PT  - JOURNAL ARTICLE
AU  - Sleiman Haddad
AU  - Javier Pizones
AU  - Riccardo Raganato
AU  - Michael M. Safaee
AU  - Justin K. Scheer
AU  - Ferran Pellisé
AU  - Christopher P. Ames
TI  - Future Data Points to Implement in Adult Spinal Deformity Assessment for Artificial Intelligence Modeling Prediction: The Importance of the Biological Dimension
AID  - 10.14444/8502
DP  - 2023 Jun 01
TA  - International Journal of Spine Surgery
PG  - S34--S44
VI  - 17
IP  - S1
4099  - http://ijssurgery.com//content/17/S1/S34.short
4100  - http://ijssurgery.com//content/17/S1/S34.full
SO  - Int J Spine Surg2023 Jun 01; 17
AB  - Adult spinal deformity (ASD) surgery is still associated with high surgical risks. Machine learning algorithms applied to multicenter databases have been created to predict outcomes and complications, optimize patient selection, and improve overall results. However, the multiple data points currently used to create these models allow for 70% of accuracy in prediction. We need to find new variables that can capture the spectrum of probability that is escaping from our control. These proposed variables are based on patients’ biological dimensions, such as frailty, sarcopenia, muscle and bone (tissue) sampling, serological assessment of cellular senescence, and circulating biomarkers that can measure epigenetics, inflammaging, and -omics. Many of these variables are proven to be modifiable and could be improved with proper nutrition, toxin avoidance, endurance exercise, and even surgery. The purpose of this manuscript is to describe the different future data points that can be implemented in ASD assessment to improve modeling prediction, allow monitoring their response to prerehabilitation programs, and improve patient counseling.