DIAGNOSTIC ACCURACY AND ADDED VALUE OF INFECTION BIOMARKERS IN PATIENTS WITH POSSIBLE SEPSIS IN THE EMERGENCY DEPARTMENT

Abstract

Background: Biomarkers for early recognition of infection are warranted. The hypothesis of this study was that calprotectin, C-reactive protein (CRP), IL-6 and procalcitonin (PCT), alone or in combination, provide clinically useful information to the clinicians for early identification of infection in patients with possible sepsis in the emergency department (ED). Biomarker dynamics in the first week of hospitalization were explored. Methods: Adult patients in rapid response teams in the ED were included in a prospective observational study (n = 391). Patients who received antibiotics after biomarker availability were excluded. The ED clinician (EDC) decision whether to start antibiotics was registered. Calprotectin, CRP, IL-6, and PCT were analyzed in blood samples drawn within 15 min after ED arrival and in a subgroup for 1 week. Infection likelihood was evaluated post hoc . Results: In identifying patients with infection, CRP (area under the receiver operating characteristic curve [AUC], 0.913) and IL-6 (AUC, 0.895) were superior to calprotectin (AUC, 0.777) and PCT (AUC, 0.838). The best regression model predicting infections included EDC, CRP, and IL-6. Using optimal cutoff values, CRP and IL-6 in combination reached 95% positive and 90% negative predictive values for infection. The EDC undertreated or overtreated 65 of 391 patients (17%), and CRP and IL-6 optimal cutoff values could correct this in 32 of 65 patients (49%). Longitudinal samples revealed that IL-6 peaked in the ED, whereas CRP and PCT peaked later. Conclusion: C-reactive protein and IL-6 were superior to calprotectin and PCT for recognizing infection in patients with possible sepsis in the ED. Combining these two biomarkers with different dynamics improved recognition of infection and could aid clinical management in rapid response teams in the ED.

Trial registration: ClinicalTrials.gov NCT03956043.

Conflict of interest statement

The authors report no conflicts of interest.

Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.

Figures

Fig. 1

Inclusion, sampling, and post hoc infection likelihood assessment flowchart. Flowchart depicting inclusion and sampling of patients in the ED (A) and for 1 week of follow-up (B) and post hoc evaluation of infection likelihood (C). Biomarker values were not available for the EDC at antibiotic therapy initiation.

Fig. 2

Biomarker distribution in the ED across infection likelihood groups. Logarithmic distribution and median (dashed line) and interquartile range (whiskers) of the biomarkers PCT, IL-6, CRP, and calprotectin in the infection likelihood groups “not likely” (n = 154), “probable” (n = 135), and “definite” (n = 102) infection. Asterisks indicate significance; **P < 0.01, ****P < 0.0001 using Kruskal-Wallis post hoc multiple comparisons tests.

Fig. 3

Receiver operating characteristic curves. Receiver operating characteristic curves for the biomarkers (dashed) and clinician (dotted) alone and combined (solid line) illustrating ability to discriminate patients with (“probable” and “definite” infection likelihood groups merged) and without infection among all included admissions (n = 391). The combined ROC curves are modeled using multiple logistic regression. Gray-shaded area indicates improved performance from clinician alone to biomarker(s) and clinician.

Fig. 4

Decision curve analysis. Decision curve analysis for the best regression model including IL6, CRP, and EDC clinical decision (solid line), the EDC alone (dashed line) compared with treating all (dash-dotted line) in all patients and patients with qSOFA <2 and ≥2. A larger area under the decision curve suggests better clinical utility. The gray-shaded area indicates improved performance from clinician alone to the regression model. At lower threshold probabilities, the increased net benefit of using the regression model is low compared with treating all, especially in patients with qSOFA ≥2.

Fig. 5

Identifying undertreated and overtreated patients using CRP and IL-6. Flowchart stratifying cases according to the EDC's decision to treat with antibiotics, CRP, and IL-6 using optimal cutoff values (≥31 mg/L and ≥52 pg/mL, respectively). †The number and percentage of patients with “probable” or “definite” infection (based on the post hoc infection likelihood assessment) in the red-shaded boxes. Infection was “probable” or “definite” in 10 of 13 patients (77%) who did not receive antibiotics in the ED but had both CRP and IL-6 values greater than the optimal cutoff values. Inversely, infection was “probable” or “definite” only in 7 of 24 patients (24%) who received antibiotics in the ED, but had CRP and IL-6 values less than the optimal cutoff values.

Fig. 6

Longitudinal biomarker dynamics. Levels of biomarkers (mean with SD) during first week of hospitalization in patients with infection (n = 30) or infection “not likely” (n = 15). Dotted lines indicate optimal cutoff values in same colors as corresponding biomarkers.

Fig. 7

Suggested treatment algorithm in RRTs in the ED. The algorithm depends on qSOFA, CRP (≥31 mg/L), and/or IL-6 (≥52 pg/mL) optimal cutoff values. For patients with qSOFA <2, if both CRP and IL-6 values are above cutoff, PPV is 97%. If either CRP or IL-6 are above cutoff, PPV is 88%. If both are below cutoff in the same group, NPV is 86%.