AI anticipates the risk of sepsis after surgery

Researchers from the area of ​​Bioengineering, Biomaterials and Nanomedicine of the Network Biomedical Research Center (CIBER-BBN) and the area of ​​Infectious Diseases (CIBERINFEC) at the University Clinical Hospital of Valladolid (Castilla y León) have applied a artificial intelligence model explainable (XAI) to identify and prioritize genetic information that increases the risk of developing sepsis after surgery.

sepsis It is a serious complication caused by a uncontrolled response of the body to an infectionusually of bacterial origin. It is the most severe form of an infection and presents a mortality ranging between 10 and 20 percentreaching 40 percent in cases of septic shock. Worldwide, sepsis causes around 11 million deaths each year, of which around 17,000 occur in Spain.

The work, published in Frontiershas analyzed data from a genome-wide association analysis (GWAS) that included genetic information from 753 patients who developed sepsis after surgery and 3,500 population controls. Using an explainable AI model, the researchers were able to not only predict the risk of sepsis, but also prioritize the genetic variants with the greatest contribution to this risk.

Thanks to this approach, the study identified genetic variations in the PRIM2, RBSN and SYNPR genes with functional, regulatory and clinical implications. In addition, genes related to key biological processes such as the regulation of gene expression, DNA replication, cell signaling, cell proliferation and cardiac dysfunction were detected.

As the researchers have pointed out, determining these genetic variants through blood tests Preoperative tests could become a useful tool to improve risk stratification in surgical patients, facilitate early detection of postoperative sepsis and guide more personalized clinical interventions, with the aim of improve clinical outcomes and patient survival.

The study points out that more research will be necessary, including in vitro and in vivo analysisas well as complementary studies in cohorts comprising patients with sepsis and without sepsis undergoing major surgery to optimally evaluate the genetic factors that contribute to the predisposition to sepsis and provide a external validation of exploratory findings.

The work has had the participation of research staff from the CIBER for Respiratory Diseases (CIBERES), the CIBER for Rare Diseases (CIBERER), the University of Valladolid and the University of Leicester in the United Kingdom, among other institutions.