Reduced 30-day in-hospital but increased long-term mortality for weekend vs weekday acute medical admission.

BACKGROUND: Acute medical admission at the weekend has been reported to be associated with increased mortality. We aimed to assess 30-day in-hospital mortality and subsequent follow-up of all community deaths following discharge for acute medical admission to our institution over 21 years. METHODS: We employed a database of all acute medical admissions to our institution over 21 years (2002-2023). We compared 30-day in-hospital mortality by weekend (Saturday/Sunday) or weekday (Tuesday/Wednesday) admission. Outcome post-discharge was determined from the National Death Register to December 2021. Predictors of 30-day in-hospital and long-term mortality were analysed by logistic regression or Cox proportional hazards models. RESULTS: The study population consisted of 109,232 admissions in 57,059 patients. A weekend admission was associated with a reduced 30-day in-hospital mortality, odds ratio (OR) 0.70 (95%CI 0.65, 0.76). Major predictors of 30-day in-hospital mortality were acute illness severity score (AISS) OR 6.9 (95%CI 5.5, 8.6) and comorbidity score OR 2.4 (95%CI 1.2, 4.6). At a median follow-up of 5.9 years post-discharge, 19.0% had died. The strongest long-term predictor of mortality was admission AISS OR 6.7 (95%CI 4.6, 9.9). The overall survival half-life after hospital discharge was 16.6 years. Survival was significantly worse for weekend admissions at 20.8 years compared to weekday admissions at 13.3 years. CONCLUSION: Weekend admission of acute medical patients is associated with reduced 30-day in-hospital mortality but reduced long-term survival.

Red Cell Distribution Width as a Prognostic Indicator in Acute Medical Admissions.

The red cell distribution width (RDW) is the coefficient of variation of the mean corpuscular volume (MCV). We sought to evaluate RDW as a predictor of outcomes following acute medical admission. We studied 10 years of acute medical admissions (2002-2011) with subsequent follow-up to 2021. RDW was converted to a categorical variable, Q1 < 12.9 fl, Q2-Q4 ≥ 12.9 and <15.7 fL and Q5 ≥ 15.7 fL. The predictive value of RDW for 30-day in-hospital and long-term mortality was evaluated with logistic and Cox regression modelling. Adjusted odds ratios (aORs) were calculated and loss of life years estimated. There were 62,184 admissions in 35,140 patients. The 30-day in-hospital mortality (n = 3646) occurred in 5.9% of admissions. An additional 15,086 (42.9%) deaths occurred by December 2021. Admission RDW independently predicted 30-day in-hospital mortality aOR 1.93 (95%CI 1.79, 2.07). Admission RDW independently predicted long-term mortality aOR 1.04 (95%CI 1.02, 1.05). Median survival post-admission was 189 months. For those with admission RDW in Q5, observed survival half-life was 133 months-this represents a shortfall of 5.7 life years (33.9%). In conclusion, admission RDW independently predicts 30-day in-hospital and long-term mortality.

Hospital mortality and length of stay differences in emergency medical admissions related to 'on-call' specialty.

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 < 0.001), Charlson Index (> group 0; 61.5% vs. 54.6%; p < 0.001), and Comorbidity Score (40.7% vs. 36.7%; p < 0.001). Over time, there was a small (+ 8%) but significant increase in 30-day in-hospital mortality. Mortality rates for Acute Medicine, major medical specialties, and Elderly Care were not different at 5.1% (95% CI: 4.7, 5.5), 4.7% (95% CI: 4.3, 5.1), and 4.7% (95% CI: 3.9, 5.4), respectively. Elderly Care admissions had shorter LOS (7.8 days (95% CI: 7.6, 8.0)) compared with either Acute Medicine (8.7 days (95% CI: 8.6, 8.8)) or major medical specialties (8.7 days (95% CI: 8.6, 8.9)). 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.

Prognostic value and clinical utility of NT-proBNP in acute emergency medical admissions.

BACKGROUND: 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. AIM: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.

The presence of a urinary catheter is an important predictor of in-hospital mortality in internal medicine patients.

RATIONALE AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Emergency medical admissions and COVID-19: impact on 30-day mortality and hospital length of stay.

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.

Non-Specific Clinical Presentations are Not Prognostic and do not Anticipate Hospital Length of Stay or Resource Utilization.

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.

Is there excessive troponin testing in clinical practice? Evidence from emergency medical admissions.

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.

Comparative influence of Acute Illness Severity and comorbidity on mortality.

BACKGROUND: The extent to which illness severity and comorbidity determine the outcome of an emergency medical admission is uncertain. We aim to quantitate the relative effect of these factors on mortality. METHODS: We evaluated all emergency medical admission to our institution between 2002 and 2018. We derived an Acute Illness Severity Score (AISS) and Comorbidity Score from admission data and International Classification of Diseases codings. We employed a multivariable logistic regression model to relate both to 30-day in-hospital mortality. RESULTS: There were 113,807 admissions in 58,126 patients. Both AISS, Odds Ratio (OR) 4.4 (95%CI 3.5, 5.5), and Comorbidity Score, OR 1.91 (95%CI 1.67, 2.18), independently predicted 30-day in-hospital mortality. The two highest AISS risk groups encompassed 46.5% of admissions with predicted mortality of 5.9% (95%CI 5.7%, 6.1%) and 14.4% (95%CI 13.9%, 14.8%) respectively. Comorbidity Score >=10 occurred in 17.9% of admissions with a predicted mortality of 13.3%. AISS and Comorbidity Score interacted to adversely influence mortality; the threshold effect for Comorbidity Score was reduced at high levels of AISS. CONCLUSION: High AISS and Comorbidity Scores were predictive of 30-day in-hospital mortality and relatively common in emergency medical admissions. There is a strong interaction between the two scores.

Outcomes in acute medicine - Evidence from extended observations on readmissions, hospital length of stay and mortality outcomes.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 patients >50 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. CONCLUSION: 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.

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

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

Hyponatraemia during an emergency medical admission as a marker of illness severity & case complexity.

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 < 0.001). Admission sodium predicted survival - OR 0.89 (95%CI 0.88-0.90). We adjusted the model with a Risk Score that is predictive and exponentially related to 30-day in-hospital mortality. When adjusted for Risk Score, the admission sodium value was less predictive - OR 0.95 (95%CI 0.92-0.97). The cumulative percentages within the lowest five deciles fell from 63.3% between 2002 and 2009 to 48.1% from 2010 to 2017. The slope of the prediction line relating admission sodium to mortality did not change over time but a lower mortality rate was predicted at any given sodium level. CONCLUSION: Hyponatraemia at the time of an emergency medical admission is predictive and probably a marker of Acuity Illness Severity and Case Complexity. Both the frequency of abnormality in admission sodium and mortality have fallen over time.

Lung function and outcomes in emergency medical admissions.

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.

Relationship of the clinical acuity & complexity to the outcome of an emergency medical admission.

BACKGROUND: An Illness Severity and Co-morbidity composite score can predict 30-day mortality outcome. METHODS: 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%). CONCLUSION: Cluster grouping of Risk Score was age related; strongest outcome determinant was Acute Illness Severity.