title: Stress markers predict mortality in patients with nonspecific complaints presenting to the emergency department and may be a useful risk stratification tool to support disposition planning.
abstract: OBJECTIVE
  To the authors' knowledge, no prospectively validated, biomarker-based risk stratification tools exist for elderly patients presenting to the emergency department (ED) with nonspecific complaints (NSCs), such as generalized weakness, despite the fact that an acute serious disease often underlies nonspecific disease presentation. The primary purpose for this study was to validate the retrospectively derived model for outcome prediction using copeptin and peroxiredoxin 4 (Prx4), in a different group of patients, in a prospective fashion, in a multicenter setting. The secondary goals were to evaluate the potential contribution of the midregional portion of the precursor of adrenomedullin (MR-proADM) for outcome prediction and to investigate whether disposition decisions show promise for potential improvement by using biomarker levels in addition to a clinical assessment.
  
  
  METHODS
  The Basel Nonspecific Complaints (BANC) study is a delayed-type cross-sectional diagnostic study, carried out in three EDs in Switzerland, with a prospective 30-day follow-up. Patients presenting to the ED with NSCs, as defined previously, were included if their vital signs were within predefined limits. Measurement of biomarkers was performed in serum samples with sandwich immunoluminometric assays. To examine the disposition process, the final disposition was compared with a combination of the first clinical disposition decision and the risk assessment, which included the biomarker MR-proADM in a retrospective simulation. Patients were divided into three groups according to MR-proADM concentration, defining three risk classes with three disposition possibilities (admission to tertiary care, transfer to geriatric hospital, discharge).
  
  
  RESULTS
  Thirty-three 30-day nonsurvivors were observed from among 504 study patients with NSCs. Biomarker levels were significantly greater in nonsurvivors than survivors (p < 0.0001 for all three biomarkers). Univariate Cox models reveal a C-index of 0.732 for MR-proADM, 0.719 for Prx4, and 0.723 for copeptin. The incremental added value for chi-square obtained via multivariate modeling showed that models inclusive of MR-proADM, copeptin, or Prx4 are superior to and independent of models limited to sex and age. The incrementally added chi-square for MR-proADM, beyond the chi-square of a base model consisting of age and sex, was 29.79 (p < 0.00001). In a multimarker approach, only Prx4 provided additional information to MR-proADM alone (C-index = 0.77). Applying an algorithm combining physicians' first clinical assessment plus biomarker information to derive a modified risk assessment, reassignment would lead to a potential decrease of 48 admissions to acute care, seven additional transfers to geriatric care, and 41 additional discharges (negative likelihood ratio [-LR] = 0.13). Analysis of 30-day mortality reveals that our algorithm is not inferior in terms of safety.
  
  
  CONCLUSIONS
  In this study the authors confirm that these new stress biomarkers permit reliable prognostication of adverse outcomes in a heterogeneous group of patients with NSCs. A simulation showed that this prognostic information could be useful to enhance the appropriateness of disposition decisions of ED patients with NSC. The use of biomarkers for risk stratification in this patient group should be evaluated with prospective intervention studies.
authors.affiliation: Emergency Department, University Hospital, Basel, Switzerland. nickelc@uhbs.ch
authors.forename: Christian H
authors.initials: CH
authors.lastname: Nickel
authors.forename: Anna S
authors.initials: AS
authors.lastname: Messmer
authors.forename: Nicolas
authors.initials: N
authors.lastname: Geigy
authors.forename: Franziska
authors.initials: F
authors.lastname: Misch
authors.forename: Beat
authors.initials: B
authors.lastname: Mueller
authors.forename: Frank
authors.initials: F
authors.lastname: Dusemund
authors.forename: Sabine
authors.initials: S
authors.lastname: Hertel
authors.forename: Oliver
authors.initials: O
authors.lastname: Hartmann
authors.forename: Sven
authors.initials: S
authors.lastname: Giersdorf
authors.forename: Roland
authors.initials: R
authors.lastname: Bingisser
mesh_terms: D000208:Acute Disease; D053607:Adrenomedullin / Q000097:blood*; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D002423:Cause of Death; D016009:Chi-Square Distribution; D002908:Chronic Disease; D003430:Cross-Sectional Studies; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005221:Fatigue / Q000175:diagnosis / Q000401:mortality; D005260:Female; D015577:Geriatric Assessment; D006020:Glycopeptides / Q000097:blood*; D017052:Hospital Mortality; D006785:Hospitals, University; D006801:Humans; D007902:Length of Stay; D008297:Male; D015999:Multivariate Analysis; D054464:Peroxiredoxins / Q000097:blood*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D013315:Stress, Psychological; D016019:Survival Analysis; D013557:Switzerland
chemical_list: D015415:Biomarkers; D006020:Glycopeptides; C048197:copeptins; D053607:Adrenomedullin; D054464:Peroxiredoxins