Présilience

Resilience-based test to accurately predict functional recovery in older patients facing hip fracture surgery

Market Challenges
Hip fracture is a major health problem, especially in elderly populations, due to serious post-operative complications upon hip fracture surgery (HFS) including chronic pain, prolonged loss of autonomy and premature death.
With rising life expectancy, the number of elderly individuals is estimated to reach 6.26 million people by 2050 worldwilde. Hip fracture accounts for almost 1.4% of total healthcare expenses, mainly due to standard-of-care that is not harmonized across ER units and that does not fit to all groups of patients.
Therapeutic innovation for HFS patients is impaired by the lack of tests allowing to stratify older patients according to their resilience status, a highly variable component.

Innovative solution
Researchers from Sorbonne Université have developed a test to predict the recovery of walking ability in older patients facing hip fracture surgery.
This signature is made of a small number of gene-expression and physiological markers, enabling to distinguish patients that successfully recover from HFS from those with a poorer prognosis.
This predictive solution allows to better manage HF by adapting care according to the stress resilience status of the patients.
Additionally, this solution allows to reduce inter-individual variability in clinical trials aiming to test treatments for stimulating cellular compensation while reducing cellular senescence in aging key systems.

Development status
The signature was obtained from a pilot study performed in the high-quality cohort UPOG (supported by ARS), involving 40 older patients facing HFS in Paris. Standardized clinical data were collected upon arrival to the ER, and RNA-seq data were obtained from blood samples collected right before surgery.
Machine learning using the Kem® method identified a marker signature that predicts functional recovery at high accuracy.
SATT Lutech is now funding a prospective study to identify the final predictive signature from the analysis of 250 UPOG patients