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

The architectural characteristics of the hamstring muscles do not differ between male and female elite-level rugby union players

Purpose: To determine whether differences exist in the architectural characteristics of the hamstring muscles of elite-level male and female rugby union players.Methods: Forty elite-level rugby union players (male n = 20, female n = 20) participated in this cross-sectional study. A sonographer acquired static ultrasound images using a 92 mm linear transducer to quantify (via a semi-automated tracing software tool) the architectural characteristics (muscle length, fascicle length, pennation angle, and muscle thickness) of the biceps femoris long head and semimembranosus muscles of participants’ left limb. Muscle length and muscle thickness of the biceps femoris short head and semitendinosus muscles of participants’ left limb were also quantified. Bonferroni adjusted independent samples t-tests were performed to evaluate whether differences exist in the architectural characteristics of the hamstring muscles of elite-level male and female rugby union players.Results: There were no significant differences in fascicle length or pennation angle of the hamstring muscles of elite-level male and female rugby union players. Some significant differences in muscle thickness (biceps femoris short head, and semimembranosus) and muscle length (biceps femoris short head, biceps femoris long head, semitendinosus, and semimembranosus) were observed; in all cases the male players had thicker and longer muscles.Conclusion: At a group level, hamstring muscle fascicle length and pennation angle are unlikely to be a sex-specific intrinsic risk factor for Hamstring strain injuries

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

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 (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

Air Quality and Hospital Outcomes in Emergency Medical Admissions with Respiratory Disease

Background: The impact of very low levels of air pollutants, particulate matter (PM10) and sulfur dioxide (SO2) concentrations, on human health is not well characterized. We examined the outcomes (30-day in-hospital mortality) of emergency hospitalizations of respiratory patients and the level of local pollutants on the day of admission. Methods: All emergency admissions (82,421 episodes in 44,660 patients) were recorded over 13 years (2002–2014) and mortality assessed. The median interquartile ranges (IQR) age was 64.5 (43.9, 78.5) years with the proportion of males at 48.5%. Univariate and multivariate logistic regression was used to examine relationships between pollutant concentration (PM10 and SO2) and odds ratio (OR) for 30-day in hospital death, after adjustment for acuity. Results: Mortality related to each pollutant variable assessed (as quintiles of increasing atmospheric concentration). For PM10 mortality, the highest two quintiles concentrations were significantly increased (p < 0.001) with univariate ORs of 1.30. For SO2, the ORs were 1.32, 1.39, and 1.46, for the top three quintiles. There was also a strong relationship between the underlying respiratory function; with forced expiratory volume (FEV1) in 1 second (FEV1) ≥ 2.0L at the lowest PM10 quintile, mortality was 6.5% (95% CI: 6.1, 6.9) increasing to 9.5% (95% CI: 9.0, 10.0) at the highest PM10 quintile. For patients with FEV1 < 2.0L, the mortality at the lowest PM10 quintile was 9.9% (95% CI: 8.8, 10.9) increasing to 14.2% (95% CI: 12.8, 15.6) at the highest quintile. Conclusion: Despite air quality improvement, there was a clear relationship between pollutant concentration and outcomes for respiratory emergency admissions; additionally, the underlying level of pulmonary function was predictive of in-hospital mortality

Pattern of Investigation Reflects Risk Profile in Emergency Medical Admissions

Demand for hospital resources may increase over time; we have examined all emergency admissions (51,136 episodes) from 2005 to 2013 for underlying trends and whether resource utilization and clinical risk are correlated. We used logistic regression of the resource indicator against 30-day in-hospital mortality and adjusted this risk estimate for other outcome predictors. Generally, resource indicators predicted an increased risk of a 30-day in-hospital death. For CT Brain the Odds Ratio (OR) was 1.37 (95% CI: 1.27, 1.50), CT Abdomen 3.48 (95% CI: 3.02, 4.02) and CT Chest, Thorax, Abdomen and Pelvis 2.50 (95% CI: 2.10, 2.97). Services allied to medicine including Physiotherapy 2.57 (95% CI: 2.35, 2.81), Dietetics 2.53 (95% CI: 2.27, 2.82), Speech and Language 5.29 (95% CI: 4.57, 6.05), Occupational Therapy 2.65 (95% CI: 2.38, 2.94) and Social Work 1.65 (95% CI: 1.48, 1.83) all predicted an increased risk. The in-hospital 30-day mortality increased with resource utilization, from 4.7% (none) to 27.0% (five resources). In acute medical illness, the use of radiological investigations and allied professionals increased over time. Resource utilization was calibrated from case complexity/30-day in-hospital mortality suggesting that complexity determined the need for and validated the use of these resources