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Crit Care Med. 2018 Apr;46(4):547-553. doi: 10.1097/CCM.0000000000002936.

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Author information

1
Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.
2
Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA.
3
Department of Surgery, Emory University School of Medicine, Atlanta, GA.
4
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA.
5
Emory Critical Care Center, Emory Healthcare, Atlanta, GA.

Abstract

OBJECTIVES:

Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis.

DESIGN:

Observational cohort study.

SETTING:

Academic medical center from January 2013 to December 2015.

PATIENTS:

Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively.

INTERVENTIONS:

None.

MEASUREMENTS AND MAIN RESULTS:

High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable.

CONCLUSIONS:

Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.

PMID:
29286945
PMCID:
PMC5851825
[Available on 2019-04-01]
DOI:
10.1097/CCM.0000000000002936

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