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Exploring biomarkers in routine diagnostics for the risk stratification of older patients in the Chest Pain Unit: a
Anna Lisa Kunz1, Anton Schönstein2, Philipp Bahrmann1
1Department of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Heidelberg, Germany.
Objectives:
This study aims to estimate the association of the often, in daily clinical practice, used biological age-related biomarkers high-sensitivity troponin-T (hs-TnT), C reactive protein (CRP) and haemoglobin (Hb) with all-cause mortality for the purpose of older patient's risk stratification in the emergency department (ED).
Design:
Exploratory, prospective cohort study with a follow-up at 2.5 years after recruitment started. For the predictors, data from the hospital files including the routinely applied biological age-related biomarkers hs-TnT, CRP and Hb were supplemented by a questionnaire.
Setting:
A cardiological ED, Chest Pain Unit, University Hospital Heidelberg, Germany.
Participants:
N=256 cardiological ED patients with a minimum age of 70 years and the capability to informed consent.
Primary Outcome Measures:
The primary outcome of this study was all-cause mortality which was assessed by requesting registry office information.
Results:
Among N=256 patients 63 died over the follow-up period. Positive results in each of the three biomarkers alone as well as the combination were associated with increased all-cause mortality at follow-up. The number of positive age-related biomarkers appeared to be strongly indicative of the risk of mortality, even when controlled for major confounders (age, sex, body mass index, creatinine clearance and comorbidity).
Conclusions:
In older ED patients, biomarkers explicitly related to biological ageing processes such as hs-TnT, CRP and Hb were to a certain degree independently of each other as well as combined associated with an increased risk of all-cause mortality. Thus, they may have the potential to be used to supplement the general risk stratification of older patients in the ED. Validation of the results in a large dataset is needed.
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