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

Nemati Shamim PhD; Holder, Andre MD, MSc; Razmi, Fereshteh MS; Stanley, Matthew D. MD; Clifford, Gari D. PhD; Buchman, Timothy G. PhD, MD
doi: 10.1097/CCM.0000000000002936
Clinical Investigation: PDF Only

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.

The opinions or assertions contained herein are the private ones of the author/speaker and are not to be construed as official or reflecting the views of the Department of Defense, the Uniformed Services University of the Health Sciences, or any other agency of the U.S. Government.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

Drs. Nemati, Stanley, and Clifford received support for article research from the National Institutes of Health (NIH). Dr. Nemati’s institution received funding from the NIH, award number K01ES025445. Dr. Holder received funding from CR Bard, Inc. Dr. Buchman’s institution received funding from the Henry M. Jackson Foundation for his role as site director in Surgical Critical Care Institute, www.sc2i.org, funded through the Department of Defense’s Health Program – Joint Program Committee 6/Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001); from Society of Critical Care Medicine for his role as Editor-in-Chief of “Critical Care Medicine”; and from Philips Corporation (unrestricted educational grant to a physician education association in South Korea so he could present the results of his research in eICU). Dr. Buchman received support for article research from the Henry M Jackson Foundation. Ms. Ramzi has disclosed that she does not have any potential conflicts of interest.

For information regarding this article, E-mail: shamim.nemati@alum.mit.edu

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