A Hypotension Surveillance and Prediction System for Critical Care

Abstract

The sudden fall of blood pressure (hypotension) is a common and serious complication in medical care. In critical care patients, hypotension may induce severe or even lethal events. Moreover, recent studies report an increase of mortality in critical care patients. By predicting hypotension (HT) in advance, medical staff can take action to minimize its effects or even avoid its occurrence. Typically, most medical systems have focused on monitoring current patient status, rather than predicting a patient’s future status. Therefore, predicting HT episodes in advance remains a challenge. In this chapter, the authors present a solution for continuous monitoring and prediction of HT episodes, using Heart Rate (HR) and mean Blood Pressure (BP) biosignals. They propose an architecture for a HT Predictor (HTP) Tool, presenting a set of applications and a real-time database capable of continuously storing, and real-time monitoring all patients’ historical HR and BP data. The tool is able to efficiently alert both probable and detected occurrences of HT episodes for each patient for the following 60 minutes. Additionally, the system promotes medical staff mobility, by taking advantage of mobile personal devices such as mobile phones and PDAs, optimizing human resources. Finally, an experimental evaluation on real-life data from the well-known Physionet database shows the efficiency of the tool, outperforming the winning proposal of the Physionet 2009 Challenge.