Scores for sepsis detection and risk stratification – construction of a novel score using a statistical approach and validation of RETTS

Research output: Contribution to journalArticle


Background To allow early identification of patients at risk of sepsis in the emergency department (ED), a variety of risk stratification scores and/or triage systems are used. The first aim of this study was to develop a risk stratification score for sepsis based upon vital signs and biomarkers using a statistical approach. Second, we aimed to validate the Rapid Emergency Triage and Treatment System (RETTS) for sepsis. RETTS combines vital signs with symptoms for risk stratification. Methods We retrospectively analysed data from two prospective, observational, multicentre cohorts of patients from studies of biomarkers in ED. A candidate risk stratification score called Sepsis Heparin-binding protein-based Early Warning Score (SHEWS) was constructed using the Least Absolute Shrinkage and Selector Operator (LASSO) method. SHEWS and RETTS were compared to National Early Warning Score 2 (NEWS2) for infection-related organ dysfunction, intensive care or death within the first 72h after admission (i.e. sepsis). Results 506 patients with a diagnosed infection constituted cohort A, in which SHEWS was derived and RETTS was validated. 435 patients constituted cohort B of whom 184 had a diagnosed infection where both scores were validated. In both cohorts (A and B), AUC for infection-related organ dysfunction, intensive care or death was higher for NEWS2, 0.80 (95% CI 0.76–0.84) and 0.69 (95% CI 0.63–0.74), than RETTS, 0.74 (95% CI 0.70–0.79) and 0.55 (95% CI 0.49–0.60), p = 0.05 and p <0.01, respectively. SHEWS had the highest AUC, 0.73 (95% CI 0.68–0.79) p = 0.32 in cohort B. Conclusions Even with a statistical approach, we could not construct better risk stratification scores for sepsis than NEWS2. RETTS was inferior to NEWS2 for screening for sepsis.


External organisations
  • University of British Columbia
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Infectious Medicine
Original languageEnglish
Article numbere0229210
JournalPLoS ONE
Issue number2
Publication statusPublished - 2020 Feb 20
Publication categoryResearch

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