Quantification of a qualitative sepsis code: laying the foundations for the automation revolution

Eur J Pediatr. 2023 May;182(5):2169-2172. doi: 10.1007/s00431-023-04867-8. Epub 2023 Feb 27.

Abstract

To quantify a qualitative screening tool for the early recognition of sepsis in children with fever either visiting the emergency department or already admitted to hospital. Prospective observational study including febrile patients under 18 years of age. Sepsis diagnosis was the main outcome. A multivariable analysis was performed with 4 clinical variables (heart rate, respiratory rate, disability, and poor skin perfusion). The cut-off points, odds ratio, and coefficients of these variables were identified. The quantified tool was then obtained from the coefficients. The area under the curve (AUC) was obtained and internal validation was performed using k-fold cross-validation. Two hundred sixty-six patients were included. The multivariable regression confirmed the independent association of the 4 variables with the outcome. The quantified screening tool yielded an excellent AUC, 0.825 (95%CI 0.772-0.878, p < 0.001), for sepsis prediction. Conclusion: We successfully quantified a sepsis screening tool, and the resulting model has an excellent discriminatory power. What is Known: • Screening tests have to be based only on clinical variables that needs minimum technological support. • The current Sepsis Code is a qualitative screening tool. What is New: • The current screening tool was quantified using four clinical variables, weighted according to the deviation from normality and differentiated according to the age of the patient. • The resulting model has an excellent discriminatory power in identifying septic patients among febrile pediatric patients.

Keywords: Automatic; Paediatric; Screening; Sepsis.

Publication types

  • Observational Study

MeSH terms

  • Automation
  • Emergency Service, Hospital
  • Humans
  • Mass Screening
  • Prospective Studies
  • Retrospective Studies
  • Sepsis* / diagnosis