• Philipp Schuetz, Marcel Wolbers, Mirjam Christ-Crain, Robert Thomann, Claudine Falconnier, Isabelle Widmer, Stefanie Neidert, Thomas Fricker, Claudine Blum, Ursula Schild, Nils G Morgenthaler, Ronald Schoenenberger, Christoph Henzen, Thomas Bregenzer, Claus Hoess, Martin Krause, Heiner C Bucher, Werner Zimmerli, Beat Mueller, and ProHOSP Study Group.
• Department of Internal Medicine, Division of Endocrinology, Diabetes and Clinical Nutrition, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland. Schuetzp@uhbs.ch
• Crit Care. 2010 Jan 1; 14 (3): R106.

IntroductionMeasurement of prohormones representing different pathophysiological pathways could enhance risk stratification in patients with community-acquired pneumonia (CAP) and other lower respiratory tract infections (LRTI).MethodsWe assessed clinical parameters and five biomarkers, the precursor levels of adrenomedullin (ADM), endothelin-1 (ET1), atrial-natriuretic peptide (ANP), anti-diuretic hormone (copeptin), and procalcitonin in patients with LRTI and CAP enrolled in the multicenter ProHOSP study. We compared the prognostic accuracy of these biomarkers with the pneumonia severity index (PSI) and CURB65 (Confusion, Urea, Respiratory rate, Blood pressure, Age 65) score to predict serious complications defined as death, ICU admission and disease-specific complications using receiver operating curves (ROC) and reclassification methods.ResultsDuring the 30 days of follow-up, 134 serious complications occurred in 925 (14.5%) patients with CAP. Both PSI and CURB65 overestimated the observed mortality (X2 goodness of fit test: P = 0.003 and 0.01). ProADM or proET1 alone had stronger discriminatory powers than the PSI or CURB65 score or any of either score components to predict serious complications. Adding proADM alone (or all five biomarkers jointly) to the PSI and CURB65 scores, significantly increased the area under the curve (AUC) for PSI from 0.69 to 0.75, and for CURB65 from 0.66 to 0.73 (P < 0.001, for both scores). Reclassification methods also established highly significant improvement (P < 0.001) for models with biomarkers if clinical covariates were more flexibly adjusted for. The developed prediction models with biomarkers extrapolated well if evaluated in 434 patients with non-CAP LRTIs.ConclusionsFive biomarkers from distinct biologic pathways were strong and specific predictors for short-term adverse outcome and improved clinical risk scores in CAP and non-pneumonic LRTI. Intervention studies are warranted to show whether an improved risk prognostication with biomarkers translates into a better clinical management and superior allocation of health care resources.Trial RegistrationNCT00350987.

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