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J Pediatr. 2019 Nov 8. pii: S0022-3476(19)31317-4. doi: 10.1016/j.jpeds.2019.09.079. [Epub ahead of print]

Development and Validation of a Predictive Model of the Risk of Pediatric Septic Shock Using Data Known at the Time of Hospital Arrival.

Author information

1
Department of Pediatrics, University of Colorado, Aurora, CO; Section of Pediatric Emergency Medicine, Children's Hospital Colorado, Aurora, CO. Electronic address: Halden.scott@childrenscolorado.org.
2
Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO.
3
Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado, Aurora, CO.
4
Department of Pediatrics, University of Colorado, Aurora, CO; Section of Pediatric Emergency Medicine, Children's Hospital Colorado, Aurora, CO; Center for Clinical Effectiveness, Children's Hospital Colorado, Aurora, CO.
5
Division of Critical Care, Department of Pediatrics, British Columbia Children's Hospital, Vancouver, British Columbia, Canada; Department of Pediatrics and Emergency Medicine, University of British Columbia, Vancouver, BC, Canada.
6
Research Informatics, Children's Hospital Colorado, Aurora, CO.
7
Department of Pediatrics, University of Colorado, Aurora, CO; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado, Aurora, CO.

Abstract

OBJECTIVE:

To derive and validate a model of risk of septic shock among children with suspected sepsis, using data known in the electronic health record at hospital arrival.

STUDY DESIGN:

This observational cohort study at 6 pediatric emergency department and urgent care sites used a training dataset (5 sites, April 1, 2013, to December 31, 2016), a temporal test set (5 sites, January 1, 2017 to June 30, 2018), and a geographic test set (a sixth site, April 1, 2013, to December 31, 2018). Patients 60 days to 18 years of age in whom clinicians suspected sepsis were included; patients with septic shock on arrival were excluded. The outcome, septic shock, was systolic hypotension with vasoactive medication or ≥30 mL/kg of isotonic crystalloid within 24 hours of arrival. Elastic net regularization, a penalized regression technique, was used to develop a model in the training set.

RESULTS:

Of 2464 included visits, septic shock occurred in 282 (11.4%). The model had an area under the curve of 0.79 (0.76-0.83) in the training set, 0.75 (0.69-0.81) in the temporal test set, and 0.87 (0.73-1.00) in the geographic test set. With a threshold set to 90% sensitivity in the training set, the model yielded 82% (72%-90%) sensitivity and 48% (44%-52%) specificity in the temporal test set, and 90% (55%-100%) sensitivity and 32% (21%-46%) specificity in the geographic test set.

CONCLUSIONS:

This model estimated the risk of septic shock in children at hospital arrival earlier than existing models. It leveraged the predictive value of routine electronic health record data through a modern predictive algorithm and has the potential to enhance clinical risk stratification in the critical moments before deterioration.

KEYWORDS:

diagnosis; emergency medicine; machine learning; prediction; sepsis

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