UC Davis

Acute respiratory distress syndrome (ARDS) is a severe form of acute hypoxemic respiratory failure affecting 10% of patients admitted to the intensive care unit (ICU) globally. In-hospital mortality of 29.7%-42.9% has been reported across the spectrum of mild-severe ARDS, and one third of patients with initially mild ARDS will progress to moderate or severe ARDS. Over the last 20 years, multiple studies have reported improved outcomes for ARDS patients using ARDS-targeted therapies. However, ARDS remains persistently under-recognized and challenging to diagnose. Only one third of ICU providers correctly identify ARDS on the first day when diagnostic criteria are met, and less than two thirds ever recognize the diagnosis in the ICU. This under recognition of ARDS may prevent some patients from receiving lifesaving therapies necessary for treating the disease. Attempts to automate ARDS diagnosis using rule-based algorithms have seen limited success, and require subjective analysis of infrequently sampled patient data, like chest radiographs, which limit diagnosis automation, timeliness, and study reproducibility. To improve the current state of the art of ARDS detection technology, we intend to utilize objective and readily available ventilator waveform data (VWD) to improve the recognition of ARDS and develop next generation clinical decision support systems (CDSS) that can function as a pervasive monitoring system for mechanical ventilation management. For this task, we make use of a novel dataset consisting of only VWD from patients receiving mechanical ventilation. We analyze this data using both classical and deep machine learning models and show results that suggest that ARDS can be detected in absence of a chest scan or medical history. This finding highlights that VWD shows promise for use as a future digital biomarker of ARDS pathophysiology. This dissertation is broadly aimed at showing the research challenges we solved and software that we built to create our ML-based ARDS detection models. Our work encompasses multiple sub-studies including: 1) designing software for annotating large amounts of VWD for our ML datasets, 2) developing and training machine learning models to extract important metadata (such as ventilation mode) from VWD, 3) performing a comprehensive clinical validation for existing, but clinically unvalidated respiratory compliance estimation algorithms, 4) training an ML model for early recognition of ARDS using only VWD, 5) improving our initial ML model for ARDS recognition by applying deep learning, and 6) discussing how to integrate all our work into a comprehensive monitoring system for patient ventilation. While our work is specifically focused on ARDS detection, this dissertation broadly highlights the utility of applying modern machine learning tools to underutilized high-frequency physiologic waveform data as a surrogate for manually charted EHR data types in disease detection and early health warning. These warnings and alerts can then be continuously monitored and returned to clinicians at the point of care in the form of actionable advice and disease prognostication. Such improvements will enable pervasive clinical decision support systems that promise to reduce the cognitive burden on care providers, improve quality of care, reduce patient suffering, and reduce overall hospital mortality from deadly conditions such as ARDS.