The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study

Kordo Saeed(Hampshire Hospitals NHS Foundation Trust), Darius Cameron Wilson, Frank Bloos(Jena University Hospital), Philipp Schüetz(University of Basel), Yuri van der Does(Erasmus MC), Olle Melander(Lund University), Pierre Hausfater(Inserm), Jacopo M. Legramante(University of Rome Tor Vergata), Y.-É. Claessens(Princess Grace Hospital Centre), Deveendra Amin(Morton Plant Hospital), Mari Rosenqvist(Lund University), Graham White(Hampshire Hospitals NHS Foundation Trust), Beat Müeller(University of Basel), Maarten Limper(Utrecht University), Carlota Clemente Callejo(Hospital Clínico San Carlos), Antonella Brandi(Policlinico Tor Vergata), Marc-Alexis Macchi(Princess Grace Hospital Centre), Nicholas Cortes(Hampshire Hospitals NHS Foundation Trust), Alexander Kutz(Kantonsspital Aarau), P. Patka(Erasmus MC), María Cecilia Yañez(Hospital Clínico San Carlos), Sergio Bernardini(University of Rome Tor Vergata), Nathalie Beau(Princess Grace Hospital Centre), Matthew Dryden(Hampshire Hospitals NHS Foundation Trust), Eric C. M. Van Gorp(Erasmus MC), Marilena Minieri(Policlinico Tor Vergata), Louisa Chan(Hampshire Hospitals NHS Foundation Trust), Pleunie P. M. Rood(Erasmus MC), Juan González del Castillo(Instituto de Investigación Sanitaria del Hospital Clínico San Carlos)
Critical Care
February 8, 2019
Cited by 134Open Access
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Abstract

BACKGROUND: There is a lack of validated tools to assess potential disease progression and hospitalisation decisions in patients presenting to the emergency department (ED) with a suspected infection. This study aimed to identify suitable blood biomarkers (MR-proADM, PCT, lactate and CRP) or clinical scores (SIRS, SOFA, qSOFA, NEWS and CRB-65) to fulfil this unmet clinical need. METHODS: An observational derivation patient cohort validated by an independent secondary analysis across nine EDs. Logistic and Cox regression, area under the receiver operating characteristic (AUROC) and Kaplan-Meier curves were used to assess performance. Disease progression was identified using a composite endpoint of 28-day mortality, ICU admission and hospitalisation > 10 days. RESULTS: One thousand one hundred seventy-five derivation and 896 validation patients were analysed with respective 28-day mortality rates of 7.1% and 5.0%, and hospitalisation rates of 77.9% and 76.2%. MR-proADM showed greatest accuracy in predicting 28-day mortality and hospitalisation requirement across both cohorts. Patient subgroups with high MR-proADM concentrations (≥ 1.54 nmol/L) and low biomarker (PCT < 0.25 ng/mL, lactate < 2.0 mmol/L or CRP < 67 mg/L) or clinical score (SOFA < 2 points, qSOFA < 2 points, NEWS < 4 points or CRB-65 < 2 points) values were characterised by a significantly longer length of hospitalisation (p < 0.001), rate of ICU admission (p < 0.001), elevated mortality risk (e.g. SOFA, qSOFA and NEWS HR [95%CI], 45.5 [10.0-207.6], 23.4 [11.1-49.3] and 32.6 [9.4-113.6], respectively) and a greater number of disease progression events (p < 0.001), compared to similar subgroups with low MR-proADM concentrations (< 1.54 nmol/L). Increased out-patient treatment across both cohorts could be facilitated using a derivation-derived MR-proADM cut-off of < 0.87 nmol/L (15.0% and 16.6%), with decreased readmission rates and no mortalities. CONCLUSIONS: In patients presenting to the ED with a suspected infection, the blood biomarker MR-proADM could most accurately identify the likelihood of further disease progression. Incorporation into an early sepsis management protocol may therefore aid rapid decision-making in order to either initiate, escalate or intensify early treatment strategies, or identify patients suitable for safe out-patient treatment.


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