Using data routinely entered into a patient's electronic health record, researchers at UC-Davis have developed an algorithm to predict sepsis cases earlier to allow for more prompt interventions.
Researchers analyzed data from 741 sepsis patients' EHRs, according to an account of the algorithm development published in the Journal of the American Medical Informatics Association. The results showed analyzing vitals along with serum white blood cell counts can predict sepsis, and median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate can predict a patient's risk of death from sepsis.
"Rather than using a 'gut-level' approach in an uncertain situation, physicians can instead use a decision-making tool that 'learns' from patient histories to identify health status and probable outcomes," said Ilias Tagkopoulos, assistant professor of computer science at UC Davis and senior author of the study, in a news release. "Another benefit of the sepsis predictor is that it is based on routine measures, so it can be used anywhere — on the battlefield or in a rural hospital in a third-world country."
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