AI for rapid detection
For the study, which included the largest-ever clinical genomics study of ER patients, researchers examined a total of 348 patients across four different continents. They confirmed their findings by re-examining two other large studies for a total of 1,062 patients. The blood of these patients underwent sequencing that revealed the expression levels of genes, which determines which proteins are produced and thus served to report on the immune status (including dysfunction) of sepsis patients.
The research showed that severe sepsis can be detected when a person first arrives for medical care. Using machine learning, the researchers were able to identify sets of genes that predict whether a patient will acquire severe sepsis, and could make sense of the five distinct ways (subtypes/endotypes) in which sepsis manifests itself.
This will lead to tests that allow healthcare providers to quickly identify the body’s dysfunctional response to an infection by measuring these specific gene-expression biomarkers associated with the disease. The technique is also 97-per-cent accurate in identifying which of the five endotypes of sepsis occurs in each patient. This is important because two subtypes are associated with a much higher risk of severe sepsis and death. These biomarkers also worked in the ICU, where it was shown that one endotype was particularly deadly, with a mortality rate of 46 per cent.
Quickly identifying the type of sepsis will help physicians determine the appropriate treatment. The team also identified other biomarkers that assess the severity of sepsis (e.g. causing organ failure) and the risk of death.
The technology for measuring gene expression is already present in hospitals, and the technique can be performed within two hours of admission to the ER.
The study was published in EBioMedicine.
Source: University of British Columbia
Source: Healthcare in Europe