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Machine Predicts Inpatient Sepsis 5 Hours
Sooner
Marcia Frellick
April 10, 2018
ORLANDO — Deep machine learning can help predict sepsis in hospitalized
patients an average of 5 hours before they meet the clinical definition, new
data show.
"Every hour delay in antibiotic therapy is associated with a 7% to 8% increase
in mortality for those with septic shock," said investigator Cara O'Brien, MD,
from Duke University in Durham, North Carolina.
"Current tools to identify sepsis don't work very well," she told Medscape
Medical News. They only look at the last set of variables, such as the last
respiratory rate or last lab result.
Deep learning looks at the trajectory of a patient's data throughout his or her
hospital stay.
"It's not looking at a single time point," O'Brien explained. "It's incorporating
where the patient was 2 days ago, 1 day ago, 12 hours ago into the risk
prediction."
Other tools used to predict sepsis are often compromised by alarm fatigue,
said lead investigator Anthony Lin, a third-year medical student at the Duke
Institute for Health Innovation.
In fact, Lin and his colleagues found that alarms for sepsis in the Duke system
were being canceled 63% of the time.
Current tools to identify sepsis don't work very well.
Sepsis is difficult to detect because the same symptoms can indicate many
diseases. Treatment is not difficult; the challenge is finding which patients are
septic, Lin explained here at the Society of Hospital Medicine 2018 Annual
Meeting.
In their retrospective study, the investigators used a deep-learning tool —
which has been used in previous forms of speech-recognition programs, such
as Google Translate — to look at fairly common predictor variables, such as
vital signs, lab results, medication administrations, and orders.
They looked at all 43,046 adult inpatient admissions at Duke University
Hospital from October 1, 2014 to December 31, 2015. An analysis of the
millions of data points gleaned from the electronic health records of these
patients yielded 83 predictor variables.
The deep-learning tool outperformed the other tools currently available for the
early detection of sepsis, Lin reported.
And because the tool uses predictor variables commonly found in electronic
health records, it could be used to identify patients experiencing cardiac or
respiratory arrest in the hospital or a postoperative infection, the investigators
note.
"We're currently investigating how this would play out in cardiogenic shock,"
Lin said.
The team is planning to launch the deep-learning intervention at Duke later
this year. "It's one thing to build a model, but another to implement it in a
health system and identify who would answer alarms and who would work up
the patient," he noted.
Evolution of Informatics in Medicine
"One of the things that excites me about this research is that it represents an
evolution in the way we're using informatics in medicine," said Ethan Cumbler,
MD, from the University of Colorado School of Medicine in Denver, who is
director of the research, innovation, and vignettes section for the meeting.
"If we can start using this form of artificial intelligence to not just store
information within the electronic health record, but to derive from electronic
health records new ways of understanding the data, we've created an entirely
new way for medical informatics to support clinicians," Cumbler
told Medscape Medical News. "That is exciting."
O'Brien, Lin, and Cumbler have disclosed no relevant financial relationships.
Society of Hospital Medicine (HM) 2018 Annual Meeting: Abstract 413603.
Presented April 10, 2018.
Follow Medscape on Twitter @Medscape and Marcia Frellick @mfrellick
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