Search

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.

Background The outcomes of acute medical admissions have been shown to be influenced by a variety of factors including system, patient, societal, and physician-specific differences. Aim To evaluate the influence of on-call specialty on outcomes in acute medical admissions. Methods All acute medical admissions to our institution from 2015 to 2020 were evaluated. Admissions were grouped based on admitting specialty. Thirty-day in-hospital mortality and length of stay (LOS) were evaluated. Data was analysed using multivariable logistic regression and truncated Poisson regression modelling. Results There were 50,347 admissions in 30,228 patients. The majority of admissions were under Acute Medicine (47.0%), and major medical subspecialties (36.1%); Elderly Care admitted 12.1%. Acute Medicine admissions were older at 72.9 years (IQR 57.0, 82.9) vs. 67.2 years (IQR 50.1, 80.2), had higher Acute Illness Severity (grades 4–6: 85.9% vs. 81.3%; p group 0; 61.5% vs. 54.6%; p p Conclusion No difference in mortality and minor differences in LOS were observed. The prior pattern of improved outcomes year on year for emergency medical admissions appears ended.

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.

Mortality rates are used to assess the quality of hospital care after appropriate adjustment for case-mix. Urinary catheters are frequent in hospitalized adults and might be a marker of patient frailty and illness severity. However, we know of no attempts to estimate the predictive value of indwelling catheters for specific patient outcomes. The objective of the present study was to (a) identify the variables associated with the presence of a urinary catheter and (b) determine whether it predicts in-hospital mortality after adjustment for these variables.The study population included all acutely admitted adult patients in 2020 (exploratory cohort) and January-October 2021 (validation cohort) to internal medicine, cardiology and intensive care departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. There were no exclusion criteria. The predictor variables were the presence of a urinary catheter on admission, age, gender, comorbidities and admission laboratory test results. We used bivariate and multivariate logistic regression to test the associations between the presence of a urinary catheter and mortality after adjustment for the remaining independent variables on admission.The presence of a urinary catheter was associated with other independent variables. In 2020, the odds of in-hospital mortality in patients with a urinary catheter before and after adjustment for the remaining predictors were 14.3 (11.6-17.7) and 6.05 (4.78-7.65), respectively. Adding the presence of a urinary catheter to the prediction logistic regression model increased its c-statistic from 0.887 (0.880-0.894) to 0.907 (0.901-0.913). The results of the validation cohort reduplicated those of the exploratory cohort.The presence of a urinary catheter on admission is an important and independent predictor of in-hospital mortality in acutely hospitalized adults in internal medicine departments.

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 are data that suggest that women hospitalised for a variety of medical conditions may have worse outcomes than men; there is a paucity of literature on hospital mortality outcome by gender and Socio-Economic status (SES) for unselected admissions. Methods: Emergency medical admissions between 2002 and 2018 were examined. We assessed 30-day in-hospital mortality, by gender and SES, using logistic regression and margins statistics modelled outcomes against predictor variables. Results: There were 113,807 episodes in 58,126 patients over the period, with known SES status. There were multiple admissions per patient; only 45.4% had a single admission with the percentage of patients with 1, 2, or 3 at 18.8%, 10.4% and 6.5%, respectively. The average per patient 30-day in-hospital mortality was 11.1% (95%CI:10.6%, 11.6%) for males and 11.0% (95%CI:10.5%, 11.6%) females (p = 0.84). Males from higher, 12.2% (95%CI:10.6%, 13.8%), or lower SES small areas, 12.6% (95%CI: 12.1%, 13.1%), had equivalent 30-day mortality outcomes. Females from higher SES had significantly better outcomes compared with females from lower SES small areas- 9.4% (95% CI:8.0%, 10.8%) versus 12.7% (95%CI:12.2%,13.2%). Conclusion: 30-day in-hospital mortality adjusted for outcome predictors were similar for males and females; however, whereas the model-adjusted mortality for males was not different across SES, females of lower SES had significantly worse outcomes than those of higher SES.

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.

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: 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).

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 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.

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 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 B25, 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.

Background Concurrent with an extension in longevity, a prodrome of ill-health (‘disability’ identifiable by certain International Classification of Disease (ICD) 9/ICD10 codes) predates the acute emergency presentation. To date, no study has assessed the effect of such ‘disability’ on outcomes of emergency medical admissions. Aim To devise a new method of scoring the burden of ‘disability’ and assess its relevance to outcomes of acute hospital admissions. Methods All emergency admissions (67 971 episodes in n = 37 828 patients) to St James' Hospital, Dublin, Ireland over an 11-year period (2002–2012) were studied, and 30-day in-hospital mortality and length of stay were assessed as objective end-points. Patients were classified according to a validated ‘disability’ classification method and scored from 0 to 4+ (5 classes), dependent on number of ICD9/ICD10 ‘hits’ in hospital episode codes. Results A disabling score of zero was present in 10.6% of patients. Scores of 1, 2, 3 and 4+ (classified by the number of organ systems involved) occurred with frequencies of 23.3%, 28.7%, 21.9% and 15.5% respectively. The ‘disability’ score was strongly driven by age. The 30-day mortality rates were 0.9% (no score), 2.6%, 4.1%, 6.3% and 10.9%. Surviving patients remained in hospital for medians of 1.8 (no score), 3.9, 6.1, 8.1 and 9.7 days respectively. High ‘disability’ and illness severity predicted a particularly bad outcome. Conclusion Disability burden, irrespective of organ system at emergency medical admission, independently predicts worse outcomes and a longer in-hospital stay.

The aims of this study were to examine the relationship between admission blood glucose and mortality in a large, unselected cohort of acutely ill medical patients and to assess the impact of diabetes on this relationship.We studied the broad pattern of acute medical admissions over an eight year period and the impact of admission serum glucose on in-hospital mortality. Significant predictors of outcome, including acute illness severity and co-morbidity, were entered into a multivariate regression model, adjusting the univariate estimates of the glycaemic status on mortality.There were 45,068 consecutive acute medical emergency admissions between 2005 and 2012. The normoglycaemic (4.0 ≤7.0 mmol/l) cohort (86%) had a 3.9% in-hospital mortality. Both hypoglycaemia (OR: 3.23: 95% CI: 2.59-4.04; p0.001) and hyperglycaemia (OR: 2.1; 95% CI: 1.9-2.4; p0.001) predicted an increased risk of an in-hospital death. Neither of these increased risks were fully adjusted nor explained by a highly predictive outcome model, using multiple acute illness parameters. Hyperglycaemia did not carry similar adverse prognostic implications for patients with diabetes.In patients without diabetes, an abnormal serum glucose is independently predictive of an increased mortality among the broad cohort of acute emergency medical patients. Similar disturbances of glucose homeostasis for patients with diabetes do not confer equivalent adverse prognostic implications.