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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.

研究者北卡罗来纳州Durham的Duke大学Cara O'Brien医生说到:“对于感染性休克患者,抗生素治疗每延误一个小时,病死率增加7%-8%。”

"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.

用于预测全身性感染的其他工具常常受到误报警的困扰,首席研究者Anthony Lin说到。Lin是Duke健康创新学院的一名3年级医学生。

In fact, Lin and his colleagues found that alarms for sepsis in the Duke system were being canceled 63% of the time.


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.

在回顾性研究中,研究者采用了一种深度学习工具 — 这种工具已经用于语言识别计划,如Google Translate — 以寻找较为常见的预测指标,如生命体征、实验室检查结果、用药情况和医嘱。

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 医学信息学的进步

"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.

Ethan Cumbler来自丹佛的科罗拉多大学医学院,她是大会研究及创新部门主任。她说:“这项研究令我兴奋的原因之一在于,它代表了我们在医学中应用信息学的一项进步。”

"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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