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

Halden F Scott 1, Kathryn L Colborn 2, Carter J Sevick 3, Lalit Bajaj 4, Niranjan Kissoon 5, Sara J Deakyne Davies 6, Allison Kempe 7

Affiliations

PMID: 31733815
PMCID: PMC6980682 (available on 2021-02-01)
DOI: 10.1016/j.jpeds.2019.09.079

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.