High-sensitivity troponin as an outcome predictor in acute medical admissions

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.

Associations between elevated high-sensitive cardiac troponin t and outcomes in patients with acute abdominal pain

PURPOSE

The purpose of this study was to determine outcomes in patients presenting to emergency department (ED) with acute abdominal pain and suspected occult myocardial injury [OMI (high-sensitive cardiac troponin T, hs-cTnT level > 14 ng/L)] without clinical signs of myocardial ischaemia. We hypothesized that OMI is a common entity associated with poor outcomes.

METHODS

After institutional research ethics committee approval, a retrospective review was performed on patients subjected to extended use of hs-cTnT measurements during two months period in patients admitted to ED with a chief complaint of abdominal pain, aged 30 years or older and triaged to red, orange, or yellow categories. Primary outcomes were 30-day, six-month, and one-year mortality, respectively. Adjusted mortality rates were compared using the Cox proportional hazard regression model.

RESULTS

Overall, 1000 consecutive patients were screened. A total of 375 patients were subjected to hs-cTnT measurement and 156 of them (41.6%) experienced OMI. None of the patients had acute myocardial infarction diagnosed in the ED. Patients with OMI had a significantly higher 30-day, six-month and one-year mortality compared to the normal hs-cTnT level group [12.8% (20/156) vs. 3.7% (8/219), p = 0.001, 34.0% (53/156) vs. 6.9% (15/219), p < 0.001 and 39.1% (61/156) vs. 9.1 (20/219), p < 0.001, respectively]. OMI was an independent risk factor for mortality at every time point analyzed.

CONCLUSION

Our investigation noted OMI in older patients with co-morbidities and in higher triage category presenting with abdominal pain to ED, respectively. OMI is an independent risk factor for poor outcomes that warrants appropriate screening and management strategy. Our results support the use of hs-cTnT as a prognostication tool in this subgroup of ED patients.

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.

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.

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.

Value of biomarkers in predicting mortality in older medical emergency department patients: a Dutch prospective study

OBJECTIVE

Older emergency department (ED) patients are at high risk of mortality, and it is important to predict which patients are at highest risk. Biomarkers such as lactate, high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), D-dimer and procalcitonin may be able to identify those at risk. We aimed to assess the discriminatory value of these biomarkers for 30-day mortality and other adverse outcomes.

DESIGN

Prospective cohort study. On arrival of patients, five biomarkers were measured. Area under the curves (AUCs) and interval likelihood ratios (LRs) were calculated to investigate the discriminatory value of the biomarkers.

SETTING

ED in the Netherlands.

PARTICIPANTS

Older (≥65 years) medical ED patients, referred for internal medicine or gastroenterology.

PRIMARY AND SECONDARY OUTCOME MEASURES

30-day mortality was the primary outcome measure, while other adverse outcomes (intensive care unit/medium care unit admission, prolonged length of hospital stay, loss of independent living and unplanned readmission) were the composite secondary outcome measure.

RESULTS

The median age of the 450 included patients was 79 years (IQR 73-85). In total, 51 (11.3%) patients died within 30 days. The AUCs of all biomarkers for prediction of mortality were sufficient to good, with the highest AUC of 0.73 for hs-cTnT and NT-proBNP. Only for the highest lactate values, the LR was high enough (29.0) to be applicable for clinical decision making, but this applied to a minority of patients. The AUC for the composite secondary outcome (intensive and medium care admission, length of hospital stay >7 days, loss of independent living and unplanned readmission within 30 days) was lower, ranging between 0.58 and 0.67.

CONCLUSIONS

Although all five biomarkers predict 30-day mortality in older medical ED patients, their individual discriminatory value was not high enough to contribute to clinical decision making.

TRIAL REGISTRATION NUMBER

NCT02946398; Results.

Air pollution and comorbidity burden influencing acute hospital mortality outcomes in a large academic teaching hospital in Dublin, Ireland: a semi-ecologic analysis

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.

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.

Study protocol for a multicentre prospective cohort study to identify predictors of adverse outcome in older medical emergency department patients (the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) study)

BACKGROUND

Older patients (≥65 years old) experience high rates of adverse outcomes after an emergency department (ED) visit. Reliable tools to predict adverse outcomes in this population are lacking. This manuscript comprises a study protocol for the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) study that aims to identify predictors of adverse outcome (including triage- and risk stratification scores) and intends to design a feasible prediction model for older patients that can be used in the ED.

METHODS

The RISE UP study is a prospective observational multicentre cohort study in older (≥65 years of age) ED patients treated by internists or gastroenterologists in Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. After obtaining informed consent, patients characteristics, vital signs, functional status and routine laboratory tests will be retrieved. In addition, disease perception questionnaires will be filled out by patients or their caregivers and clinical impression questionnaires by nurses and physicians. Moreover, both arterial and venous blood samples will be taken in order to determine additional biomarkers. The discriminatory value of triage- and risk stratification scores, clinical impression scores and laboratory tests will be evaluated. Univariable logistic regression will be used to identify predictors of adverse outcomes. With these data we intend to develop a clinical prediction model for 30-day mortality using multivariable logistic regression. This model will be validated in an external cohort. Our primary endpoint is 30-day all-cause mortality. The secondary (composite) endpoint consist of 30-day mortality, length of hospital stay, admission to intensive- or medium care units, readmission and loss of independent living. Patients will be followed up for at least 30 days and, if possible, for one year.

DISCUSSION

In this study, we will retrieve a broad range of data concerning adverse outcomes in older patients visiting the ED with medical problems. We intend to develop a clinical tool for identification of older patients at risk of adverse outcomes that is feasible for use in the ED, in order to improve clinical decision making and medical care.

TRIAL REGISTRATION

Retrospectively registered on clinicaltrials.gov ( NCT02946398 ; 9/20/2016).

Comparing the effectiveness of three scoring systems in predicting adult patient outcomes in the emergency department

This study aimed to evaluate the performance of the rapid acute physiology score (RAPS), the rapid emergency medicine score (REMS), and the modified early warning score (MEWS) in predicting the outcomes of adult patients presenting to the emergency department (ED).A retrospective review was undertaken between February 2014 and February 2018 in an adult ED of a 3300-bed university hospital. The RAPS, REMS, and MEWS were calculated to assess their capability to predict hospital admission, length of hospital stay, and in-hospital mortality, using area under receiver operating characteristic analysis. Multivariate analysis was used to identify variables that were independent predictors of the outcomes.We included 39,977 patients who had presented to the ED during 48 consecutive months, of whom 4857 were admitted and 213 died in hospital. The predictabilities of REMS, RAPS, and MEWS for hospital admission were 0.76, 0.59, and 0.55, respectively; the predictability of REMS, RAPS, and MEWS for hospital mortality were 0.88, 0.72, and 0.73, respectively; and the predictability of REMS, RAPS, and MEWS for length of hospital stay were 0.76, 0.67, and 0.65, respectively. Multivariate analysis showed that the Glasgow coma scale (GCS) (odds ratio (OR), 1.61; P < .001), age (OR, 1.50; P < .001), and MAP (OR, 1.27; P < .001) were independent predictors for hospital admission; GCS (OR, 2.92; P < .001), respiratory rate (RR) (OR, 2.69; P < .001), peripheral oxygen saturation (OR, 2.67; P < .001), MAP (OR, 2.11; P < .001), age (OR, 1.75; P < .001), and pulse rate (PR) (OR, 1.73; P < .001) were independent predictors for in-hospital mortality; and RR (OR, 1.41; P < .001), temperature (OR, 1.05; P = .01), and PR (OR, 0.96; P = .04) were independent predictors for length of hospital stay.Our study evaluated and confirmed the REMS as a powerful predictor of ED adult patient outcomes, including hospital admission, length of hospital stay, and in-hospital mortality compared to RAPS and MEWS.

Persons with disability, social deprivation and an emergency medical admission

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram

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.

High Risk Subgroups Sensitive to Air Pollution Levels Following an Emergency Medical Admission

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 (NO_x) 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 NO_x 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 NO_x-1.09 (1.06-1.13). For the older admission cohort (≥70 years), as admission day pollution increased (NO_x 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.

Social Factors Determine the Emergency Medical Admission Workload

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

Ready for Prime Time? Biomarkers in Sepsis

Sepsis is a common condition managed in the emergency department. Current diagnosis relies on physiologic criteria and suspicion of a source of infection using history, physical examination, laboratory studies, and imaging studies. The infection triggers a host response with the aim to destroy the pathogen, and this response can be measured. A reliable biomarker for sepsis should assist with earlier diagnosis, improve risk stratification, or improve clinical decision making. Current biomarkers for sepsis include lactate, troponin, and procalcitonin. This article discusses the use of lactate, procalcitonin, troponin, and novel biomarkers for use in sepsis.

Cardiac Troponin I is Increased in Patients with Polytrauma and Chest or Head Trauma. Results of A Retrospective Case-Control Study

Background

We performed a retrospective case-control study to assess the values of cardiac troponin I (cTnI) in a large number of patients admitted to the emergency department (ED) with different types of trauma.

Methods

The study population consisted of all patients aged 18 years or older admitted to the local ED with all types of traumas over a 1-year period. Results of cTnI were compared with those of 125 consecutive blood donors and 25 non-cardiac chest pain ED patients.

Results

The final study population consisted of 380 trauma patients, 10 with isolated abdominal trauma, 99 with isolated trauma of the limbs, 49 with isolated chest trauma, 145 with isolated head trauma and 77 with polytrauma. The concentration of cTnI did not differ among the three study populations, but the frequency of measurable values was substantially higher in patients with trauma (63%) than in blood donors and non-cardiac chest pain ED patients (both 20%). The frequency of cTnI values above the 99^th percentile of the reference range was significantly higher in trauma patients (20%) than in blood donors (0%) and noncardiac chest pain ED patients (8%). Increased cTnI values were more frequent after head trauma (21%), chest trauma (27%) and polytrauma (29%) compared to patients with abdominal (0%) or limbs trauma (8%).

Conclusions

These results suggest that the measurement of cardiac troponin may be advisable to identify potential cardiac involvement in trauma patients, especially in those with polytrauma and head or chest trauma.