Canadian Journal of Emergency Medicine最新文献

Background: Acute heart failure is a serious condition commonly seen in the emergency department (ED). The HEARTRISK6 Scale has been recently developed to identify the risk of poor outcomes but has not been tested. We sought to describe the management and outcomes of ED patients with acute heart failure and to evaluate the potential impact of the HEARTRISK6 Scale.

Methods: We conducted a health records review of 300 consecutive acute heart failure patients presenting to two tertiary care EDs. Two evaluators abstracted clinical variables, ED management and treatment details, and patient outcomes using the electronic health records platform (EPIC) and attending physicians verified the data. The primary outcome measure was a short-term serious outcome, as shown in Results. In addition, the HEARTRISK6 score was calculated retrospectively.

Results: We included 300 patients with mean age of 78.5 years, 51.0% male, 56.3% arrival by ambulance, and 67.0% admitted to hospital. 25.3% experienced a short-term serious outcome 1) after admission (N = 201): non-invasive ventilation 14.9%, intubation 1.5%, major cardiac procedure 5.0%, myocardial infarction 2.0%, death 8.5%; 2) after ED discharge (N = 99): return to ED 21.2%, death 4.0%. Those initially admitted experienced a much higher proportion of serious outcomes compared to those discharged (29.9% vs. 16.2%). A HEARTRISK6 Scale cut-point score of ≥ 1 would have had a sensitivity of 91.0%, specificity 24.5%, and negative likelihood ratio 0.37 for short-term serious outcomes and suggested hospital admission for 80.7% of cases.

Conclusion: There was a large range of severity of illness of acute heart failure patients and a wide variety of treatments were administered in the ED. Both admitted and discharged patients experienced a high proportion of poor outcomes. The HEARTRISK6 Scale showed a high sensitivity for short-term serious outcomes but with the potential to increase hospital admissions. Further validation of the HEARTRISK6 Scale is required before routine clinical use.

Objective: To address an important care issue in Canada, we tested the association between paramedic system hospital offload and response time, while considering the impact of other system-level factors.

Methods: Data from Calgary, Alberta (2014-2017), included median offload (exposure) and response (outcome) time aggregated by hour, with covariates paramedic system episodes of care-dispatch and arrival of a response unit-and hospital transport arrivals (collectively called volume), time of day, and season. Analyses used linear regression and modified Poisson models.

Results: 301,105 EMS episodes of care over 26,193 1-h periods were included. For any given 1-h period, the median (IQR) across all episodes of care for offload time, response time, episodes of care, and hospital transport arrivals were 55.3 (45.7, 66.3) min, 8.6 (7.6, 9.8) min, 12 (8, 16) episodes, and 8 (5, 10) hospital arrivals, respectively. Multivariable modelling revealed a complex association differing over levels of exposure and covariates, requiring description using "light stress" and "heavy stress" system scenarios. The light scenario was defined as median offload of 30 min and volume < 10th percentile (six episodes and four hospital arrivals), in the summer, and the heavy scenario as median offload of 90 min and volume > 90th percentile (17 episodes and 13 hospital arrivals), in the winter. An increase is reported in minutes:seconds for median hourly response time between scenarios by time of day: 1:04-4:16 (0000-0559 h.), 0:42-2:05 (0600-1159 h.), 0:57-3:01 (1200-1759 h.), and 0:18-2:21 (1800-2359 h.).

Conclusions: Increasing offload is associated with increased response time; however the relationship is complex, with a greater impact on response time noted in select situations such as high volume in the winter. These observations illustrate the interdependence of paramedic, ED, and inpatient systems and provide high-yield targets for polices to mitigate the risk to community availability of paramedic resources at times of high offload delay/system stress.

Objective: Triage is the process of identifying patients with both the greatest clinical need and the greatest likelihood of benefit in the setting of limited clinical resources. The primary objective of this study was to assess the ability of formal mass casualty incident triage tools to identify patients requiring urgent lifesaving interventions.

Methods: Data from the Alberta Trauma Registry (ATR) was used to assess seven triage tools: START, JumpSTART, SALT, RAMP, MPTT, BCD and MITT. Clinical data captured in the ATR was used to determine which triage category each of the seven tools would have applied to each patient. These categorizations were compared to a reference standard definition based on the patients' need for specific urgent lifesaving interventions.

Results: Of the 9448 records that were captured 8652 were included in our analysis. The most sensitive triage tool was MPTT, which demonstrated a sensitivity of 0.76 (0.75, 0.78). Four of the seven triage tools evaluated had sensitivities below 0.45. JumpSTART had the lowest sensitivity and the highest under-triage rate for pediatric patients. All the triage tools evaluated had a moderate to high positive predictive value (> 0.67) for patients who had experienced penetrating trauma.

Conclusions: There was a wide range in the sensitivity of triage tools to identify patients requiring urgent lifesaving interventions. MPTT, BCD and MITT were the most sensitive triage tools assessed. All of the triage tools assessed should be employed with caution during mass casualty incidents as they may fail to identify a large proportion of patients requiring urgent lifesaving interventions.

Objective: Health economic evaluations are used in decision-making regarding resource allocation and it is imperative that they are completed with rigor. The primary objectives were to describe the characteristics and assess the quality of economic evaluations published in emergency medicine journals.

Methods: Two reviewers independently searched 19 emergency medicine-specific journals via Medline and Embase from inception until March 3, 2022. Quality assessment was completed using the Quality of Health Economic Studies (QHES) tool, and the primary outcome was the QHES score out of 100. Additionally, we identified factors that may contribute to higher-quality publications.

Results: 7260 unique articles yielded 48 economic evaluations that met inclusion criteria. Most studies were cost-utility analyses and of high quality, with a median QHES score of 84 (interquartile range, IQR: 72, 90). Studies based on mathematical models and those primarily designed as an economic evaluation were associated with higher quality scores. The most commonly missed QHES items were: (i) providing and justifying the perspective of the analysis, (ii) providing justification for the primary outcome, and (iii) selecting an outcome that was long enough to allow for relevant events to occur.

Conclusions: The majority of health economic evaluations in the emergency medicine literature are cost-utility analyses and are of high quality. Decision analytic models and studies primarily designed as economic analyses were positively correlated with higher quality. To improve study quality, future EM economic evaluations should justify the choice of the perspective of the analysis and the selection of the primary outcome.

Purpose: To characterize patients who left without being seen (LWBS) from a Canadian pediatric Emergency Department (ED) and create predictive models using machine learning to identify key attributes associated with LWBS.

Methods: We analyzed administrative ED data from April 1, 2017, to March 31, 2020, from IWK Health ED in Halifax, NS. Variables included: visit disposition; Canadian Triage Acuity Scale (CTAS); triage month, week, day, hour, minute, and day of the week; sex; age; postal code; access to primary care provider; visit payor; referral source; arrival by ambulance; main problem (ICD10); length of stay in minutes; driving distance in minutes; and ED patient load. The data were randomly divided into training (80%) and test datasets (20%). Five supervised machine learning binary classification algorithms were implemented to train models to predict LWBS patients. We balanced the dataset using Synthetic Minority Oversampling Technique (SMOTE) and used grid search for hyperparameter tuning of our models. Model evaluation was made using sensitivity and recall on the test dataset.

Results: The dataset included 101,266 ED visits where 2009 (2%) records were excluded and 5800 LWBS (5.7%). The highest-performing machine learning model with 16 patient attributes was XGBoost which was able to identify LWBS patients with 95% recall and 87% sensitivity. The most influential attributes in this model were ED patient load, triage hour, driving minutes from home address to ED, length of stay (minutes since triage), and age.

Conclusion: Our analysis showed that machine learning models can be used on administrative data to predict patients who LWBS in a Canadian pediatric ED. From 16 variables, we identified the five most influential model attributes. System-level interventions to improve patient flow have shown promise for reducing LWBS in some centres. Predicting patients likely to LWBS raises the possibility of individual patient-level interventions to mitigate LWBS.