Sex differences in clinical presentation and risk stratification in the emergency department: an observational multicenter cohort study

Objective: The aim of this study was to investigate whether sex differences exist in disease presentations, disease severity and (case-mix adjusted) outcomes in the Emergency Department (ED).Methods: Observational multicenter cohort study using the Netherlands Emergency Department Evaluation Database (NEED), including patients >= 18 years of three Dutch EDs. Multivariable logistic regression was used to study the associations between sex and outcome measures in-hospital mortality and Intensive Care Unit/Medium Care Unit (ICU/MCU) admission in ED patients and in subgroups triage categories and presenting complaints.Results: Of 148,825 patients, 72,554 (48.8%) were females. Patient characteristics at ED presentation and diagnoses (such as pneumonia, cerebral infarction, and fractures) were comparable between sexes at ED presentation. In-hospital mortality was 2.2% in males and 1.7% in females. ICU/MCU admission was 4.7% in males and 3.1% in females. Males had higher unadjusted (OR 1.34(1.25-1.45)) and adjusted (AOR 1.34(1.24-1.46)) risks for mortality, and unadjusted (OR 1.54(1.46-1.63)) and adjusted (AOR 1.46(1.37-1.56)) risks for ICU/MCU admission. Males had higher adjusted mortality and ICU/MCU admission for all triage categories, and with almost all presenting complaints except for headache.Conclusions: Although patient characteristics at ED presentation for both sexes are comparable, males are at higher unadjusted and adjusted risk for adverse outcomes. Males have higher risks in all triage categories and with almost all presenting complaints. Future studies should investigate reasons for higher risk in male ED patients

Machine learning for developing a prediction model of hospital admission of emergency department patients:Hype or hope?

Objective: Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models predicting the hospitalization of ED patients and conventional regression techniques at three points in time after ED registration. Methods: We analyzed consecutive ED patients of three hospitals using the Netherlands Emergency Department Evaluation Database (NEED). We developed prediction models for hospitalization using an increasing number of data available at triage, similar to 30 min (including vital signs) and similar to 2 h (including laboratory tests) after ED registration, using ML (random forest, gradient boosted decision trees, deep neural networks) and multivariable logistic regression analysis (including spline transformations for continuous predictors). Demographics, urgency, presenting complaints, disease severity and proxies for comorbidity, and complexity were used as covariates. We compared the performance using the area under the ROC curve in independent validation sets from each hospital. Results: We included 172,104 ED patients of whom 66,782 (39 %) were hospitalized. The AUC of the multi-variable logistic regression model was 0.82 (0.78-0.86) at triage, 0.84 (0.81-0.86) at similar to 30 min and 0.83 (0.75-0.92) after similar to 2 h. The best performing ML model over time was the gradient boosted decision trees model with an AUC of 0.84 (0.77-0.88) at triage, 0.86 (0.82-0.89) at similar to 30 min and 0.86 (0.74-0.93) after similar to 2 h. Conclusions: Our study showed that machine learning models had an excellent but similar predictive performance as the logistic regression model for predicting hospital admission. In comparison to the 30-min model, the 2-h model did not show a performance improvement. After further validation, these prediction models could support management decisions by real-time feedback to medical personal

Development and External Validation of the International Early Warning Score for Improved Age- and Sex-Adjusted In-Hospital Mortality Prediction in the Emergency Department

Objectives: Early Warning Scores (EWSs) have a great potential to assist clinical decision-making in the emergency department (ED). However, many EWS contain methodological weaknesses in development and validation and have poor predictive performance in older patients. The aim of this study was to develop and externally validate an International Early Warning Score (IEWS) based on a recalibrated National Early warning Score (NEWS) model including age and sex and evaluate its performance independently at arrival to the ED in three age categories (18-65, 66-80, &gt; 80 yr). Design: International multicenter cohort study. Setting: Data was used from three Dutch EDs. External validation was performed in two EDs in Denmark. Patients: All consecutive ED patients greater than or equal to 18 years in the Netherlands Emergency department Evaluation Database (NEED) with at least two registered vital signs were included, resulting in 95,553 patients. For external validation, 14,809 patients were included from a Danish Multicenter Cohort (DMC). Measurements and Main Results: Model performance to predict in-hospital mortality was evaluated by discrimination, calibration curves and summary statistics, reclassification, and clinical usefulness by decision curve analysis. In-hospital mortality rate was 2.4% (n = 2,314) in the NEED and 2.5% (n = 365) in the DMC. Overall, the IEWS performed significantly better than NEWS with an area under the receiving operating characteristic of 0.89 (95% CIs, 0.89-0.90) versus 0.82 (0.82-0.83) in the NEED and 0.87 (0.85-0.88) versus 0.82 (0.80-0.84) at external validation. Calibration for NEWS predictions underestimated risk in older patients and overestimated risk in the youngest, while calibration improved for IEWS with a substantial reclassification of patients from low to high risk and a standardized net benefit of 5-15% in the relevant risk range for all age categories. Conclusions: The IEWS substantially improves in-hospital mortality prediction for all ED patients greater than or equal to18 years.</p

The temporal order of changes in physical activity and subjective sleep in depressed versus nondepressed individuals:Findings From the MOOVD Study

Epidemiological studies have shown an association between physical activity and sleep, but it is unclear what the temporal order of this association is and whether it differs for depressed patients and healthy controls. Using a multiple repeated observations design, 27 depressed and 27 pair-matched nondepressed participants completed daily measurements of subjective sleep quality and duration during 30 consecutive days while an accelerometer continuously registered their physical activity. Changes in sleep duration, not quality, predicted next-day changes in physical activity (B = -0.21, p <.001), but not the other way around. Significant heterogeneity between individuals was observed, but the effect was not different for depressed and nondepressed participants. The findings underline the strength of a multiple repeated observations design in observational sleep research

The association between presenting complaints and clinical outcomes in emergency department patients of different age categories

BACKGROUND AND IMPORTANCE: Although aging societies in Western Europe use presenting complaints (PCs) in emergency departments (EDs) triage systems to determine the urgency and severity of the care demand, it is unclear whether their prognostic value is age-dependent. OBJECTIVE: To assess the frequency and association of PCs with hospitalization and mortality across age categories. METHODS: An observational multicenter study using all consecutive visits of three EDs in the Netherlands Emergency department Evaluation Database. Patients were stratified by age category (0-18; 19-50; 51-65; 66-80; >80 years), in which the association between PCs and case-mix adjusted hospitalization and mortality was studied using multivariable logistic regression analysis (adjusting for demographics, hospital, disease severity, comorbidity and other PCs). RESULTS: We included 172  104 ED-visits. The most frequent PCs were 'extremity problems' [range across age categories (13.5-40.8%)], 'feeling unwell' (9.5-23.4%), 'abdominal pain' (6.0-13.9%), 'dyspnea' (4.5-13.3%) and 'chest pain' (0.6-10.7%). For most PCs, the observed and the case-mix-adjusted odds for hospitalization and mortality increased the higher the age category. The most common PCs with the highest adjusted odds ratios (AORs, 95% CI) for hospitalization were 'diarrhea and vomiting' [2.30 (2.02-2.62)] and 'feeling unwell' [1.60 (1.48-1.73)]. Low hospitalization risk was found for 'chest pain' [0.58 (0.53-0.63)] and 'palpitations' [0.64 (0.58-0.71)]. CONCLUSIONS: Frequency of PCs in ED patients varies with age, but the same PCs occur in all age categories. For most PCs, (case-mix adjusted) hospitalization and mortality vary across age categories. 'Chest pain' and 'palpitations,' usually triaged 'very urgent', carry a low risk for hospitalization and mortality

Impact of infection on proteome-wide glycosylation revealed by distinct signatures for bacterial and viral pathogens

Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection