In this project, we aim to improve the scientific understanding of patterns (phenotypes) of long-term functional outcomes after Acute Respiratory Distress Syndrome (ARDS). ARDS is a life-threatening lung injury that commonly requires mechanical ventilation in an intensive care unit (ICU). Many patients survive ARDS with substantial impairments across multiple domains, including physical functioning, cognitive status, and mental health. For many patients, survival after ARDS represents a new-onset or worsening chronic health condition often affecting multiple domains. Within the apparent heterogeneity, we hypothesize that there are distinct subgroups or phenotypes of ARDS survivors. Those phenotypes may have different underlying physiological abnormalities and causative mechanisms; patients with different phenotypes may require different therapies to improve their overall health status. In this project, we propose to discover and then predict such clinical phenotypes at six months after ARDS. Using novel adaptations of big data analytic techniques frequently applied in the field of statistical genetics, we will identify phenotypes of survivors' functional outcomes at 6 months after ARDS and then derive and validate a prediction model for such phenotypes based on clinical information routinely available within the first 3 days of ARDS onset. For this work, we will use the investigators' unique pre-existing dataset of the NHLBI ARDS Network Long Term Outcome Study (ALTOS), the largest and most comprehensive multi-center follow-up study of ARDS survivors. We anticipate that this research will assist with a) improving clinical trial design through prospective identification of relevant patient subgroups who may benefit most from a specific intervention, b) supporting development of personalized treatment strategies for specific patient subgroups, and c) improving understanding of causal mechanisms for long-term functional outcomes after ARDS. This project aims to make important advancement towards a research mandate, repeatedly emphasized by the NHLBI and critical care academic and professional societies, of improving our understanding of long-term functional outcomes after ARDS.
Acute Respiratory Distress Syndrome (ARDS) causes significant death and long-term disability in America. With an interdisciplinary team, we will use state-of-the-art statistical analysis techniques to make novel discoveries and develop prediction models for patterns of disabilities ('phenotypes') among ARDS survivors. Through this research, we will improve our understanding of what causes disability after ARDS and provide essential knowledge for improving the design and testing of personalized treatments designed to decrease disabilities after ARDS.
| Brown, Samuel M; Wilson, Emily; Presson, Angela P et al. (2017) Predictors of 6-month health utility outcomes in survivors of acute respiratory distress syndrome. Thorax 72:311-317 |
| Brown, Samuel M; Wilson, Emily L; Presson, Angela P et al. (2017) Understanding patient outcomes after acute respiratory distress syndrome: identifying subtypes of physical, cognitive and mental health outcomes. Thorax 72:1094-1103 |
| Brown, Samuel M; Duggal, Abhijit; Hou, Peter C et al. (2017) Nonlinear Imputation of PaO2/FIO2 From SpO2/FIO2 Among Mechanically Ventilated Patients in the ICU: A Prospective, Observational Study. Crit Care Med 45:1317-1324 |
| Tang, Jun; Baxter, Samantha; Menon, Arjun et al. (2016) Immune cell screening of a nanoparticle library improves atherosclerosis therapy. Proc Natl Acad Sci U S A 113:E6731-E6740 |
| Brown, Samuel M; Grissom, Colin K; Moss, Marc et al. (2016) Nonlinear Imputation of Pao2/Fio2 From Spo2/Fio2 Among Patients With Acute Respiratory Distress Syndrome. Chest 150:307-13 |
