Halden F Scott1, Kathryn L Colborn2, Carter J Sevick3, Lalit Bajaj4, Niranjan Kissoon5, Sara J Deakyne Davies6, Allison Kempe7. 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.
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.
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Authors: Fran Balamuth; Halden F Scott; Scott L Weiss; Michael Webb; James M Chamberlain; Lalit Bajaj; Holly Depinet; Robert W Grundmeier; Diego Campos; Sara J Deakyne Davies; Norma Jean Simon; Lawrence J Cook; Elizabeth R Alpern Journal: JAMA Pediatr Date: 2022-07-01 Impact factor: 26.796
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Authors: Daniele Roberto Giacobbe; Alessio Signori; Filippo Del Puente; Sara Mora; Luca Carmisciano; Federica Briano; Antonio Vena; Lorenzo Ball; Chiara Robba; Paolo Pelosi; Mauro Giacomini; Matteo Bassetti Journal: Front Med (Lausanne) Date: 2021-02-12