Abstract 448: Predicting Blood Pressure Response to Fluid Bolus Therapy Using Neural Networks with Clinical Interpretability

Abstract

Background: Fluid bolus therapy (FBT), the rapid infusion of fluid, has been recommended as the primary-line treatment for acute hypotensive episode (AHE) that occurs in about 41% of patients in ICU. However, previous studies have reported that approximately one-third of the acute hypotensive patients do not successfully respond to FBT treatment. Avoiding the administration of FBT that will not successfully resolve AHE might prevent an inappropriate increase of the total fluid volume administered to ICU patients, potentially reducing their risk for severe organ dysfunction and increased mortality. Methods: Our study utilized regression models and attention-based recurrent neural network (RNN) algorithms and two large-scale information system databases, the multi-clinical MIMIC-ICU one and the multi-center Philips eICU CRD one, to predict the successful response to FBT among hypotensive patients in ICUs. We investigated both time-aggregated modeling and time-series modeling using RNN with the attention mechanism (AM) for clinical interpretability. The successful FBT is defined by intensive care experts as the presence of the maximum mean atrial pressure (MAP) > 1.15 * average (MAP) at least once, where maximum(MAP) is the maximal MAP from the FBT starting time to two hours after FBT, and average (MAP) is the average MAP from 30 minutes before FBT until 10 minutes after FBT. Results: The stacked RNN with AM yielded the highest accuracy of 0.852 and area under the curve (AUC) value of 0.925 when trained and tested on the MIMIC-ICU dataset. The top features learned from regression include the patient's respiratory rate, diastolic pressure, temperature, and bicarbonate and base excess levels in blood. Preliminary results from training and testing the RNN on the Philips eICU-CRD database yielded an accuracy of 0.812 and AUC value of 0.769. We were also able to identify timesteps close to the time of FBT administration as clinically meaningful using the RNN models with AM. Conclusion: This is the first study that utilizes machine learning for identifying hypotensive patients in ICUs who will have sufficient blood pressure recovery after FBT. Utilizing AM and identifying the top features learned also provided clinical interpretability to the models we used.