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

AbstractBACKGROUND:
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.
AuthorsKordo Saeed, Darius Cameron Wilson, Frank Bloos, Philipp Schuetz, Yuri van der Does, Olle Melander, Pierre Hausfater, Jacopo M Legramante, Yann-Erick Claessens, Deveendra Amin, Mari Rosenqvist, Graham White, Beat Mueller, Maarten Limper, Carlota Clemente Callejo, Antonella Brandi, Marc-Alexis Macchi, Nicholas Cortes, Alexander Kutz, Peter Patka, María Cecilia Yañez, Sergio Bernardini, Nathalie Beau, Matthew Dryden, Eric C M van Gorp, Marilena Minieri, Louisa Chan, Pleunie P M Rood, Juan Gonzalez Del Castillo
JournalCritical care (London, England) (Crit Care) Vol. 23 Issue 1 Pg. 40 (Feb 08 2019) ISSN: 1466-609X [Electronic] England
PMID30736862 (Publication Type: Journal Article, Multicenter Study, Observational Study)
Chemical References
  • Biomarkers
  • Peptide Fragments
  • Protein Precursors
  • mid-regional pro-adrenomedullin, human
  • Adrenomedullin
  • Lactic Acid
  • C-Reactive Protein
Topics
  • Adolescent
  • Adrenomedullin (analysis, blood)
  • Adult
  • Aged
  • Aged, 80 and over
  • Area Under Curve
  • Biomarkers (analysis, blood)
  • C-Reactive Protein (analysis)
  • Disease Progression
  • Early Diagnosis
  • Emergency Service, Hospital (organization & administration, statistics & numerical data)
  • England
  • Female
  • France
  • Humans
  • Infections (diagnosis)
  • Italy
  • Lactic Acid (analysis, blood)
  • Logistic Models
  • Male
  • Middle Aged
  • Organ Dysfunction Scores
  • Peptide Fragments (analysis, blood)
  • Proportional Hazards Models
  • Protein Precursors (analysis, blood)
  • Spain
  • Statistics, Nonparametric
  • Sweden
  • Switzerland
  • Validation Studies as Topic

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