Forecasting Aortic Pressure Cross-Cohort with Deep Sequence Models

Alan Li, Zihao Zhou, Elise Jortberg, Rose Yu

Abstract: Mean aortic pressure (MAP) is a primary measurement for monitoring blood and O2 delivery to major organs. Prolonged periods of hypotension, low MAP, lead to low tissue perfusion and subsequent end organ damage. Patients on mechanical circulatory support (MCS) devices, such as the Impella CP, are managed to maintain sufficient MAPfor end-organ perfusion. Forecasting MAP is important for early warning of clinically concerning events, including hypotension and instability as well as device weaning. Patients presenting with cardiogenic shock as a result of acute myocardial infarction (AMI/CGS) have increased hemodynamic instability when compared to patients undergoing high-risk percutaneous coronary interventions (HRPCI). Existing deep sequence models for forecasting often focus on the same patient cohort and cannot generalize across cohorts. In this paper, we examine how deep sequence models respond to the distribution shift of the MAP across the MCS patient cohorts during forecasting. We propose conditional RNN, a deep sequence model that learns to adapt to a different cohort by conditioning on time-invariant cohort features. Our proposed model improves the forecasting performance, achieving a 5.2 mmHg- 6.1 mmHg RMSEfor cross-cohort patients.