This application addresses the broad Challenge Area (15) Translational Science and the specific Challenge Topic 15-LM-103: In silico hypothesis testing for biology and medicine. The objective of this project is to determine the feasibility of mathematical models in both optimizing clinical trial designs for, and predicting onset of, Acute Lung Injury. Acute Lung Injury (ALI) and its more severe form, acute respiratory distress syndrome (ARDS) are representative examples of important critical care ailments affecting 200,000 patients each year in the US. Subsequent progression to multiple organ failure carries a grave prognosis with high mortality, and long term morbidity affecting both patients and caregivers. Carefully designed clinical trials have confirmed the relationships between mechanical ventilators and fluid management and their effects on ALI, hence leading to improvements in supportive treatment. Unfortunately, feasibility, as well as ethical and financial barriers greatly limit the ability to conduct meaningful clinical trials in the critical care setting. Indeed, the impact of clinical trials to date has been marginal, and certainly not cost-effective. To circumvent the aforementioned difficulties related to ALI in critical care medicine, this unique collaborative proposal between expert critical care researchers and mathematicians will expand existing complementary mathematical models and assess their ability individually and in tandem to predict the onset of ALI, and enable in silico hypothesis testing. This novel modeling approach will assist in design and implementation of efficient clinical trials targeting prevention of this devastated disorder. Specifically, the investigators will combine: a) mechanistic model that elucidates the pathophysiology of ALI, b) a rule based model to capture clinicians'putative knowledge and expertise, and c) a probabilistic/data mining model derived from empirical evidence extracted from electronic medical records and clinical trial databases. The goal is to address the complexity of ALI at the systems'level by leveraging each individual model's particular strengths and characteristics. Hence, such an approach estimates a patient's physiologic status based on both quantitative measurements and qualitative clinical observations enabling the predicting of ALI and evaluating therapeutic options. The benefit of this methodology will be evaluated by comparing our model's prediction against the US Critical Illness and Injury Trials Group-Lung Injury Prediction Study.
The aim of the USCIITG-LIPS1 clinical study, slated to start in May of 2009, is to identify patients at high risk of ALI for future enrollment into preventive clinical trials. The results of this project will include an assessment of the feasibility of such an approach to improving clinical trial design, as well as predicting ALI/ARDS onset. Our vision is a validated methodology for predicting acute and devastating ICU illnesses - transitioning from today's evidence-based to tomorrow's model-based medicine.

Public Health Relevance

In the US, Acute Lung Injury (ALI) and its more severe form Acute Respiratory Distress Syndrome (ARDS) are a major public health problem afflicting 200,000 patients per year and accounting for an in-hospital mortality of 40% and 3.5 million hospital-days. Despite advancements in supportive care the burden of ALI remains high not only in terms of mortality and morbidity, but also long term decrease in quality of life, and enormous cost of both intensive care and rehabilitation. Very little has been done on the prevention of this devastating hospital complication, largely due to difficulties in timely identification of patients at high risk as well as ethical and financial barriers. The impact of clinical trials has been marginal, and certainly not cost-effective. Conventional clinical trials are indeed unlikely to provide solution for effective prevention of ALI and ARDS. This research will be conducted by a unique multidisciplinary team of clinical research and engineering experts, aiming to design a mathematical model of ALI development for future in silico hypothesis testing of different ALI prevention strategies, and better design of clinical trials. It will allow for improved patient outcome by early diagnosing ALI as well as by testing the effect of interventions without putting the patient at risk.

National Institute of Health (NIH)
National Library of Medicine (NLM)
NIH Challenge Grants and Partnerships Program (RC1)
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Special Emphasis Panel (ZRG1-SBIB-V (58))
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Sim, Hua-Chuan
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Mayo Clinic, Rochester
United States
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