Project Summary
A complex disease often affects the functions of multiple organs of the human body. In intensive care, the recovery of major organ functions is essential to patient’s overall recovery averting death. As part of the Glue Grant Consortium, the Inflammation and the Host Response to Injury, we have collected functional trajectories of six major organs of 1,875 severe trauma patients enrolled at one of the seven U.S. Level I trauma centers between 2003 to 2009. We analyzed the recovery events and death with a newly developed multivariate survival analysis method. We found that, after severe trauma, the hematologic and hepatic systems recovered first, followed by the respiratory system, then the neurological and renal systems, and finally the cardiovascular system. In addition, we found that the delayed recovery of complicated patients was mainly due to the delayed recovery of the respiratory system, and the recovery of the neurological, renal and cardiovascular systems strongly depended on the respiratory recovery. Importantly, the non-recovery of the hematologic and respiratory systems significantly increased the risk of death. The functional states of these two organs were incorporated into an early predictor of patient outcomes of recovery together with the immunological states measured by gene expression of white blood cells. This early predictor successfully predicts patient outcome without complications. The overall results shed lights on prospective medical practices of prioritized treatments and monitoring on organ functions.
Significance
Integrative analysis of genomic and systems-wide clinical information can significantly accelerate disease studies. As a continuation of our investigation of the genomic response to injury in trauma patients (Xiao et al, JEM, 2011), we further integrated the clinical information of multiple organ systems. This integrative approach is applicable in many other disease studies.
Approach
Accomplishments
Future Objectives
• More integration toward an in silico disease model that explains molecular-phenotype connections.
• Computational method developments for efficient computing, data integration, and knowledge inference.
• Generalization of the approach to other complex diseases.
Reference
Seok J, Tian L, Wong WH. Density estimation on multivariate censored data with optional Pólya tree. Biostatistics. 2014 Jan;15(1):182-95.
Personnel
Junhee Seok1,2,3, Lu Tian2, Ronald Maier3, Ronald Davis1,3, Ron Tompkins3, Wing Wong1,2,3, andWenzhong Xiao1,3
1Stanford Genome Technology Center, 2Dept. of Statistics, Stanford, 3Glue Grant Consortium