“EHRs have become essential resources for providing relevant information on patients’ medical histories and improving the quality of care,” said study co-author 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.”
Sepsis is an immune system response to infection that can damage organs and cause permanent physical and mental disabilities. It is associated with increased blood levels of lactate, an acid produced when organs receive too little oxygen.
Patients are rarely screened for blood lactate levels, because sepsis is very hard to distinguish in its early stages. The blood test also lacks specificity, as many patients with elevated lactate do not have sepsis.
While early treatment with broad-spectrum antibiotics and intravenous fluids is associated with better outcomes for those with sepsis, the potential harm from those treatments for low-risk patients far outweighs the benefits.
“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 electronic health records 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.”
In conducting their investigation, the researchers analyzed data from the EHRs of 741 patients with sepsis at UC Davis Medical Center during 2010. They found that vital signs combined with serum white blood cell count — measures routinely taken for hospitalized patients — could accurately predict high lactate levels and sepsis. They also found that lactate level, blood pressure and respiratory rate could determine a patient’s risk of death from sepsis.
The research team is now working on a specific sepsis-risk algorithm that can be automatically calculated in the EHR.
“The electronic health record has been a transformative development for the delivery of health care with enormous potential,” said Ilias Tagkopoulos, assistant professor of computer science at UC Davis and senior author of the study. “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.”
Other study authors were Eren Gultepe, a former student at the Department of Biomedical Engineering, Jeffrey Green of the UC Davis Department of Emergency Medicine, and Jason Adams of the UC Davis Department of Internal Medicine.
The study, “From Vital Signs to Clinical Outcomes for Patients with Sepsis: A Machine Learning Basis for a Clinical Decision Support System,” was funded by the Center for Information Technology Research in the Interest of Society (grant #2469085) and the National Center for Advancing Translational Sciences of the National Institutes of Health (grant #UL1 TR000002). It is published in the current issue of the Journal of the American Medical Informatics Association and can be downloaded on the journal’s website.
UC Davis Medical Center, a leader in instituting EHR, won the 2013 Enterprise HIMSS Davies Award of Excellence, which recognizes achievements in using information technology to improve health-care delivery processes and patient safety. For three consecutive years, the medical center also has been designated as a national leader in health information technology based on the Most Wired Survey and Benchmarking Study published in Hospitals & Health Networks magazine.