Septicemia: This computer-assisted model can predict the risk of sepsis by analyzing early symptoms



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LONDON: British scientists have developed a computer-badisted model that could help predict sepsis by regularly collecting data to identify early symptoms of the life-threatening disease.

Sepsis is a leading cause of death in hospitals and early detection is critical to preventing death, said researchers at the University of Bradford in the UK.

The condition is caused by the body's response to an infection, causing changes that can damage several organ systems.

According to a study published in the Canadian Medical Association Journal, every hour of delay is badociated with a 7% reduction in survival, but detection times are common.

There are several scores to help identify patients with sepsis, including the National Early Warning Score (NEWS) used in National Health Service hospitals in the UK.

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The researchers developed the national computer-badisted early warning score (cNEWS) to determine if it could improve the accuracy of sepsis prediction.

"The main benefit of these computer models is that they are designed to incorporate data present in the patient record, can be easily automated and do not impose any additional burden on hospital staff for the gathering additional information, "said Professor Mohammed A Mohammed of the University of Bradford.

The cNEWS score can trigger screening for sepsis generally within 30 minutes of admission, once the information collected systematically has been entered electronically into the patient's medical record.

"These risk scores should support, rather than replace, clinical judgment, and we hope they will raise awareness about sepsis with additional information about this serious disease," Mohammed said in a statement.

The researchers said cNEWS could be introduced with caution in hospitals with an appropriate and evaluated IT infrastructure.

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