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Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis - 21/03/19

Doi : 10.1016/j.annemergmed.2018.11.036 
Ryan J. Delahanty, PhD a, JoAnn Alvarez, MS a, Lisa M. Flynn, MD a, Robert L. Sherwin, MD b, Spencer S. Jones, PhD a,
a Tenet Healthcare Corporation, Nashville, TN 
b Department of Emergency Medicine, Wayne State University, Detroit, MI 

Corresponding Author.

Abstract

Study objective

The Third International Consensus Definitions (Sepsis-3) Task Force recommended the use of the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score to screen patients for sepsis outside of the ICU. However, subsequent studies raise concerns about the sensitivity of qSOFA as a screening tool. We aim to use machine learning to develop a new sepsis screening tool, the Risk of Sepsis (RoS) score, and compare it with a slate of benchmark sepsis-screening tools, including the Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment (SOFA), qSOFA, Modified Early Warning Score, and National Early Warning Score.

Methods

We used retrospective electronic health record data from adult patients who presented to 49 urban community hospital emergency departments during a 22-month period (N=2,759,529). We used the Rhee clinical surveillance criteria as our standard definition of sepsis and as the primary target for developing our model. The data were randomly split into training and test cohorts to derive and then evaluate the model. A feature selection process was carried out in 3 stages: first, we reviewed existing models for sepsis screening; second, we consulted with local subject matter experts; and third, we used a supervised machine learning called gradient boosting. Key metrics of performance included alert rate, area under the receiver operating characteristic curve, sensitivity, specificity, and precision. Performance was assessed at 1, 3, 6, 12, and 24 hours after an index time.

Results

The RoS score was the most discriminant screening tool at all time thresholds (area under the receiver operating characteristic curve 0.93 to 0.97). Compared with the next most discriminant benchmark (Sequential Organ Failure Assessment), RoS was significantly more sensitive (67.7% versus 49.2% at 1 hour and 84.6% versus 80.4% at 24 hours) and precise (27.6% versus 12.2% at 1 hour and 28.8% versus 11.4% at 24 hours). The sensitivity of qSOFA was relatively low (3.7% at 1 hour and 23.5% at 24 hours).

Conclusion

In this retrospective study, RoS was more timely and discriminant than benchmark screening tools, including those recommend by the Sepsis-3 Task Force. Further study is needed to validate the RoS score at independent sites.

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Plan


 Please see page 335 for the Editor’s Capsule Summary of this article.
 Supervising editor: Alan E. Jones, MD. Specific detailed information about possible conflict of interest for individual editors is available at editors.
 Author contributions: RJD and SSJ conceived and designed the study. SSJ supervised the model development and analysis. JA implemented the code to identify sepsis-positive cases. RJD developed and evaluated the machine learning model and created the tables and figures. SSJ drafted the article, and all authors contributed substantially to its revision. SSJ takes responsibility for the paper as a whole.
 All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
 Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist. Dr. Sherwin has received funding from the Agency for Healthcare Research and Quality (PA-14-001, Exploratory and Developmental Grant to Improve Health Care Quality through Health Information Technology [IT]–R21) for the project titled “Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning.”
 Readers: click on the link to go directly to a survey in which you can provide XQHX3K6 to Annals on this particular article.
 A podcast for this article is available at www.annemergmed.com.


© 2018  American College of Emergency Physicians. Publié par Elsevier Masson SAS. Tous droits réservés.
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Vol 73 - N° 4

P. 334-344 - avril 2019 Retour au numéro
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