Understanding Response Shift in Acute Respiratory Distress Syndrome (ARDS) survivors

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Project Summary / Abstract Acute respiratory distress syndrome (ARDS) is a severe lung injury usually requiring mechanical ventilation in an intensive care unit (ICU). While the number of ICU patients requiring mechanical ventilation is steadily increasing, in-hospital mortality is declining, creating a growing population of ARDS survivors. Such survivorship comes at a high “cost,” with ARDS patients frequently experiencing new or worsening physical, cognitive, and/or mental health impairments that last for years after ARDS. NHLBI and professional societies identify improving quality of life for ARDS survivors as a key research priority. Some ARDS survivors may adapt to new impairments over time and report improving quality of life not explained by improvements in objective measures of their physical, cognitive, and mental health. This adaptation phenomenon is known as “response shift.” We hypothesize that ARDS survivors demonstrate widely varying degrees of response shift, and that patients' baseline characteristics prior to ARDS have important associations with the magnitude of response shift after ICU discharge. We also investigate how psychosocial factors, including trait anxiety, social support, resilience, and survivor expectations for functional recovery, impact quality of life during recovery as a first step toward designing and evaluating new interventions for ARDS survivors. This proposal will exploit a unique, pre-existing cohort of very well-characterized ARDS survivors from the NHLBI ARDS Network Long Term Outcome Study (ALTOS) containing hundreds of data elements per patient. Using modern data science methods, I will empirically evaluate the association between baseline patient characteristics and response shift between 6 and 12 months after ARDS [Aim 1], and identify the changes in specific aspects of physical, cognitive, and mental health, from among ~165 candidate measures, that best predict changes in quality of life [Aim 2]. To investigate how psychosocial factors impact quality of life, I will conduct a new, prospective, ICU cohort study of ARDS survivors to collect data for these novel analyses [Aim 3]. The results from these three Aims will generate new knowledge regarding empirically-derived, testable hypotheses about important determinants of quality of life, and provide essential data for designing future studies of interventions aimed at improving the quality of life of ARDS survivors. This career development award provides training in data science, patient outcomes-oriented clinical research, and machine learning statistical techniques for the applicant who is a PhD-trained epidemiologist without these skills. Immediate, short-term, and long-term career goals include: (1) publishing results of the proposed research, (2) successfully competing for R-level funding to explore whether addressing psychosocial issues, including setting appropriate patient expectations for recovery, is a potentially modifiable, low-cost intervention to improve ARDS survivor's quality of life, and (3) becoming an NHLBI-funded independent research scientist who conducts innovative and methodologically rigorous data science research on ARDS outcomes.