1
|
Gilani A, Maknojia A, Mufty M, Patel S, Grines CL, Ghatak A. Mechanical circulatory device utilization in cardiac arrest: Racial and gender disparities and impact on mortality. Int J Cardiol 2023; 371:460-464. [PMID: 36087630 DOI: 10.1016/j.ijcard.2022.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/16/2022] [Accepted: 09/03/2022] [Indexed: 01/08/2023]
Abstract
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.
Collapse
Affiliation(s)
- Aamir Gilani
- Internal Medicine Chief Resident; Northside Hospital Gwinnett, 1000 Medical Center Blvd, Lawrenceville, GA 30046, USA.
| | - Arish Maknojia
- Internal Medicine Resident; Northside Hospital Gwinnett, 1000 Medical Center Blvd, Lawrenceville, GA 30046, USA
| | - Muhammad Mufty
- Internal Medicine Resident; Northside Hospital Gwinnett, 1000 Medical Center Blvd, Lawrenceville, GA 30046, USA
| | - Shaan Patel
- Internal Medicine Resident; Northside Hospital Gwinnett, 1000 Medical Center Blvd, Lawrenceville, GA 30046, USA
| | - Cindy L Grines
- Northside Hospital Cardiovascular Institute, 575 Professional Dr #400, Lawrenceville, GA 30046, USA. Past president of the Society for Cardiovascular Angiography and Interventions
| | - Abhijit Ghatak
- Cardiovascular Clinic of North Georgia, 1475 Jesse Jewell Pkwy NE #300, Gainesville, GA 30501, USA
| |
Collapse
|
2
|
Joseph JW, Leventhal EL, Grossestreuer AV, Chen PC, White BA, Nathanson LA, Elhadad N, Sanchez LD. Machine Learning Methods for Predicting Patient-Level Emergency Department Workload. J Emerg Med 2023; 64:83-92. [PMID: 36450614 DOI: 10.1016/j.jemermed.2022.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/30/2022]
Abstract
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.
Collapse
Affiliation(s)
- Joshua W Joseph
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Evan L Leventhal
- Harvard Medical School, Boston, Massachusetts; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Anne V Grossestreuer
- Harvard Medical School, Boston, Massachusetts; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Paul C Chen
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Benjamin A White
- Harvard Medical School, Boston, Massachusetts; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Larry A Nathanson
- Harvard Medical School, Boston, Massachusetts; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Noémie Elhadad
- Departments of Biomedical Informatics and Computer Science, Columbia University, New York, New York
| | - Leon D Sanchez
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
3
|
Farcas AM, Joiner AP, Rudman JS, Ramesh K, Torres G, Crowe RP, Curtis T, Tripp R, Bowers K, von Isenburg M, Logan R, Coaxum L, Salazar G, Lozano M, Page D, Haamid A. Disparities in Emergency Medical Services Care Delivery in the United States: A Scoping Review. PREHOSP EMERG CARE 2022; 27:1058-1071. [PMID: 36369725 DOI: 10.1080/10903127.2022.2142344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 10/25/2022] [Indexed: 11/14/2022]
Abstract
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.
Collapse
Affiliation(s)
- Andra M Farcas
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, Colorado
| | - Anjni P Joiner
- Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Jordan S Rudman
- Harvard Affiliated Emergency Medicine Residency, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Karthik Ramesh
- School of Medicine, University of California San Diego, San Diego, California
| | | | | | | | - Rickquel Tripp
- Department of Emergency Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Karen Bowers
- Atlanta Fire Rescue Department; Department of Emergency Medicine, University of Tennessee-Chattanooga, Chattanooga, Tennessee
| | - Megan von Isenburg
- Duke University Medical Center Library, Duke University, Durham, North Carolina
| | - Robert Logan
- San Diego Fire - Rescue Department, San Diego, California
| | - Lauren Coaxum
- Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina
| | | | - Michael Lozano
- Division of Emergency Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida
| | - David Page
- Department of Emergency Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Ameera Haamid
- Section of Emergency Medicine, University of Chicago School of Medicine, Chicago, Illinois
| |
Collapse
|
4
|
Schrader CD, Kumar D, Zhou Y, Meyering S, Saltarelli N, Alanis N, Iloma C, Smiley R, Wang H. Using HEART2 score to risk stratify chest pain patients in the Emergency Department: an observational study. BMC Cardiovasc Disord 2022; 22:79. [PMID: 35246065 PMCID: PMC8896146 DOI: 10.1186/s12872-022-02528-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/25/2022] [Indexed: 11/10/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Chet D Schrader
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Darren Kumar
- Department of Cardiology, JPS Health Network, 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Yuan Zhou
- Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, 701 S. Nedderman Dr., Arlington, TX, 76019, USA
| | - Stefan Meyering
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Nicholas Saltarelli
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Naomi Alanis
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Chukwuagozie Iloma
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Rebecca Smiley
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA
| | - Hao Wang
- Department of Emergency Medicine, John Peter Smith Health Network (JPS Health Network), 1500 S. Main St., Fort Worth, TX, 76104, USA.
| |
Collapse
|