Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Early prediction of adverse clinical events and optimal intervention in ICUs
Abstract: Personalized healthcare is a rapidly evolving research area with tremendous potential for optimizing patient care strategies and improving patient outcomes. Traditionally, clinical decision making relies on assessment and intervention based on the collective experience of physicians. Using big-data analytics techniques, we can now harness data-driven models to enable early prediction of patients at risk of adverse clinical events. These predictive models can provide timely analytical information to physicians facilitating early therapeutic intervention and efficient management of patients in intensive care units (ICUs).
In addition to early prediction, it is equally important to optimize intervention strategies for critically ill patients. One such urgent need is to optimally oxygenate COVID-19 patients diagnosed with acute respiratory distress syndrome (ARDS). Moderate to severe ARDS patients generally require mechanical ventilation to improve oxygen saturation and to reduce the risk of organ failure and death. The most common ventilator settings across all modes of mechanical ventilation are positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2). Increasing either of these settings is expected to increase oxygen saturation. However, prolonged ventilation of patients with high PEEP and FiO2 significantly increases the risk of ventilator associated lung injury. Therefore, an optimal strategy is required to improve patient outcomes.
This thesis presents two overarching aims: (1) early prediction of adverse events and (2) optimal intervention for mechanically ventilated patients. In contrast to fixed lead-time prediction models in prior work, our methodology proposes a new framework which hypothesizes the presence of a time-varying pre-event physiologic state that differentiates the target patients from the control group. We also present a unique approach to patient risk-stratification using unsupervised clustering technique that could enable identification of a high-risk group among all positive predicted cases with a positive predictive value of more than 93% when applied to multiple organ dysfunction prediction.
In the second aim, we propose a novel application of data-driven linear parameter varying systems to capture time-varying dynamics of oxygen saturation in response to ventilator settings with a changing physiological state of a patient and its comparison with linear time invariant models. Most prior studies on closed loop ventilator control have used stepwise, rule-based procedures, fuzzy logic, and a combination of rule-based methods and proportional integral derivative (PID) controller for closed loop control of FiO2. Other studies have worked on control strategies based on ventilator measured variables and on various mathematical lung models. In contrast we design optimal closed-loop ventilator strategies that are model based. A simulation of optimal ventilation settings for maintaining desired oxygen saturation using feedback control of LPV systems is presented.
Raimond L. Winslow, Department of Biomedical Engineering
Sridevi V. Sarma, Department of Biomedical Engineering
Enrique Mallada, Department of Electrical Engineering
Melania M. Bembea, Department of Anesthesiology and Critical Care Medicine