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OBJECTIVES: To determine whether implementation of an Emergency Critical Care Program (ECCP) is associated with improved survival and early downgrade of critically ill medical patients in the emergency department (ED). DESIGN: Single-center, retrospective cohort study using ED-visit data between 2015 and 2019. SETTING: Tertiary academic medical center. PATIENTS: Adult medical patients presenting to the ED with a critical care admission order within 12 hours of arrival. INTERVENTIONS: Dedicated bedside critical care for medical ICU patients by an ED-based intensivist following initial resuscitation by the ED team. MEASUREMENTS AND MAIN RESULTS: Primary outcomes were inhospital mortality and the proportion of patients downgraded to non-ICU status while in the ED within 6 hours of the critical care admission order (ED downgrade <6 hr). A difference-in-differences (DiD) analysis compared the change in outcomes for patients arriving during ECCP hours (2 pm to midnight, weekdays) between the preintervention period (2015-2017) and the intervention period (2017-2019) to the change in outcomes for patients arriving during non-ECCP hours (all other hours). Adjustment for severity of illness was performed using the emergency critical care Sequential Organ Failure Assessment (eccSOFA) score. The primary cohort included 2,250 patients. The DiDs for the eccSOFA-adjusted inhospital mortality decreased by 6.0% (95% CI, -11.9 to -0.1) with largest difference in the intermediate illness severity group (DiD, -12.2%; 95% CI, -23.1 to -1.3). The increase in ED downgrade less than 6 hours was not statistically significant (DiD, 4.8%; 95% CI, -0.7 to 10.3%) except in the intermediate group (DiD, 8.8%; 95% CI, 0.2-17.4). CONCLUSIONS: The implementation of a novel ECCP was associated with a significant decrease in inhospital mortality among critically ill medical ED patients, with the greatest decrease observed in patients with intermediate severity of illness. Early ED downgrades also increased, but the difference was statistically significant only in the intermediate illness severity group.

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OBJECTIVES: Studies have found that prolonged boarding time for intensive care unit (ICU) patients in the emergency department (ED) is associated with higher in-hospital mortality. However, these studies introduced selection bias by excluding patients with ICU admission orders who were downgraded and never arrived in the ICU. Consequently, they may overestimate mortality in prolonged ED boarders. METHODS: This was a retrospective cohort study at a single center covering the period from August 14, 2015 to August 13, 2019. Adult ED patients with medical ICU admission orders and at least 6 hours of subsequent critical care in either the ED or the ICU were included. Patients were classified as having either prolonged (>6 hours) or non-prolonged (≤6 hours) ED boarding. Downgraded patients were identified, and mortality was compared, both including and excluding downgraded patients. RESULTS: Of 1862 patients, 612 (32.9%) had prolonged boarding; at 6 hours after ICU admission order entry, they were still in the ED. The remaining 1250 (67.1%) had non-prolonged boarding; at 6 hours after the ICU admission order entry, they were already in the ICU. In-hospital mortality in the non-prolonged boarding group was 18.9%. In the prolonged boarding group, 296 (48.4%) patients were downgraded in the ED and never arrived in the ICU. Including these ED downgrades, the mortality in the prolonged boarding group was 13.4% (risk difference -5.5%, 95% confidence interval [CI] -8.9% to -2.0%, P = 0.0031). When we excluded downgrades, the mortality in the prolonged boarding group increased to 17.4% (risk difference -1.5%, 95% CI -6.2% to 3.2%, P = 0.5720). The lower mortality in the prolonged group was attributable to lower severity of illness (mean emergency critical care SOFA [eccSOFA] difference: -0.8, 95% CI -1.1 to -0.4, P < 0.0001). CONCLUSIONS: Excluding critical care patients who were downgraded in the ED leads to selection bias and overestimation of mortality among prolonged ED boarders.

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BACKGROUND: The Mortality Probability Model (MPM) is used in research and quality improvement to adjust for severity of illness and can also inform triage decisions. However, a limitation for its automated use or application is that it includes the variable "intracranial mass effect" (IME), which requires human engagement with the electronic health record (EHR). We developed and tested a natural language processing (NLP) algorithm to identify IME from CT head reports. METHODS: We obtained initial CT head reports from adult patients who were admitted to the ICU from our ED between 10/2013 and 9/2016. Each head CT head report was labeled yes/no IME by at least two of five independent labelers. The reports were then randomly divided 80/20 into training and test sets. All reports were preprocessed to remove linguistic and style variability, and a dictionary was created to map similar common terms. We tested three vectorization strategies: Term Frequency-Inverse Document frequency (TF-IDF), Word2Vec, and Universal Sentence Encoder to convert the report text to a numerical vector. This vector served as the input to a classification-tree-based ensemble machine learning algorithm (XGBoost). After training, model performance was assessed in the test set using the area under the receiver operating characteristic curve (AUROC). We also divided the continuous range of scores into positive/inconclusive/negative categories for IME. RESULTS: Of the 1202 CT reports in the training set, 308 (25.6%) reports were manually labeled as "yes" for IME. Of the 355 reports in the test set, 108 (30.4%) were labeled as "yes" for IME. The TF-IDF vectorization strategy as an input for the XGBoost model had the best AUROC:-- 0.9625 (95% CI 0.9443-0.9807). TF-IDF score categories were defined and had the following likelihood ratios: "positive" (TF-IDF score > 0.5) LR = 24.59; "inconclusive" (TF-IDF 0.05-0.5) LR = 0.99; and "negative" (TF-IDF < 0.05) LR = 0.05. 82% of reports were classified as either "positive" or "negative". In the test set, only 4 of 199 (2.0%) reports with a "negative" classification were false negatives and only 8 of 93 (8.6%) reports classified as "positive" were false positives. CONCLUSION: NLP can accurately identify IME from free-text reports of head CTs in approximately 80% of records, adequate to allow automatic calculation of MPM based on EHR data for many applications.

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STUDY HYPOTHESIS: We hypothesized that establishing a program of specialized emergency critical care (ECC) nurses in the ED would improve mortality of ICU patients boarding in the ED. METHODS: This was a retrospective before-after cohort study using electronic health record data at an academic medical center. We compared in-hospital mortality between the pre- and post-intervention periods and between non-prolonged (≤6 h) boarding time and prolonged (>6 h) boarding time. In-hospital mortality was stratified by illness severity (eccSOFA category) and adjusted using logistic regression. RESULTS: Severity-adjusted in-hospital mortality decreased from 12.8% pre-intervention to 12.3% post-intervention (-0.5% (95% CI, -3.1% to 2.1%), which was not statistically significant. This was despite a concurrent increase in ED and hospital crowding. The proportion of ECC patients downgraded to a lower level of care while still in the ED increased from 6.4% in the pre-intervention period to 17.0% in the post-intervention period. (+10.6%, 8.2% to 13.0%, p < 0.001). Severity-adjusted mortality was 12.8% in the non-prolonged group vs. 11.3% in the prolonged group (p = 0.331). CONCLUSIONS: During the post-intervention period, there was a significant increase in illness severity, hospital congestion, ED boarding time, and downgrades in the ED, but no significant change in mortality. These findings suggest that ECC nurses may improve the safety of boarding ICU patients in the ED. Longer ED boarding times were not associated with higher mortality in either the pre- or post-intervention periods.

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BACKGROUND: Boarding of ICU patients in the ED is increasing. Illness severity scores may help emergency physicians stratify risk to guide earlier transfer to the ICU and assess pre-ICU interventions by adjusting for baseline mortality risk. Most existing illness severity scores are based on data that is not available at the time of the hospital admission decision or cannot be extracted from the electronic health record (EHR). We adapted the SOFA score to create a new illness severity score (eccSOFA) that can be calculated at the time of ICU admission order entry in the ED using EHR data. We evaluated this score in a cohort of emergency critical care (ECC) patients at a single academic center over a period of 3 years. METHODS: This was a retrospective cohort study using EHR data to assess predictive accuracy of eccSOFA for estimating in-hospital mortality risk. The patient population included all adult patients who had a critical care admission order entered while in the ED of an academic medical center between 10/24/2013 and 9/30/2016. eccSOFA's discriminatory ability for in-hospital mortality was assessed using ROC curves. RESULTS: Of the 3912 patients whose in-hospital mortality risk was estimated, 2260 (57.8%) were in the low-risk group (scores 0-3), 1203 (30.8%) in the intermediate-risk group (scores 4-7), and 449 (11.5%) in the high-risk group (scores 8+). In-hospital mortality for the low-, intermediate, and high-risk groups was 4.2% (95%CI: 3.4-5.1), 15.5% (95% CI 13.5-17.6), and 37.9% (95% CI 33.4-42.3) respectively. The AUROC was 0.78 (95%CI: 0.75-0.80) for the integer score and 0.75 (95% CI: 0.72-0.77) for the categorical eccSOFA. CONCLUSIONS: As a predictor of in-hospital mortality, eccSOFA can be calculated based on variables that are commonly available at the time of critical care admission order entry in the ED and has discriminatory ability that is comparable to other commonly used illness severity scores. Future studies should assess the calibration of our absolute risk predictions.

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Critical care medicine (CCM) is central to emergency medicine (EM) resident education. We feel that the traditional lecture format is not the ideal way to teach EM critical care, which requires integration and prioritization of diagnostic workup and team-based resuscitation under time pressure. We describe a novel critical care education day where an interactive, practical, and multidisciplinary critical care educational experience was provided for EM residents using case-based small-group sessions and fast-paced simulation.

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