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Impact of impaired renal function on kinetics of high-sensitive cardiac troponin following cardiac surgery.
Clin Res Cardiol
January 2025
Clinic for General and Interventional Cardiology/Angiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, Georgstraße 11, 32545, Bad Oeynhausen, Germany.
Background: Impaired renal function can increase cardiac troponin levels due to reduced elimination, potentially affecting its diagnostic utility. Limited data exist on high-sensitivity cardiac troponin I (hs-cTnI) kinetics after cardiac surgery relative to renal function. This study evaluates how impaired renal function influences hs-cTnI kinetics following cardiac surgery, distinguishing between patients with and without postoperative myocardial infarction (PMI).
View Article and Find Full Text PDFClinical characteristics and predictive biomarkers of intensive care unit-acquired weakness in patients with cardiogenic shock requiring mechanical circulatory support.
Sci Rep
January 2025
Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Fukuoka, Japan.
Intensive care unit-acquired weakness (ICU-AW) is recognized as newly-acquired bilateral muscle weakness, which is a complication of critical illness in the ICU; however, there are no reports on the pathogenesis and early predictors of ICU-AW specifically associated with cardiogenic shock (CS). Therefore, this study aimed to investigate the clinical characteristics of ICU-AW in patients with CS requiring mechanical circulatory support (MCS). This study was a single-center, prospective, and observational study.
View Article and Find Full Text PDFClinical derivation and data simulated validation of rule-out and rule-in algorithms for the Siemens Atellica IM high-sensitivity cardiac troponin I assay.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Heart Disease, Haukeland University Hospital, Bergen, Norway.
Background: This prospective, two-centre study derived and validated predictive algorithms for the Siemens Atellica IM high-sensitivity cardiac troponin I (hs-cTnI) assay in the emergency department (ED).
Methods: Algorithms for predicting 30-day myocardial infarction type 1 and 2 (MI) and death or non-ST-elevation myocardial infarction (NSTEMI, type 1 and 2) at index admission were developed from a derivation cohort of 1896 patients and validated using a synthetic dataset with nearly 1 million patient cases. Performance was compared to the European Society of Cardiology algorithms for hs-cTnT (Roche Diagnostics) and hs-cTnI (Abbott Diagnostics).
Prognostication in emergency room patients: comparing ultrasensitive and contemporary quantification of cardiac troponin levels below the 99th percentile.
Front Cardiovasc Med
January 2025
Department of Cardiology, Joan XXIII University Hospital, Tarragona, Spain.
Introduction: Cardiac troponin levels below the 99th percentile improve the predictive efficacy for cardiovascular events when associated with relevant clinical variables. However, whether ultra-sensitive analytical methods improve this predictive efficacy over less sensitive or contemporary analytical methods remains unknown.
Methods: This retrospective observational study involved consecutive patients who presented to the emergency department for suspected acute coronary syndrome and underwent measurement of ultra-sensitive cardiac troponin I (Singulex) and contemporary cardiac troponin I (Siemens) with levels below the 99th percentile.
Machine-learning based risk prediction of outcomes in patients hospitalised with COVID-19 in Australia: the AUS-COVID score.
J Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
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