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EHR data analytics help detect risk, outcomes of sepsis

By Jennifer Bresnick

- Researchers from the University of California Davis have devised a method of identifying sepsis in hospitalized patients using just three routinely collected categories of data, and have also determined that an additional algorithm is capable of assessing the risk of death from the disease that is increasingly difficult to treat the longer it grips a patient’s system.

“Finding a precise and quick way to determine which patients are at high risk of developing the disease is critically important,” said study co-author Hien Nguyen, Associate Professor of Internal Medicine and Medical Director of EHRs at UC Davis. “We wanted to see if EHRs could provide the foundation for knowing when aggressive diagnosis and treatment are needed and when they can be avoided.”

The first method gives providers an early warning by crunching data from three simple measures: blood pressure, respiratory rate, and white blood cell count.  After analyzing data on 741 patients with sepsis, they found similarities in vital signs as well as elevated lactate levels that gave additional clues about the risk of dying from the immune system disease.  Using the data, the research team is currently working on developing an algorithm that can be used to create EHR alerts and provide instant information for clinicians.

“The electronic health record has been a transformative development for the delivery of health care with enormous potential,” said senior author Ilias Tagkopoulos, Assistant Professor of Computer Science at UC Davis. “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. 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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  • Sepsis kills nearly 40% of the 750,000 patients who contract it each year, and costs hospitals more than $12.5 billion in care costs.  Because the disease is difficult to detect and distinguish from other conditions in its early stages, the infection often takes hold before physicians can administer antibiotics and other effective treatments.

    The UC Davis research team follows a similar project by aerospace contractor Lockheed Martin, which announced its analytical approach to sepsis detection last October.  By applying missile defense data crunching techniques to patient information, Lockheed Martin has achieved 90% accuracy in identifying sepsis sixteen hours before it manifests to most physicians, and has also reduced the false positive rate to hone the usefulness of EHR alerts.

    “EHRs have become essential resources for providing relevant information on patients’ medical histories and improving the quality of care,” added Tim Albertson, chair of UC Davis Department of Internal Medicine. “We have shown that they can also be powerful resources for identifying best practices in medicine and reducing patient mortality.”

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