Gender Disparities in Cardiac Catheterization Rates Among Emergency Department Patients With Chest Pain

Mechanical circulatory device utilization in cardiac arrest: Racial and gender disparities and impact on mortality

The objectives of this retrospective study include identifying the utilization trend of mechanical circulatory devices (MCD) such as Intra-Aortic Balloon Pump (IABP), Impella and Extracorporeal Membrane Oxygenation (ECMO) in admissions with cardiac arrest, determining whether racial or gender disparities exist in their usage, and discerning if their use is associated with a reduction in mortality. By leveraging the National Inpatient Sample, we identified 229,180 weighted adult cardiac arrest admissions between October 1, 2015 and December 31, 2018. MCD were used in 6005 admissions (2.6%). IABP had the highest utilization, representing 77.8% of all MCDs, followed by Impella at 24.8%. The utilization of IABP decreased from 90.6% to 71.6%, while the use of Impella increased from 13.5% to 29.8% in this study period; both trends were statistically significant. MCD use was found to be lower in women compared to men (1.4% vs 3.6, P < 0.001) and in the Black population compared to White (1.5% vs 2.8%, P < 0.001). There was no difference in MCD utilization between Hispanic and the White cohorts. In-hospital mortality was lower in admissions associated with MCD (31.4% vs 45.9%, P < 0.001). ECMO was associated with the lowest mortality rate at 14.3%, followed by IABP at 28.1%. The use of Impella and combination therapy were not associated with a significant decrease in mortality. In conclusion, MCD use may decrease mortality in cardiac arrest, however their utilization appears to be lower in African Americans and in women.

Machine Learning Methods for Predicting Patient-Level Emergency Department Workload

BACKGROUND

Work Relative Value Units (wRVUs) are a component of many compensation models, and a proxy for the effort required to care for a patient. Accurate prediction of wRVUs generated per patient at triage could facilitate real-time load balancing between physicians and provide many practical operational and clinical benefits.

OBJECTIVE

We examined whether deep-learning approaches could predict the wRVUs generated by a patient's visit using data commonly available at triage.

METHODS

Adult patients presenting to an urban, academic emergency department from July 1, 2016-March 1, 2020 were included. Deidentified triage information included structured data (age, sex, vital signs, Emergency Severity Index score, language, race, standardized chief complaint) and unstructured data (free-text chief complaint) with wRVUs as outcome. Five models were examined: average wRVUs per chief complaint, linear regression, neural network and gradient-boosted tree on structured data, and neural network on unstructured textual data. Models were evaluated using mean absolute error.

RESULTS

We analyzed 204,064 visits between July 1, 2016 and March 1, 2020. The median wRVUs were 3.80 (interquartile range 2.56-4.21), with significant effects of age, gender, and race. Models demonstrated lower error as complexity increased. Predictions using averages from chief complaints alone demonstrated a mean error of 2.17 predicted wRVUs per visit (95% confidence interval [CI] 2.07-2.27), the linear regression model: 1.00 wRVUs (95% CI 0.97-1.04), gradient-boosted tree: 0.85 wRVUs (95% CI 0.84-0.86), neural network with structured data: 0.86 wRVUs (95% CI 0.85-0.87), and neural network with unstructured data: 0.78 wRVUs (95% CI 0.76-0.80).

CONCLUSIONS

Chief complaints are a poor predictor of the effort needed to evaluate a patient; however, deep-learning techniques show promise. These algorithms have the potential to provide many practical applications, including balancing workloads and compensation between emergency physicians, quantify crowding and mobilizing resources, and reducing bias in the triage process.

Disparities in Emergency Medical Services Care Delivery in the United States: A Scoping Review

BACKGROUND

Emergency medical services (EMS) often serve as the first medical contact for ill or injured patients, representing a critical access point to the health care delivery continuum. While a growing body of literature suggests inequities in care within hospitals and emergency departments, limited research has comprehensively explored disparities related to patient demographic characteristics in prehospital care.

OBJECTIVE

We aimed to summarize the existing literature on disparities in prehospital care delivery for patients identifying as members of an underrepresented race, ethnicity, sex, gender, or sexual orientation group.

METHODS

We conducted a scoping review of peer-reviewed and non-peer-reviewed (gray) literature. We searched PubMed, CINAHL, Web of Science, Proquest Dissertations, Scopus, Google, and professional websites for studies set in the U.S. between 1960 and 2021. Each abstract and full-text article was screened by two reviewers. Studies written in English that addressed the underrepresented groups of interest and investigated EMS-related encounters were included. Studies were excluded if a disparity was noted incidentally but was not a stated objective or discussed. Data extraction was conducted using a standardized electronic form. Results were summarized qualitatively using an inductive approach.

RESULTS

One hundred forty-five full-text articles from the peer-reviewed literature and two articles from the gray literature met inclusion criteria: 25 studies investigated sex/gender, 61 studies investigated race/ethnicity, and 58 studies investigated both. One study investigated sexual orientation. The most common health conditions evaluated were out-of-hospital cardiac arrest (n = 50), acute coronary syndrome (n = 36), and stroke (n = 31). The phases of EMS care investigated included access (n = 55), pre-arrival care (n = 46), diagnosis/treatment (n = 42), and response/transport (n = 40), with several studies covering multiple phases. Disparities were identified related to all phases of EMS care for underrepresented groups, including symptom recognition, pain management, and stroke identification. The gray literature identified public perceptions of EMS clinicians' cultural competency and the ability to appropriately care for transgender patients in the prehospital setting.

CONCLUSIONS

Existing research highlights health disparities in EMS care delivery throughout multiple health outcomes and phases of EMS care. Future research is needed to identify structured mechanisms to eliminate disparities, address clinician bias, and provide high-quality equitable care for all patient populations.

Using HEART2 score to risk stratify chest pain patients in the Emergency Department: an observational study

BACKGROUND

A significant number of chest pain patients had previous cardiac imaging tests (CIT) performed before being presented to the Emergency Department (ED). The HEART (history, electrocardiogram, age, risk factors, and troponin) score has been used to risk-stratify chest pain patients in the ED, but not particularly for patients with CIT performed. We aim to modify the current HEART score with the addition of most recent CIT findings (referred to as HEART2 score), to predict a 30-day major adverse cardiac event (MACE) among ED chest pain patients, compare the performance accuracy of using HEART versus HEART2 score for 30-day MACE outcome predictions, and further determine the value of HEART2 in a subset group of ED chest pain patients (i.e., ones with previous CIT).

METHODS

This is a single-center observational study. We included chest pain patients with HEART scores calculated during their index ED visits. A modified HEART2 score was developed with the addition of CIT findings as one of the HEART2 components. Patients were divided into three groups, including low (≤ 3), moderate (4-6), and high-risk HEART/HEART2 scores (≥ 7). MACE occurrence of a patient with different risks of HEART and HEART2 scores and overall performance accuracy of HEART versus HEART2 score predicting MACE outcomes were compared.

RESULTS

We included a total of 9419 chest pain patients at ED, among which one out of five patients (1874/9419) had previous CIT performed. Fewer (38.2%) of such patients had low-risk HEART scores in comparison to 55.5% of low-risk HEART2 scores (p < 0.001). The MACE outcomes were similar in low-risk HEART patients compared with low-risk HEART2 patients (2.2% versus 3.1%, p = 0.3021). The overall performance accuracy of using the HEART2 score to stratify chest pain patients with previous CIT was better than using the HEART score's (AUC 0.74 versus 0.71, p = 0.0082).

CONCLUSIONS

Using the HEART2 score might be suitable to stratify low-to-moderate risk chest pain patients at ED with a similar 30-days MACE occurrence compared to the HEART score. More importantly, with the use of similar low-risk criteria (HEART2 ≤ 3), over 45% more chest pain patients with previous CIT performed could be discharged directly from ED.