-day hospital episode mortality outcome demonstrated curvilinear relationships to the underlying FEV1 or DLCO values; adjusted for major outcome predictors, a higher FEV1 – OR 0.85 (95% CI: 0.82, 0.89) or DLCO OR 0.76 (95% CI: 0.73, 0.79) values predicted survival. The range of predicted mortalities was from 3.3% (95% CI: 2.5, 4.0) to 23.5% (95% CI: 20.8, 26.2); the FEV1 (Model1) and DLCO (Model2) outcome prediction was essentially equivalent (Chi2 = 2.9: p = 0.08). Conclusion The 30-day mortality outcome was clearly related to the pre-admission FEV1 and DLCO value. The outcome relationship was curvilinear. Either parameter appears a useful tool to explore hospital outcomes. Previously suggested cut-points are likely an artefact and not supported by these data.

Background: Positive blood cultures predict mortality. The prognostic value of blood culture performance itself has not been fully defined. Methods: We evaluated medical admissions from 2002-2017. We defined blood culture category as 1) no culture 2) negative culture 3) positive culture. We employed a multivariable logistic regression model to evaluate outcomes. Results: We evaluated 78,568 blood cultures in 106,586 admissions. 30-day in-hospital mortality for no culture was 2.8% (95%CI 2.7, 2.9), culture negative 8.9% (95%CI 8.5, 9.3) and culture positive 16.7% (95%CI 15.5, 17.9). There was significant interaction between blood culture category and illness severity, OR 1.06 (95%CI 1.05, 1.08), and comorbidity, OR 1.09 (95%CI 1.09, 1.10). Conclusion: Performance and results of blood cultures are independently associated with increased mortality.

Background: The prediction of clinical outcomes using biochemical markers is an important tool. Methods: We calculated a risk score for all emergency admissions 2002-2017. We related potassium and mortality in a multivariable fractional polynomial model. We investigated the potassium distribution and relationship of potassium to mortality over time. Results: There were 106,586 admissions in 54,928 patients. Mortality was higher for those with an admission potassium above the median – 6.1% vs 4.6% (p

Objective To investigate the extent to which air pollution interacts with comorbidity in determining mortality outcomes of emergency medical admissions. Study design Routinely collected data were used to study all emergency medical admissions to an academic teaching hospital in Dublin, Ireland, from 2002 to 2018. Air pollution was measured by particulate matter with aerodynamic diameter ≤10 μm (PM10) and sulphur dioxide (SO2) levels on the day of admission. Comorbidity Score was measured using a previously derived score. Methods A multivariable logistic regression model was used to relate air pollutant levels, Comorbidity Scores, and their interaction to 30-day in-hospital mortality. Results There were 102,483 admissions in 58,127 patients over 17 years. Both air pollutant levels and Comorbidity Score were associated with 30-day in-hospital mortality. On admission days with PM10 levels above the median, mortality was higher (Odds ratio [OR] 1.09; 95% confidence interval [CI] 1.06, 1.18) at 11.2% (95% CI 10.5, 12) compared with 10.4% (95% CI 10, 10.7) on days when PM10 levels were below the median. On admission days with SO2 levels above the median, mortality was higher (OR 1.13; 95% CI 1.10, 1.16) at 12.2% (95% CI 11.4, 13) compared with 10.7% (95% CI 10.3, 11.1) on days when SO2 levels were below the median. Comorbidity Score was strongly associated with mortality (mortality rate of 8.9% for those with a 6-point score vs mortality rate of 30.3% for those with a 16-point score). There was limited interaction between air pollutant levels and Comorbidity Score. Conclusion Both air pollution levels on the day of admission and Comorbidity Score were associated with 30-day in-hospital mortality. However, there was limited interaction between these two factors.

Background: There is concern that undue ED wait times may result in adverse outcomes. Methods: We studied 30-day in-hospital mortality (2002-2017) for all medical admissions (106,586 episodes; 54,928 patients) focusing on clinical risk profile Results: Comparing 2002-09 vs. 2010-17, median ED waits > 6 hours (hr) increased 10h (95% CI: 8,13) to 15h (95% CI: 9,19). 30-day mortality declined 6.2% to 4.9%- (RRR- 20.8%/ NNT- 78). 30-day-mortality by ED wait: – < 4hr 6.6% (95% CI: 6.3%, 6.9%), 4-8hr 4.8% (95% CI: 4.6%, 5.0%), 8-12hr 4.3% (95% CI: 4.1%, 4.5%) or >=12hr 4.2% (95% CI: 3.9%, 4.5%). Conclusion: Admissions with shorter waits are overrepresented with high clinical acuity. Higher Risk Score patient with extended wait times had worse clinical outcomes.

Background: The impact of environmental air pollutants on the outcome of an emergency hospitalisation of respiratory patients has received limited study. We report on how levels of pollutants, particulate matter levels (PM10) and oxides of Nitrogen (NOx) infl uence hospital outcomes (30-day inhospital mortality).

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. 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). 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. 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.

Background Deprivation has been shown to adversely affect health outcomes. However, whether deprivation increases hospitalisation costs is uncertain. We have examined the relationship between deprivation and the costs of emergency medical admissions in a single centre between 2008–2014. Methods We calculated the total hospital costs of emergency admissions related to their deprivation status, based on area of residence (Electoral Division - small census area). We used truncated Poisson and quantile regression methods to examine relationships between predictor variables and total hospital episode costs. Results Over the study period, 29,508 episodes were recorded in 15,932 patients. Compared with the least deprived (Q1), the incidence rate ratios (IRR) for annual costs were increased to Q3 1.15 (95% CI: 1.12, 1.19), Q4 2.39 (95% CI: 2.30, 2.49) and Q5 2.76 (95% CI: 2.68, 2.85). The margin statistic cost estimate per thousand population increased from 183.8 K€ in Q1 to 507.9 K€ in Q5. The total bed days/1000 population increased as follows (compared with Q1): Q3 IRR 1.41 (95% CI: 1.37, 1.45), Q4 1.96 (95% CI: 1.89, 2.03) and Q5 3.04 (95% CI: 2.96, 3.12). The margin statistic bed day estimate (/1000 population) increased from 218.7 in Q1 to 664.0 in Q5. Conclusion Deprivation status had a profound impact on total hospital costs for emergency medical admissions. This was primarily mediated through a tripling of total bed days in the most deprived groups.

Hospital readmissions are a perennial problem. We reviewed readmissions to one institution (2002-2015) and investigated their dynamics.96,474 emergency admissions (in 50,701 patients) to an Irish hospital over a 15-year period were studied, and patterns surrounding early (28days) and late (any other) readmissions determined. Univariate and logistic or truncated Poisson regression methods were employed.Early readmission rate averaged 9.6% (95% CI: 9.4, 9.8) with a low/high of 8.4% (95% CI: 7.8, 9.1) and 10.3% (95% CI: 9.6, 11.0) respectively with no overall time trend. Early readmissions represented 20.1% (95% CI: 19.8, 20.5) of emergency medical readmissions. Median time to first readmission was 55weeks (95% CI: 13, 159), time to second was 35weeks (95% CI: 9, 98); by the 7th/8th readmissions, intervals were 13weeks (95% CI: 4, 36) and 11weeks (95% CI: 4, 30). Readmissions were older 67.1years (95% CI: 48.3, 79.2) vs. single admissions 53.9years (34.3, 72.4) and stayed longer - 5.8days (2.7, 10.6) vs. 3.9days (1.5, 8.0). Readmissions had more Acute Illness Severity, Charlson Co-Morbidity and Chronic Disabling Disease. Between 2002 and 2015 the logistic adjusted model of 30-day in-hospital mortality reduced from 6.1% (95% CI: 5.7, 6.5) to 4.4% (95% CI: 4.1, 4.7) (RRR 30.4%).Early hospital readmission rate did not change over 15years despite improvements in hospital mortality outcomes. Readmissions have a consistent pattern related to patient illness and social characteristics; the fundamentals are driven by disease progression over time.

Background The aim of this study was to detail the time profile and frequency distribution of mortality following an emergency admission and to compare these for weekday and weekend admissions. Methods We profiled in-hospital deaths following emergency medical admission between 2002 and 2014. We determined the frequency distribution, time pattern, causality and influence of day of admission on mortality out to 120 days. We utilized a multivariable regression model (logistic for in-hospital mortality and truncated Poisson for count data) to adjust for major predictor variables. Results There were 82,368 admissions in 44,628 patients with 4587 in-hospital deaths. The 30-day in-hospital mortality declined from 8.2% in 2002 to 3.7% in 2014. The mortality pattern showed an exponential decay over time; the time to death was best described by the three-parameter Weibull model. The calculated time to death for the 5th, 10th, 25th, 50th, 75th, and 90th centiles were 0.5, 1.2, 3.8, 11.1, 26.3 and 49.3 days. Acute Illness Severity Score, Chronic Disabling Disease Score, Charlson Co-Morbidity Index and Sepsis status were associated with mortality. The risk of death was initially high, lower by day 3, and showed a cumulative increase over time. The mortality pattern was very similar between a weekday or weekend admission; however, the risk of death was greater at all time points between 0 and 120 days for patients admitted at a weekend OR 1.08 (95% CI 1.01–1.15). Conclusion We have demonstrated the pattern of mortality following an emergency admission. The underlying pattern is similar between weekday and weekend admissions.

Introduction Saddle pulmonary embolism is a life-threatening, clinically challenging diagnosis. Syncope is an uncommon presentation, which may be misdiagnosed. We report the case of a 17-year-old Caucasian girl with saddle embolism induced syncope. Case presentation A 17-year-old girl, with no history of disease, was admitted to the emergency department following a 1 day history of two syncopal episodes on a background of left groin pain. She had been taking the COCP for 6 months for the treatment of acne. On admission, she became haemodynamically unstable with a BP 70/40, sinus tachy 150bpm and 83% oxygen saturations on room air. ECG demonstrated sinus tachy, RBBB and V1Q3. Doppler was positive for a left iliofemoral DVT. Urgent CTPA confirmed a massive saddle embolus, multiple right lower zone PE and increased right heart pressure. She was immediately transferred to IR where she had catheter-directed mechanical thrombolysis with 10 mg TPA bolus, 500 IU of heparin followed by an infusion of TPA 0.5 mg/hour. Follow-up pulmonary angiogram demonstrated marked interval improvement with no evidence of residual thrombus. An ECHO was performed showing normal LV systolic function and a negative bubble study. Her lupus screen was negative. She was treated with Heparin according to the SJH protocol and discharged on Apixaban with haematology follow-up. Discussion The annual incidence of paediatric PE is 0.5/10,000. Diagnosis may be delayed in young individuals, with some reporting a median of 7 days from presentation to diagnosis. Prompt diagnosis is vital to broaden management options and decrease mortality. This is the case of a young woman presenting with a clear indication for thrombolysis. Correct recognition and appropriate use of catheter directed thrombolysis was life-saving. Research indicates that it may be considered in patients with persistent haemodynamic instability, those at risk of death before systemic therapy can be effective and those with high bleeding risks. Conclusion Pulmonary embolism is an important differential in patients presenting with syncope, with many of these having a large or saddle embolus. Early identification is vital to avoid haemodynamic compromise and to optimise survival.

Background: Areas of low socio-economic status (SES) have a disproportionate number of emergency medical admissions; we quantitate the profile of multi-morbidity related to SES. Methods: We developed a logistic multiple variable regression model, based on over 15 years of hospital data, to examine the effect of socio-demography on hospital outcomes. Results: Admissions from low SES cohort were a decade younger, and had a shorter hospital stay, and lower 30-day episode mortality outcome. The number of morbidities was equivalent between groups, but the more disadvantaged were more likely to have a respiratory diagnosis or diabetes. Conclusion: Low SES emergency admissions present > 10 yr. earlier than the high SES population; their equivalent multimorbidity, despite a lower age, could reflect accelerated disease progression.

The Acute Medical Admission Unit (AMAU) model of care has been widely deployed, we examine changes in hospital readmission rates, length of stay (LOS) and 30-day in-hospital mortality over 16 years.All emergency medical admissions between 2002 and 2017 were examined. We assessed 30-day in-hospital mortality, readmission rates, and LOS using logistic regression and margins statistics modelled outcomes against predictor variables.There were 106,586 admissions in 54,928 patients over 16 years. Calculated per patient the 30-day in-hospital mortality was 8.9% (95%CI 8.6% to 9.2%) and showed a relative risk reduction (RRR) of 61.1% from 12.4% to 4.8% over the 16 years (p = .001). Calculated per admission the 30-day in-hospital mortality was 4.5% (95%CI 4.4% to 4.6%) with a RRR of 31.9% from 2002 to 2017. Over this extended period 48.7% of patients were readmitted at least once, 9.3%5 times and 20 patients50 times each. The median LOS was 5.9 days (IQR 2.4, 12.9) with no trend of change over time. Total readmissions increased as a time dependent function; early readmissions (4 weeks) fluctuated without time trend at 10.5% (95%CI 9.6 to 11.3). A logistic regression model described the hospital LOS as a linear function both of comorbidity and the utilisation of inpatient procedures and services.30-day in-hospital mortality showed a linear trend to reduce over time at unaltered LOS and readmission rates. LOS showed linear dependency on clinical complexity; interventions aimed at reducing LOS may not be appropriate beyond a certain point.

Blood cultures are performed in the emergency room when sepsis is suspected, and a cohort of patients is thereby identified. The present study investigated the outcomes (mortality and length of hospital stay) in this group following an emergency medical admission.Prospective assessment of all emergency medical admissions presenting to the emergency department at St James's Hospital, Dublin, over an 11-year period (2002-2012) was carried out. Outcomes including 30-day in-hospital mortality and length of stay were explored in the context of an admission blood culture. Generalized estimating equations, logistic or zero-truncated Poisson multivariate models were used, with adjustment for confounding variables including illness severity, comorbidity, and chronic disabling disease, to assess the effect of an urgent blood culture on mortality and length of stay.A total of 60 864 episodes were recorded in 35 168 patients admitted over the time period assessed. Patients more likely to undergo blood cultures in the emergency department were male, younger, and had more comorbidity. Univariate and multivariate analyses showed that those who had a blood culture, irrespective of result, had increased mortality and a longer in-hospital stay. This was highest for those with a positive culture, irrespective of the organism isolated.A clinical decision to request a blood culture identified a subset of emergency admissions with markedly worse outcomes. This patient cohort warrants close monitoring in the emergency setting.

Background Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission. How deprivation relates to the admission or readmission incidence rates, episode length of stay (LOS) and ancillary resource utilization is less clear. Methods All emergency medical admissions (66,861 episodes in 36,214 patients) between 2002 and 2013, categorized by quintile of Irish National Deprivation Index were assessed against admission or readmission incidence rates (/1000 local population by electoral division), LOS and utilization of five ancillary services. Univariate and multi-variable risk estimates (odds ratios (OR) or incidence rate ratios (IRR)) were calculated, using truncated Poisson regression. Results The deprivation index quintile was strongly correlated with the emergency medical admission rate with IRR (as compared with quintile 1) as follows: Q2 1.99 (95% CI: 1.96, 2.01), Q3 3.45 (95% CI: 3.41, 3.49), Q4 3.27 (95% CI: 3.23, 3.31) and Q5 4.29 (95% CI: 4.23, 4.35). LOS was not influenced by deprivation status; although increasing deprivation resulted in increased utilization of social services (OR 1.04: 95% CI: 1.03, 1.06), with a lower requirement for occupational therapy (OR 0.94: 95% CI: 0.93, 0.96) and speech/language services (OR 0.83: 95% CI: 0.80, 0.86). There was a rather decreased use of ancillary services with increasing deprivation; however, the readmission rate was strongly predicted by deprivation status. Conclusion Deprivation status strongly influenced the admission and readmission rates for medical patients admitted as emergencies; however, ancillary resource utilization was not increased. Deprivation index will increase demand on hospital resources due to the aggregate effect on both admission and readmission incidence rates.

Background: Air quality is known to aggravate cardiopulmonary disease. The aim of this work was to examine the extent to which air pollution, underlying illness, and age influenced 30-day inhospital mortality outcomes. Methods: All emergency medical admissions, between 2002 and 2018, to St. James's Hospital, Dublin, Ireland (113,807 episodes in 58,126 patients) and particulate matter (PM10) level on the day of admission were studied; we determined 30-day mortality outcomes for older (≥70 years) persons and whether outcomes were conditionally dependent on the underlying illness severity or comorbidity score. We employed a logistic multiple variable regression model to calculate PM10influence on the outcome adjusted for other predictors. Results: PM10levels fell over time; the daily median was 15.8 μg/m3 (interquartile ranges [IQR]: 12.1, 21.0) prior to 2010 but 11.5 μg/m3 (IQR: 8.3, 15.7) in subsequent years. A higher admission day PM10level predicted a worse 30-day mortality – odds ratios 1.09 (95% confidence intervals: 1.05, 1.2) for those >70 years, while for younger patients, this was not significant. The influence of PM on outcomes appeared largely confined to older persons; comparisons between increasing PM10quintiles with Q1 median values of 7.5 μg/m3 had a model predicted mortality of 10.8% but 15.0% at Q5 median values of 29.3 μg/m3. An explanation for such difference in outcomes between older and younger may lie in the computed comorbidity and illness severity scores that were quantitatively markedly more severe with advancing age. Conclusion: PM10levels on the day of admission predicted an increased 30-day in hospital mortality risk, with older patients identified to be more susceptible to poor air quality. The disproportionate impact on older persons may be due to their higher concomitant illness severity and comorbidity scores.

BACKGROUND Altered sodium balance at time of an emergency medical admission adversely impacts on outcome; whether hyponatraemia is independently associated with outcomes or a surrogate of acute illness severity has been debated. METHODS All emergency medical admissions between 2002 and 2017 were studied and a risk score calculated. We compared univarate deciles of admission sodium using a multivariable model, adjusting for risk score. RESULTS There were 106,586 admissions in 54,928 patients. Patients with lower sodium at admission were older at 66.7 years (IQR 46.7-79.5) compared with 63.3 years (IQR 42.9-78.2) with a longer length of stay (LOS) of 6.8 days (IQR 3.0-14.7) versus 4.9 days (IQR 1.8-10.9). They had a higher 30-day in-hospital mortality at 6.4% vs 4.4% (p

An Illness Severity and Co-morbidity composite score can predict 30-day mortality outcome.We computed a summary risk score (RS) for emergency medical admissions and used cluster analysis to define four subsets Results: Four cluster groups were defined. Cluster 1 - RS 7 points (IQR 5, 8) Cluster 2 - 9 (IQR 8, 11), Cluster 3 - 12 (IQR 11, 13) and Cluster 4 - 14 (IQR 13, 15). Clusters predicted 30-day in hospital mortality OR 1.86 (95%CI: 1.82, 1.92); respective rates 1.4% (95% CI: 1.3%, 1.6%), 3.4% (95% CI: 3.1%, 3.6%), 7.8% (95% CI: 7.5%, 8.1%) and 16.5% (95% CI: 15.7%, 17.2%).Cluster grouping of Risk Score was age related; strongest outcome determinant was Acute Illness Severity.

The Acute Medical Admission Unit (AMAU) model of care has been associated with improved short- and medium-term outcomes; whether these improvements are sustained remains unclear. We report on the 15-year outcomes of an AMAU in our institution. All emergency medical admissions between 2002 and 2016 were examined and 30-day in-hospital mortality, admission rates, readmission rates and length of stay (LOS) assessed. We used logistic and Poisson regression and margin statistics to evaluate outcomes. There were 96,305 admissions in 50,612 patients. By admission, the 30-day in-hospital mortality averaged 5.6% (95% CI 5.4 to 5.7%); there was a relative risk reduction (RRR) of 33.9% between 2002 and 2016, from 7.0 to 4.6% (p = 0.001), number need to treat (NNT) 41.9. By unique patient the 30-day in-hospital mortality averaged 10.5% (95% CI 10.3 to10.8%); there was a RRR of 61.7% between 2002 and 2016, from 15.1 to 5.8% (p = 0.001), NNT 10.7. The median LOS was 5.0 days (IQR 2.1, 9.8) and was unaltered over time. Deprivation status strongly influenced the admission rate/1000 population increasing from Q1 7.7 (95% CI 7.6 to 7.8) to Q5 37.8 (95% CI 37.6 to 38.0); this showed a slight trend to increase over time. Total readmissions increased as a function of time; early readmissions (

The relationship between serum potassium levels and mortality in acute medical admissions is uncertain. In particular, the relevance of minor abnormalities in potassium level or variations within the normal range remains to be determined. We performed a retrospective cohort study of all emergency medical admissions to St James's Hospital (Dublin, Ireland) between 2002 and 2012. We used a stepwise logistic regression model to predict in-hospital mortality, adjusting risk estimates for major predictor variables. There were 67,585 admissions in 37,828 patients over 11 years. After removing long-stay patients, 60,864 admissions in 35,168 patients were included in the study. Hypokalaemia was present in 14.5% and hyperkalaemia in 4.9%. In-hospital mortality was 3.9, 5.0, and 18.1% in the normokalaemic, hypokalaemic and hyperkalaemic groups respectively. Hypokalaemic patients had a univariate odds ratio (OR) of 1.29 for in-hospital mortality (95% confidence interval (CI) 1.16–1.43; p

Background : The identification of high-risk patients could form a basis for targetted intervention following an emergency medical admission. Methods: All emergency admissions to our institution over 12 years (2002–13) were included. An Illness Severity method based on admission laboratory parameters, previously developed between 2002 and 2007, was investigated for the 2008–13 cohort. We compared the area under the receiver operating characteristic (AUROC) to predict a 30-day in-hospital death between the original and validating cohorts using logistic multiple variable analyses. We defined six risk subgroups, based on admission laboratory data and examined the frequency of 30-day in-hospital mortality within these subgroups. Results: About 66 933 admissions were recorded in 36 271 patients. Between 2002 and 2007, the 30-day in-hospital mortality was 11.3% but between 2008 and 2013 was 6.7% ( P < 0.001). This represented an absolute risk reduction (ARR) of 4.6%, a relative risk reduction (RRR) of 41.0%, and a number needed to treat of 21.6. The laboratory model was similarly predictive in both cohorts—for 2002–07, the AUROC was 0.82 (95% CI 0.81, 0.82) and for 2008–13 was 0.82 (95% CI 0.81, 0.83). Two high-risk subgroups were identified within each cohort; for 2002–07, these contained 15.0 and 30.2% of admitted patients but 95.5% of in-hospital deaths. For 2008–13, these two groups contained 15.7 and 31.0% of admitted patients but 97.0% of in-hospital deaths. Conclusion: A previously described laboratory score method, based on admission biochemistry, identified patients at high risk for an in-hospital death. Risk profiling at admission is feasible for emergency medical admissions and could offer a means to outcome improvement.

Background The factors influencing hospital readmissions are debated. We assessed whether readmissions could be predicted using routinely collected hospital data. Methods All emergency admissions to a single institution over 12 years (2002–2013) were included. The predictor variables, of acute illness severity, Manchester Triage Category, chronic disabling disease and Charlson co-morbidity scores, were studied univariably and entered into a multivariable logistic regression model to predict the bivariate of any readmission or none. A zero truncated Poisson regression model assessed the predictors against the readmission count and incidence rate ratios were calculated. Factors reflecting the clinical load on the emergency department were examined. Results 66,933 admissions were recorded in 36,271 patients. The readmission rates at 1, 3, 6 and 9 years were 29.5%, 38.9%, 42.9% and 44.1%. Early readmissions represented 14.1%. In the multivariable model, an admission in the previous 6 months was the strongest predictor of readmission, OR of 5.02 (95% CI: 4.86, 5.18). Acute illness severity — OR of 2.68 (95% CI: 2.33, 3.09) for group VI vs group I, and chronic disabling score — OR of 2.08 (95% CI: 1.87, 2.32) for a score of 4 + vs 0 were significant predictors of readmission in the multivariable model. Both of these predictors demonstrated a linear relationship. Illness severity was the strongest predictor of an early readmission within 4 weeks. Conclusion Readmissions increase as a function of time; illness severity, chronic disabling disease score and a recent admission are the strongest predictors of readmission.

For three cohorts (the elderly, socially deprived, and those with chronic disabling disease), the relationship between the concentrations of particulate matter (PM10), sulphur dioxide (SO2), or oxides of nitrogen (NOx) at the time of hospital admission and outcomes (30-day in-hospital mortality) were investigated All emergency admissions (90,423 episodes, recorded in 48,035 patients) between 2002 and 2015 were examined. PM10, SO2, and NOx daily levels from the hospital catchment area were correlated with the outcomes for the older admission cohort (>70 years), those of lower socio-economic status (SES), and with more disabling disease. Adjusted for acuity and complexity, the level of each pollutant on the day of admission independently predicted the 30-day mortality: for PM10–OR 1.11 (95% CI: 1.08, 1.15), SO2–1.20 (95% CI: 1.16, 1.24), and NOx–1.09 (1.06–1.13). For the older admission cohort (≥70 years), as admission day pollution increased (NOx quintiles) the 30-day mortality was higher in the elderly (14.2% vs. 11.3%: p < 0.001). Persons with a lower SES were at increased risk. Persons with more disabling disease also had worse outcomes on days with higher admission particulate matter (PM10 quintiles). Levels of pollutants on the day of admission of emergency medical admissions predicted 30-day hospital mortality.

Background.We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission.Methods.For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program.Results.The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score.Conclusion.A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described.

The community level of disability and social deprivation may result in an emergency hospitalisation; we have examined the annual admission incidence rate for emergency medical conditions in relation to the community prevalence of such factors. All emergency medical admissions (96,305 episodes in 50,612 patients) within the institution’s catchment area were examined between 2002 and 2016. The frequency of disability, level of full-time carers and unemployment for the 74 electoral divisions of the catchment area was regressed against admission rates; incidence rate ratios (IRR) were calculated using truncated Poisson regression. Disability was present in 12.1% of the catchment area population (95% CI = 9.7–15.0). The annual admission incidence rates/1000 population across disability quintiles for the more affluent areas increased from Q1 7.6 (95% CI = 7.4–7.8) to Q5 27.3 (95% CI = 27.0–27.5) and for the more deprived area from Q1 16.6 (95% CI = 16.4, 16.8) to and Q5 40.4 (95% CI = 40.1–40.7). Disability status influenced the overall admission IRR (compared with Q1/Q3) for Q4/Q5 1.11 (95% CI = 1.09–1.13) showing an increased rate of hospitalisation for the more deprived areas. Community disability levels interacted with local area unemployment and frequency of full-time carers; as they increased, a linear relationship between disability and the admission rate incidence was demonstrated. Local catchment area disability prevalence rates in addition to social deprivation factors are an important determinant of the annual incidence rate of emergency medical admissions.

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.

Chronic disabling disease is present in nearly 90% of emergency medical admissions. We have examined its impact on outcomes and costs in one institution, using a database of episodes collected prospectively over 12 years.All emergency admissions (66,933 episodes; 36,271 patients) to St James' Hospital over a 12-year period (2002-13) were evaluated in relation to 30-day in-hospital mortality, length of stay (LOS) and hospital costs. Predictor variables (identified univariately) were entered into a multi-variable logistic regression model to predict 30-day in-hospital mortality. The data were also modelled as count data (absolute LOS, total cost) using zero-truncated Poisson regression.Acute illness severity was the best independent predictor of mortality; chronic disabling disease was an independent predictor (P0.001) for patients with 4+ disabling conditions. Age, adjusted for other predictors, was only independently predictive of mortality for patient 85+ years. Chronic disabling disease was an independent predictor of LOS increasing linearly with incidence rate ratios of 1.35 (95% CI: 1.29, 1.42), 1.59 (95% CI: 1.51, 1.66), 1.73 (95% CI: 1.65, 1.83) and 1.74 (95% CI: 1.65, 1.84) for those with 1, 2, 3 or 4+ disabling conditions, respectively. Age, as a predictor of LOS was strongly correlated with the presence of disabling disease. Chronic disabling disease independently predicted costs non-linearly; those with 2 or more disabling conditions had particularly high total hospital costs.Chronic disabling disease is an independent predictor of hospital LOS and costs in unselected emergency admissions; adjusted for illness severity, it is only a mortality predictor for those with multiple disabling conditions.

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 &lt, 0.001) and the LOS longer (6.3 (95% CI 2.9, 12.2) vs. 4.0 (95% CI 1.5, 8.2) p &lt, 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.

Background: Physician experience has been shown to improve clinical outcomes. Limited numbers of experienced clinicians make it unfeasible that they would care for all patients. We hypothesized that physician experience would impact outcomes for patients with high, but not low, risk of mortality. Methods : All emergency admissions from 2002 to 2013 were studied. Two groups at low and high risk for mortality (1.3% (95% CI: 1.13, 1.62) vs. 19.4% (95% CI: 18.5, 20.3)) were defined by a risk prediction method comprising acute Illness Severity Score, Charlson Comorbidity Index and sepsis status. Length of stay (LOS) and 30-day in-hospital mortality were the primary end points. Consultant experience was studied univariately and also entered into a multiple logistic regression model to predict 30-day in-hospital survival. A zero-truncated Poisson regression model assessed LOS and incidence rate ratios (IRR) were calculated. Results : Over the 12-year study period 66 933 admissions were recorded in 36 271 patients. High-risk patients cared for by more experienced (≥20 years qualified) physicians had a lower 30-day in-hospital mortality (19.4 vs. 17.1% P < 0.001), relative risk reduction 11.9%. The multivariable odds ratio of a 30-day in-hospital death for an experienced physician was 0.89 (95% CI: 0.82, 0.97; P = 0.009). LOS was shorter for high-risk patients allocated to experienced physicians with a multivariable IRR of 0.77 (95% CI: 0.94, 0.99; P = 0.02). No difference in outcomes was found for low-risk patients. Conclusion: High-risk but not low-risk patients have improved outcomes when cared for by physicians with greater experience.

Background: There is interest in emergency medical admissions, the outcomes of major reconfigurations and the development of systems and processes for Acute Medicine. We report on the long-term outcomes of an Acute Medical Admissions Unit (AMAU), using a database of emergency admissions to St James' Hospital, Dublin, from 2002-2012. Methods: All emergency admissions (67,971 episodes in 37,828 patients) were tracked and in-hospital mortality, length of stay and emergency ‘wait’ numbers and times summarized. We examined outcomes using generalized estimating equations, an extension of generalized linear models that permitted adjustment for correlated observations (readmissions). Margins statistics used adjusted predictions to test for interactions of key predictors while controlling for other variables using computations of the average marginal effect. Results: By episode, the in-hospital mortality averaged 5.8% (95%CI 5.6% - 5.9%); the relative risk reduction (RRR) was 35.0% between 2002 and 2012, from 7.0% to 4.6% (p = 0.001), with a number needed to treat (NNT) of 40.7. By unique patient the in-hospital mortality averaged 10.3% (95%CI 10.0% - 10.6%) with a RRR of 60.0% from 14.5% to 5.7% (p = 0.001), with a NNT of 11.4. Emergency Department ‘wait’ numbers decreased by 43%. The main mortality outcome predictors were Illness Severity, Charlson Co-Morbidity, Manchester Triage Category, O2 saturation, blood culture results, transfusion requirement, and a primary respiratory or neurological diagnosis; the model had a high AUROC - 0.88 (95% CI 0.87, 0.88). Conclusion: Institution reform can result in substantial outcome and process measure benefits, improving care delivery to emergency medical admissions.

Background: Increasing hospital or specialist volumes has been shown to improve outcomes; there are little data on volumes and outcomes in emergency medical admissions. We have examined the hospital length of stay (LOS) and 30-day mortality for patients admitted under a consultant ‘of the day’ having high- or low-admission volumes. Methods: An analysis was performed on all emergency medical patients admitted between 1 January 2002 and 31 December 2011, using anonymous patient data. We calculated the numbers of unique patients admitted to each ‘on call’ consultant and allocated the latter to a high- (70th centile with 8/22 consultants) or low-volume (14/22 consultants) category. We examined outcomes (LOS and in-hospital 30-day mortality), by these cut-offs employing logistic regression to calculate unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: The hospital LOS was shorter ( P < 0.001) for high [median 4.2, inter-quartile range (IQR) 1.7, 8.7] compared with the lower volume group (median 4.8, IQR 1.9, 9.7). There was a reduced 30-day in hospital mortality for high-volume (8.2%) compared with low-volume consultants (9.6%: P < 0.01). An admission under a high-volume consultant was independently predictive of survival, after adjustment for other outcome predictors including co-morbidity; the relative risk reduction was 25% [OR 0.75 (95% CI 0.68–0.82): P < 0.001]. Conclusion: In an era of increasing specialization, these data provide support for the concept that the frequency of being ‘on-call’ contributes to maintaining competence with an associated improvement in patient outcomes.

Background Patients with respiratory disorders constitute a major source of activity for Acute Medicine. We have examined the impact of Socio-Economic Status (SES) and weather factors on the outcomes (30-day in-hospital mortality) of emergency hospitalisations with a respiratory presentation. Methods All emergency respiratory admissions to St. James Hospital, Dublin, from 2002 to 2014 were evaluated. Patients were categorized by quintile of Deprivation Index, and evaluated against hospital admission rate (/1000 population) and 30-day in-hospital mortality. 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 There were 32,538 episodes in 14,093 patients, representing 39.5% of medical emergency episodes over the 13-yr period. Deprivation Quintile independently predicted the admission rate, with incidence rate ratios (IRR) of Q3 2.02 (95% CI: 1.27, 3.23), Q4 2.55 (95% CI: 1.35, 4.83) and Q5 5.68 (95% CI: 3.56, 9.06). The 30-day in-hospital mortality for the highest quintile was increased (p 10 ) was predictive for the top two quintiles (>17.2 and 23.8 μg/m 3 respectively) with an OR for a worse outcome of Q4 1.22 (95% CI: 1.07, 1.40) and Q5 1.24 (95% CI: 1.08, 1.42). Weather (season) and the daily temperature did not affect the admission rate but were significantly associated with worse outcome. Conclusion Socio-Economic Status influences the admission rate incidence and hospital mortality of respiratory emergency admissions; local environmental conditions (air pollution and temperature) appear only relevant to the mortality outcomes.

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

Background The ageing of the population may be anticipated to increase demand on hospital resources. We have investigated the relationship between hospital episode costs and age profile in a single centre. Methods All Emergency Medical admissions (33 732 episodes) to an Irish hospital over a 6-year period, categorized into three age groups, were evaluated against total hospital episode costs. Univariate and adjusted incidence rate ratios (IRRs) were calculated using zero truncated Poisson regression. Results The total hospital episode cost increased with age ( P < 0.001). The multi-variable Poisson regression model demonstrated that the most important drivers of overall costs were Acute Illness Severity-IRR 1.36 (95% CI: 1.30, 1.41), Sepsis Status -1.46 (95% CI: 1.42, 1.51) and Chronic Disabling Disease Score -1.25 (95% CI: 1.22, 1.27) and the Age Group as exemplified for those 85 years IRR 1.23 (95% CI: 1.15, 1.32). Conclusion Total hospital episode costs are a product of clinical complexity with contributions from the Acute Illness Severity, Co-Morbidity, Chronic Disabling Disease Score and Sepsis Status. However age is also an important contributor and an increasing patient age profile will have a predictable impact on total hospital episode costs.

Ambulatory care sensitive conditions (ACSCs) are a group of conditions judged to be suitable for healthcare efficiency initiatives to reduce the rate of hospital admissions. All emergency medical admissions to an Irish hospital between 2002 and 2013 were assessed for ACSCs. They were categorised by quintile of deprivation index and evaluated against hospital admission rate. Univariable and multivariable risk estimates were calculated, using logistic regression or zero-truncated Poisson regression. There were 66,861 admissions in 36,214 patients. ACSCs represented 66.4% of admissions. The rate of ACSC admissions increased with deprivation index, Q1 10.4 (95% confidence interval (CI) 10.2–10.5), Q2 17.3 (95% CI 17.2–17.5), Q3 34.0 (95% CI 33.7–34.2), Q4 30.2 (95% CI 30.0–30.4) and Q5 44.5 (95% CI 43.8– 45.1) (p

Multiple studies have suggested an association between weekend hospital admissions and mortality. These have been limited by potential residual confounders and a lack of explanation of causation. We previously attributed adverse weekend outcomes to higher acuity; we have re-examined this question for all emergency medical admissions to our institution from 2002 to 2014. We divided admissions by a weekday or weekend (Friday to Sunday) hospital arrival. We utilised a multivariate logistic regression model, to determine whether the latter was independently predictive of 30-day in-hospital mortality. There were 82,368 admissions in 44,628 patients over the 13-year period. Of admissions, 37.4% occurred at the weekend. The Acute Illness Severity Score, the Charlson Co-morbidity Index and the Chronic Disabling Disease Score were similar by a weekday or weekend admission. The multivariable logistic regression showed no increase in 30-day in-hospital mortality for weekend admissions, odds ratio 1.07 (95% confidence interval 0.98 to 1.16) (p = 0.11). Since the inception of the AMAU, the per patient mortality for a weekend admission has declined from 13.5% in 2002 to 4.4% in 2014. This represents a relative risk reduction of 67.9% with a number needed to treat of 10.8. Outcomes improved similarly for weekday and weekend admissions. No increase in 30-day in-hospital mortality for weekend admissions was found in this study. There has been a substantial reduction in mortality for both weekday and weekend admissions over time.

Aim: to describe the characteristics and outcomes of homeless people admitted to our Internal Medicine service in St. James’s Hospital, Dublin (Ireland), between 2002 and 2011. Methods: we interrogated an anonymized in-patient database. Results: there were 1,460 homeless admissions (623 unique patients; 39% admitted more than once). Most patients were young, male, and had low comorbidity levels. Thirty-seven percent of the admissions were alcohol-related and 27% substance abuse-related. Thirteen percent had an active psychiatric illness. Their in-patient mortality rate was 5%. Seventy two percent were discharged without the residential arrangement being explicitly documented, 15% self-discharged or absconded, and 8% were discharged to a residential facility. Conclusion: results are novel in our context and will be relevant for local policy and practice.

Background. Both physiological- and laboratory-derived variables, alone or in combination, have been used to predict mortality among acute medical admissions. Using the Modification of Diet in Renal Disease (MDRD) not as an estimate of glomerular filtration rate but as an outcome predictor for hospital mortality, we examined the relationship between the MDRD value and in-hospital death during an emergency medical admission. Methods. An analysis was performed on all emergency medical patients admitted between 1 January 2002 and 31 December 2008, using the hospital in-patient enquiry system, linked to the patient administration system and laboratory datasets. Hospital mortality (any in-patient death within 30 days) was obtained from a database of deaths occurring during the same period under physicians participating in the ‘on-call’ roster. Logistic regression was used to calculate unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (CI) for MDRD value. Results. Univariate analysis identified those with MDRD value of

Summary Background: Serum sodium has been shown topredict mortality in heart and liver failure.Aim: To determine whether serum sodium indepen-dently predicts in-hospital mortality during anyemergency medical admission.Design: An analysis was performed of all emergencymedical patients admitted to St James’s Hospital(SJH), Dublin between 1 January 2002 and 31December 2006, using the hospital inpatientenquiry (HIPE) system, linked to the patient admin-istration system and laboratory datasets. Hospitalmortality was obtained from a database of 20 deathsoccurring during the same period under physiciansparticipating in the ‘on call’ roster.Methods: The serum sodium was determined atadmission in all cases where it was deemedclinically necessary. Logistic regression was usedto calculate crude and 25 adjusted odds ratios(ORs). Factors adjusted for included age, illnessseverity score (Modified Apache II score), majordisease category, ICU stay, year effect, bloodtransfusion, gender and sepsis.Results: A total of 14 239 patients (47.5% male)were included in the analysis. Mortality had aU-shaped distribution and was highest in patientswhose sodium level was 140mmol/l. Theunadjusted OR of death within 30 days of admissionwas 3.36 (95% CI 2.59–4.36) and 4.07 (95% CI2.95–5.63) with sodium level 140mmol/l, respectively. Adjustment for all ofthe factors above reduced the mortality odds in allhyponatraemia groups but all remained significantpredictors of mortality. After adjustment for illnessseverity score the OR ratio for death in the>140mmol/l group fell to 1.41 (95% CI 0.97–2.07).Discussion: The serum sodium is a powerful initialmarker of likely mortality in unselected generalmedical patients. The increased death rate inhyponatraemic patients is independent of otherclinical variables, whereas mortality in the hyperna-traemic group is primarily a factor of illness severity.

Aim: To determine the impact of the introduction of an acute medical admission unit (AMAU) on all-cause hospital mortality in unselected patients undergoing acute medical admission to a teaching hospital. Design: Analysis of data recorded in the hospital in-patient enquiry (HIPE) system relating to all emergency medical patients admitted to St James's Hospital (SJH), Dublin between 1 January 2002 and 31 December 2006. Methods: The reference year was 2002, during which patients were admitted to a variety of wards under the care of a named consultant physician. In 2003, two centrally located wards were re-configured to function as an AMAU, and all emergency medical patients were admitted to this unit following emergency department evaluation. Hospital mortality was obtained from a database of deaths occurring during this period and linked to HIPE data. Results: Following the introduction of the AMAU process, all-cause hospital mortality decreased from 12.6% in 2002 to 7.0% in 2006 ( P < 0.0001), representing a 44.4% relative reduction during the course of the 5-year observation period ( P < 0.0001). The Odds ratio (95% confidence interval) for all-cause mortality in 2006 compared with 2002 was 0.28 (0.23, 0.35). This effect was powerfully independent of other covariates, including Charlson co-morbidity and illness severity score (APACHE II), in binary logistic regression analysis and was observed across a wide cross-section of diagnostic groups. Conclusions: The introduction of an AMAU significantly improved all-cause hospital mortality in acute unselected medical patients. The delivery of Acute Medicine may be enhanced by structural reform with emphasis on focus and volume. Prospective studies validating similar models elsewhere should be explored.