Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning
Background: Acute aortic dissections (AAD) have a high morbidity and mortality rate. Treatment for type B aortic dissection includes strict systolic blood pressure (SBP) and heart rate (HR) control per the American Heart Association (AHA) guidelines. However, predictors of successful emergency depar...
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eScholarship Publishing, University of California
2025-05-01
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| Series: | Western Journal of Emergency Medicine |
| Online Access: | https://escholarship.org/uc/item/4b04t42g |
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| author | Nelson Chen Jessica V. Downing Jacob Epstein Samira Mudd Angie Chan Sneha Kuppireddy Roya Tehrani Isha Vashee Emily Hart Emily Esposito Rose Chasm Quincy K. Tran |
| author_facet | Nelson Chen Jessica V. Downing Jacob Epstein Samira Mudd Angie Chan Sneha Kuppireddy Roya Tehrani Isha Vashee Emily Hart Emily Esposito Rose Chasm Quincy K. Tran |
| author_sort | Nelson Chen |
| collection | DOAJ |
| description | Background: Acute aortic dissections (AAD) have a high morbidity and mortality rate. Treatment for type B aortic dissection includes strict systolic blood pressure (SBP) and heart rate (HR) control per the American Heart Association (AHA) guidelines. However, predictors of successful emergency department (ED) management of SBP have not been well studied. Methods: We retrospectively analyzed the records of adult patients presenting to any regional ED with type B AAD between 2017–2020 with initial SBP >120 mmHg and HR >60 beats per minute (bpm) and were subsequently transferred to our quaternary center. Primary outcome was SBP <120 mmHg based on both the 2010 and 2022 AHA guidelines and HR <60 bpm (based on the 2010 guideline), or HR <80 (2022 guideline). We used random forest (RF) algorithms, a machine-learning tool that uses clusters of decision trees to predict a categorical outcome, to identify predictors of achieving HR and SBP goals prior to ED departure, defined as the time point at which patients left the referring ED to come to our institution. Results: The analysis included 134 patients. At the time of ED departure, 26 (19%) had SBP <120 mmHg, 96 (67%) received anti-impulse therapy, and 40 (28%) received beta-blocker or vasodilator infusions specifically. The RF algorithm identified higher triage SBP and treatment with intravenous labetalol as the top predictors for SBP >120 mmHg at ED departure, contrary to AHA guidelines. Pain management with higher total morphine equivalent unit, as well as shorter time to computed tomography as predictors for HR <60 bpm and <80 bpm, were in concert with AHA guidelines. Conclusion: Many patients with type B AAD did not achieve hemodynamic parameters in line with 2010 or 2022 AHA guidelines while being in the ED prior to transferring to a quaternary care center for further evaluation and management. Patients with higher heart rate and systolic blood pressure on ED arrival were less likely to achieve goals at the time of departure from the referring EDs. Those receiving more pain medications prior to transfer were more likely to meet certain AHA goals. |
| format | Article |
| id | doaj-art-64bb0e3bbfe2425996800f26542c55d2 |
| institution | OA Journals |
| issn | 1936-900X 1936-9018 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | eScholarship Publishing, University of California |
| record_format | Article |
| series | Western Journal of Emergency Medicine |
| spelling | doaj-art-64bb0e3bbfe2425996800f26542c55d22025-08-20T02:34:59ZengeScholarship Publishing, University of CaliforniaWestern Journal of Emergency Medicine1936-900X1936-90182025-05-0126367468410.5811/westjem.25005wjem-26-674Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine LearningNelson Chen0Jessica V. Downing1Jacob Epstein2Samira Mudd3Angie Chan4Sneha Kuppireddy5Roya Tehrani6Isha Vashee7Emily Hart8Emily Esposito9Rose Chasm10Quincy K. Tran11University of Maryland School of Medicine, Baltimore, MarylandUniversity of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandUniversity of Maryland Medical Center, Critical Care Resuscitation Unit, Baltimore, MarylandUniversity of Maryland School of Medicine, Program in Trauma, The R. Adam Cowley Shock Trauma Center, Baltimore, MarylandUniversity of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandResearch Associate Program in Emergency Medicine and Critical Care, University of Maryland School of Medicine, Department of Emergency Medicine, Baltimore, MarylandBackground: Acute aortic dissections (AAD) have a high morbidity and mortality rate. Treatment for type B aortic dissection includes strict systolic blood pressure (SBP) and heart rate (HR) control per the American Heart Association (AHA) guidelines. However, predictors of successful emergency department (ED) management of SBP have not been well studied. Methods: We retrospectively analyzed the records of adult patients presenting to any regional ED with type B AAD between 2017–2020 with initial SBP >120 mmHg and HR >60 beats per minute (bpm) and were subsequently transferred to our quaternary center. Primary outcome was SBP <120 mmHg based on both the 2010 and 2022 AHA guidelines and HR <60 bpm (based on the 2010 guideline), or HR <80 (2022 guideline). We used random forest (RF) algorithms, a machine-learning tool that uses clusters of decision trees to predict a categorical outcome, to identify predictors of achieving HR and SBP goals prior to ED departure, defined as the time point at which patients left the referring ED to come to our institution. Results: The analysis included 134 patients. At the time of ED departure, 26 (19%) had SBP <120 mmHg, 96 (67%) received anti-impulse therapy, and 40 (28%) received beta-blocker or vasodilator infusions specifically. The RF algorithm identified higher triage SBP and treatment with intravenous labetalol as the top predictors for SBP >120 mmHg at ED departure, contrary to AHA guidelines. Pain management with higher total morphine equivalent unit, as well as shorter time to computed tomography as predictors for HR <60 bpm and <80 bpm, were in concert with AHA guidelines. Conclusion: Many patients with type B AAD did not achieve hemodynamic parameters in line with 2010 or 2022 AHA guidelines while being in the ED prior to transferring to a quaternary care center for further evaluation and management. Patients with higher heart rate and systolic blood pressure on ED arrival were less likely to achieve goals at the time of departure from the referring EDs. Those receiving more pain medications prior to transfer were more likely to meet certain AHA goals.https://escholarship.org/uc/item/4b04t42g |
| spellingShingle | Nelson Chen Jessica V. Downing Jacob Epstein Samira Mudd Angie Chan Sneha Kuppireddy Roya Tehrani Isha Vashee Emily Hart Emily Esposito Rose Chasm Quincy K. Tran Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning Western Journal of Emergency Medicine |
| title | Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning |
| title_full | Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning |
| title_fullStr | Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning |
| title_full_unstemmed | Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning |
| title_short | Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning |
| title_sort | emergency department blood pressure management in type b aortic dissection an analysis with machine learning |
| url | https://escholarship.org/uc/item/4b04t42g |
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