The TRIAGE-ProADM score for an early risk stratification of medical patients in the emergency department - development based on a multi-national, prospective, observational study

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Abstract

Introduction: The inflammatory biomarker pro-adrenomedullin (ProADM) provides additional prognostic information for the risk stratification of general medical emergency department (ED) patients. The aim of this analysis was to develop a triage algorithm for improved prognostication and later use in an interventional trial. Methods: We used data from the multi-national, prospective, observational TRIAGE trial including consecutive medical ED patients from Switzerland, France and the United States. We investigated triage effects when adding ProADM at two established cut-offs to a five-level ED triage score with respect to adverse clinical outcome. Results: Mortality in the 6586 ED patients showed a step-wise, 25-fold increase from 0.6% to 4.5% and 15.4%, respectively, at the two ProADM cut-offs (≤0.75nmol/L, >0.75-1.5nmol/L, >1.5nmol/L, p ANOVA <0.0001). Risk stratification by combining ProADM within cut-off groups and the triage score resulted in the identification of 1662 patients (25.2% of the population) at a very low risk of mortality (0.3%, n = 5) and 425 patients (6.5% of the population) at very high risk of mortality (19.3%, n = 82). Risk estimation by using ProADM and the triage score from a logistic regression model allowed for a more accurate risk estimation in the whole population with a classification of 3255 patients (49.4% of the population) in the low risk group (0.3% mortality, n = 9) and 1673 (25.4% of the population) in the high-risk group (15.1% mortality, n = 252). Conclusions: Within this large international multicenter study, a combined triage score based on ProADM and established triage scores allowed a more accurate mortality risk discrimination. The TRIAGE-ProADM score improved identification of both patients at the highest risk of mortality who may benefit from early therapeutic interventions (rule in), and low risk patients where deferred treatment without negatively affecting outcome may be possible (rule out).

Figures

  • Table 1. Patient characteristics.
  • Table 1. (Continued)
  • Fig 1. Definition of ProADM cut-off values for 30-day mortality prediction. p (goodness of fit) = 0.494; ProADM, pro-adrenomedullin.
  • Table 2. 30-day mortality according to predefined ProADM categories.
  • Table 3. Mortality risk prediction according to predefined ProADM categories.
  • Fig 2. Observed risk assessment combining initial emergency department triage score information and ProADM cut-off values.
  • Fig 3. Derivation of a biomarker based algorithm combining the triage score and ProADM to more efficiently triage patients at risk for 30-day mortality. (A) Triage score based risk stratification, (B) ProADM based risk stratification, (C) Combined model. ProADM, pro-adrenomedullin.
  • Fig 4. Effect of reclassification on overall identification rate of non-survivors 30 days after emergency department admission. This figure shows mortality in patients classified as “low risk”, “moderate risk” and “high risk” based on the triage score only (left panel), the ProADM cut-offs only (second from left panel), and the triage/ProADM cut-offs combination (second from right panel). Use of the triage score only, identified 147/315 (46.7%) non-survivors in the group of patients classified as “high risk”. The combined model however identified 214/315 (67.9%) non-survivors which corresponds to a relative risk increase of 45.6% with the addition of ProADM. Similarly, the number of non-survivors in the low risk “not urgent” population was reduced from 2.3% in the triage score classification to 1.1% in the combined model, resulting in an improvement of 53.5%. ProADM, pro-adrenomedullin.

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APA

Kutz, A., Hausfater, P., Amin, D., Amin, A., Canavaggio, P., Sauvin, G., … Simpson, J. (2016). The TRIAGE-ProADM score for an early risk stratification of medical patients in the emergency department - development based on a multi-national, prospective, observational study. PLoS ONE, 11(12). https://doi.org/10.1371/journal.pone.0168076

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