Pre-analytic factors and initial biomarker levels in community-acquired pneumonia patients.

BMC anesthesiology

PubMedID: 25419180

Kutz A, Grolimund E, Christ-Crain M, Thomann R, Falconnier C, Hoess C, Henzen C, Zimmerli W, Mueller B, Schuetz P, ProHOSP Study Group. Pre-analytic factors and initial biomarker levels in community-acquired pneumonia patients. BMC Anesthesiol. 2014;14102.
Blood biomarkers are increasingly used to diagnose, guide therapy in, and risk-stratify community-acquired pneumonia (CAP) patients in emergency departments (EDs). How pre-analytic factors affect these markers' initial levels in this population is unknown.

In this secondary analysis of consecutive ED patients with CAP from a large multicentre antibiotic stewardship trial, we used adjusted multivariate regression models to determine the magnitude and statistical significance of differences in mean baseline concentrations of five biomarkers (procalcitonin [PCT], C-reactive protein [CRP], white blood cells count [WBC], proadrenomedullin [ProADM], copeptin) associated with six pre-analytic factors (antibiotic or corticosteroid pretreatment, age, gender, chronic renal failure or chronic liver insufficiency).

Of 925 CAP patients (median age 73 years, 58.8% male), 25.5% had antibiotic pretreatment, 2.4%, corticosteroid pretreatment, 22.3%, chronic renal failure, 2.4% chronic liver insufficiency. Differences associated with pre-analytic factors averaged 6.1% ± 4.6%; the three largest statistically significant changes (95% confidence interval) were: PCT, +14.2% (+2.1% to +26.4%, p = 0.02) with liver insufficiency; ProADM, +13.2% (+10.2% to +16.1%, p < 0.01) with age above median; CRP, -12.8% (-25.4% to -0.2%, p = 0.05) with steroid pretreatment. In post hoc sensitivity analyses, reclassification statistics showed that these factors did not result in significant changes of biomarker levels across clinically used cut-off ranges.

Despite statistically significant associations of some pre-analytic factors and biomarker levels, a clinically relevant influence seems unlikely. Our observations reinforce the concept of using biomarkers in algorithms with widely-separated cut-offs and overruling criteria considering the entire clinical picture.

Identifier ISRCTN95122877.