Prohormones for prediction of adverse medical outcome in community-acquired pneumonia and lower respiratory tract infections

Abstract

Introduction: Measurement of prohormones representing different pathophysiological pathways could enhance risk stratification in patients with community-acquired pneumonia (CAP) and other lower respiratory tract infections (LRTI).

Methods: We 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.

Results: During 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.

Conclusions: Five 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 registration: NCT00350987.

Figure 1

Univariate association of the biomarkers with serious complications (left panel) and death (right panel). ProADM (black, solid line), proET1 (black, dashed line), PSI class (grey, dashed line) and CURB65 score (grey, dash-dotted line).

Figure 2

Estimated association of proADM and proET1 levels with risk of serious complications (upper black line) and death (lower blue line). Estimates are based on generalized additive models and shaded gray regions correspond to (point-wise) 95% confidence intervals. The rugs at the bottom of the plots display the distribution of the biomarker.

Figure 3

ROC curves of multivariable models for the prediction of serious complications (left panel) and death (right panel) during 30 days of follow-up. Models are based on CURB65 covariates alone (grey, dash-dotted lines), or jointly with proADM (black, solid lines) or all five biomarkers (black, dashed lines), respectively, ROC curve estimated by six-fold cross-validation (leave-one-hospital out). The predictive accuracy of the PSI class (gray, dashed lines) is added as a comparison.

Figure 4

Boxplots of measured ProADM levels on admission (Day 0) and during follow-up (Days 3, 5, and 7) in patients with serious complications (boxplots with grey filling) and those without (boxplots with white filling). n refers to the number of available ProADM measurements at the respective time point (for example, on admission, ProADM was available in 130/134 patients with serious complications). Only ProADM values before the first serious complication were included in patients with complications.