Research in the Sinha Lab focuses on redefining the way critically ill patients are identified and treated through the use of big data analytics and cutting-edge biological measurements.
In critical care medicine, sepsis and acute respiratory distress syndrome (ARDS) are widespread conditions with high death and complication rates. Unfortunately, most clinical trials for potential treatments for these conditions have failed. One possible reason for these repeated failures is the significant biological heterogeneity within sepsis and ARDS patients, as these conditions are currently defined by broad, non-specific symptoms rather than specific underlying causes.
Researchers have identified two unique biological sub-phenotypes of these conditions, called ‘Hyper-inflammatory’ and ‘Hypo-inflammatory,’ which appear to have different outcomes and responses to treatment.
Our lab aims to build on these findings by using advanced biological, genomic, and technological methods to quickly classify patients with sepsis or ARDS into these sub-phenotypes as soon as they are diagnosed. This will allow for more personalized and effective treatments tailored to the specific sub-phenotype of the condition.
To achieve this, we are focused on two main objectives:
- Developing a large group of septic patients to study the changes in their condition over time to better understand how these sub-phenotypes evolve (Project PRECCISE).
- Implementing machine learning models to assign these sub-phenotypes at the bedside and validating these models with real-time patient data.