Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning
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

TL;DR
This study uses machine learning to analyze blood pressure and heart rate management in type B aortic dissection patients in the emergency department.
Contribution
The study introduces a machine learning approach to identify predictors of successful ED management of type B aortic dissection.
Findings
Higher triage SBP and intravenous labetalol treatment predicted unmet SBP goals at ED departure.
Pain management with higher morphine equivalents and faster CT scans predicted met HR goals.
Many patients did not meet AHA guidelines for hemodynamic control before transfer.
Abstract
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. 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…
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Taxonomy
TopicsCardiac, Anesthesia and Surgical Outcomes · Hip and Femur Fractures · Cardiac Health and Mental Health
