August M
A new artificial intelligence tool could change how doctors prepare patients for surgery. By analyzing genetic data, researchers believe it may be possible to identify life-threatening risks earlier than ever.
The development targets sepsis, a condition responsible for millions of deaths each year worldwide.
Scientists from CIBER at the University of Valladolid Clinical Hospital have created an AI-based model designed to estimate the likelihood of sepsis developing after surgery, WPtech reported. The findings were published in the journal Frontiers in Medicine.
Rather than relying solely on clinical observation, the system evaluates genetic patterns to flag patients who may face higher risks of complications.
A global threat
Sepsis occurs when the body overreacts to an infection, often bacterial, triggering widespread inflammation and organ damage. Mortality rates typically range between 10 and 20 percent, rising to around 40 percent in cases of septic shock.
Globally, the condition is linked to roughly 11 million deaths each year, making it one of the leading causes of mortality.
These figures highlight the urgency behind efforts to detect and manage the condition earlier in medical care.
Genes in focus
To build the model, researchers analyzed genomic data from a large group of patients. This included 750 individuals who developed sepsis after surgery and 3,500 who did not.
The system then evaluated genetic variants, ranking them according to their relevance in predicting complications.
Through this process, scientists identified key variations in genes such as PRIM2, SYNPR, and RBSN, which appear to play a role in susceptibility.
Earlier intervention
Beyond prediction, the model sheds light on biological mechanisms linked to sepsis. It highlights genes involved in processes like DNA replication, cell signaling, and regulation of gene activity.
Researchers say this approach could allow clinicians to act before complications arise, tailoring care to each patient’s genetic profile.
If implemented in clinical settings, the tool may help reduce risks and improve surgical outcomes by enabling earlier and more targeted interventions.
Sources: WPtech, Frontiers in Medicine
