Emergency department crowding increases 10-day mortality for non-critical patients: a retrospective observational study.

The current evidence suggests that higher levels of crowding in the Emergency Department (ED) have a negative impact on patient outcomes, including mortality. However, only limited data are available about the association between crowding and mortality, especially for patients discharged from the ED. The primary objective of this study was to establish the association between ED crowding and overall 10-day mortality for non-critical patients. The secondary objective was to perform a subgroup analysis of mortality risk separately for both admitted and discharged patients. An observational single-centre retrospective study was conducted in the Tampere University Hospital ED from January 2018 to February 2020. The ED Occupancy Ratio (EDOR) was used to describe the level of crowding and it was calculated both at patient's arrival and at the maximum point during the stay in the ED. Age, gender, Emergency Medical Service transport, triage acuity, and shift were considered as confounding factors in the analyses. A total of 103,196 ED visits were included. The overall 10-day mortality rate was 1.0% (n = 1022). After controlling for confounding factors, the highest quartile of crowding was identified as an independent risk factor for 10-day mortality. The results were essentially similar whether using the EDOR at arrival (OR 1.31, 95% CI 1.07-1.61, p = 0.009) or the maximum EDOR (OR 1.27, 95% CI 1.04-1.56, p = 0.020). A more precise, mortality-associated threshold of crowding was identified at EDOR 0.9. The subgroup analysis did not yield any statistically significant findings. The risk for 10-day mortality increased among non-critical ED patients treated during the highest EDOR quartile.

Early Warning Software for Emergency Department Crowding.

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters' seasonal methods. We show that the software could predict next hour crowding with an AUC of 0.94 (95% CI: 0.91-0.97) and 24 hour crowding with an AUC of 0.79 (95% CI: 0.74-0.84) using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84 (95% CI: 0.74-0.91).

Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach.

BACKGROUND AND OBJECTIVE: Emergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown. METHODS: We extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric. RESULTS: Calendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant. CONCLUSIONS: Our study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables.

Tumor margins that lead to reoperation in breast cancer: A retrospective register study of 4,489 patients.

BACKGROUND AND OBJECTIVES: Optimal margins for ductal carcinoma in situ (DCIS) remain controversial in breast-conserving surgery (BCS) and mastectomy. We examine the association of positive margins, reoperations, DCIS and age. METHODS: A retrospective study of histopathological reports (4489 patients). Margin positivity was defined as ink on tumor for invasive carcinoma. For DCIS, we applied 2 mm anterior and side margin thresholds, and ink on tumor in the posterior margin. RESULTS: The incidence of positive side margins was 20% in BCS and 5% in mastectomies (p < 0.001). Of these patients, 68% and 14% underwent a reoperation (p < 0.001). After a positive side margin in BCS, the reoperation rates according to age groups were 74% (<49), 69% (50-64), 68% (65-79), and 42% (80+) (p = 0.013). Of BCS patients with invasive carcinoma in the side margin, 73% were reoperated on. A reoperation was performed in 70% of patients with a close (≤1 mm) DCIS side margin, compared to 43% with a wider (1.1-2 mm) margin (p = 0.002). The reoperation rates were 55% in invasive carcinoma with close DCIS, 66% in close extensive intraductal component (EIC), and 83% in close pure DCIS (p < 0.001). CONCLUSIONS: Individual assessment as opposed to rigid adherence to guidelines was used in the decision on reoperation.

NYU-EDA in modelling the effect of COVID-19 on patient volumes in a Finnish emergency department.

BACKGROUND: Emergency departments (EDs) worldwide have been in the epicentre of the novel coronavirus disease (COVID-19). However, the impact of the pandemic and national emergency measures on the number of non-COVID-19 presentations and the assessed acuity of those presentations remain uncertain. METHODS: We acquired a retrospective cohort containing all ED visits in a Finnish secondary care hospital during years 2018, 2019 and 2020. We compared the number of presentations in 2020 during the national state of emergency, i.e. from March 16 to June 11, with numbers from 2018 and 2019. Presentations were stratified using localized New York University Emergency Department Algorithm (NYU-EDA) to evaluate changes in presentations with different acuity levels. RESULTS: A total of 27,526 presentations were observed. Compared to previous two years, total daily presentations were reduced by 23% (from 113 to 87, p < .001). In NYU-EDA classes, Non-Emergent visits were reduced the most by 42% (from 18 to 10, p < .001). Emergent presentations were reduced by 19 to 28% depending on the subgroup (p < .001). Number of injuries were reduced by 25% (from 27 to 20, p < .001). The NYU-EDA distribution changed statistically significantly with 4% point reduction in Non-Emergent visits (from 16 to 12%, p < .001) and 0.9% point increase in Alcohol-related visits (from 1.6 to 2.5%, p < .001). CONCLUSIONS: We observed a significant reduction in total ED visits in the course of national state of emergency. Presentations were reduced in most of the NYU-EDA groups irrespective of the assessed acuity. A compensatory increase in presentations was not observed in the course of the 3 month lockdown. This implies either reduction in overall morbidity caused by decreased societal activity or widespread unwillingness to seek required medical advice.

Differential mobility spectrometry imaging for pathological applications.

Pathologic examination of clinical tissue samples is time consuming and often does not involve the comprehensive analysis of the whole specimen. Automated tissue analysis systems have potential to make the workflow of a pathologist more efficient and to support in clinical decision-making. So far, these systems have been based on application of mass spectrometry imaging (MSI). MSI provides high fidelity and the results in tissue identification are promising. However, the high cost and need for maintenance limit the adoption of MSI in the clinical setting. Thus, there is a need for new innovations in the field of pathological tissue imaging. In this study, we show that differential ion mobility spectrometry (DMS) is a viable option in tissue imaging. We demonstrate that a DMS-driven solution performs with up to 92% accuracy in differentiating between two grossly distinct animal tissues. In addition, our model is able to classify the correct tissue with 81% accuracy in an eight-class setting. The DMS-based system is a significant innovation in a field dominated by mass-spectrometry-based solutions. By developing the presented platform further, DMS technology could be a cost-effective and helpful tool for automated pathological analysis.