Exploring biomarkers in routine diagnostics for the risk stratification of older patients in the Chest Pain Unit: a prospective cohort study - Hugo A Katus; Norbert Frey; Evangelos Giannitsis; Philipp Bahrmann; Anna Lisa Kunz; Anton Schönstein; Hans-Werner Wahl; Anke Bahrmann (BMJ Open, 2022)
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.