Biniyam G Demissei1,2, Mattia A E Valente1, John G Cleland3, Christopher M O'Connor4, Marco Metra5, Piotr Ponikowski6, John R Teerlink7, Gad Cotter8, Beth Davison8, Michael M Givertz9, Daniel M Bloomfield10, Howard Dittrich11, Peter van der Meer1, Dirk J van Veldhuisen1, Hans L Hillege1,2, Adriaan A Voors1. 1. Department of Cardiology, University of Groningen, University Medical Centre Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands. 2. Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands. 3. Imperial College, London, UK. 4. Duke University Medical Center, Durham, NC, USA. 5. University of Brescia, Brescia, Italy. 6. Medical University, Clinical Military Hospital, Wroclaw, Poland. 7. University of California at San Francisco and San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA. 8. Momentum Research, Durham, NC, USA. 9. Brigham and Women's Hospital, Boston, MA, USA. 10. Merck Research Laboratories, Rahway, NJ, USA. 11. Abboud Cardiovascular Research Center, University of Iowa Carver College of Medicine, Iowa, USA.
Abstract
AIM: The clinical value of single biomarkers at single time-points to predict outcomes in patients with acute heart failure (AHF) is limited. We performed a multimarker, multi-time-point analysis of biomarkers for the prediction of post-discharge clinical outcomes in high-risk AHF patients. METHODS AND RESULTS: A set of 48 circulating biomarkers were measured in the PROTECT trial which enrolled 2033 patients with AHF. Associations between baseline levels of biomarkers and outcomes (30-day all-cause mortality, 30-day death or rehospitalization for renal/cardiovascular causes and 180-day all-cause mortality) were evaluated. Prognostic accuracies of baseline, days 2 or 3, 7, and 14 biomarker measurements were estimated and compared utilizing a time-dependent area under the curve (AUC) analysis. Forty-four biomarkers were significantly associated with outcomes, but 42 had limited prognostic value (C-index < 0.70). However, multimarker models combining best-performing biomarkers from different clusters had a much stronger prognostic value. Combining blood urea nitrogen (BUN), chloride, interleukin (IL)-6, cTnI, sST-2 and VEGFR-1 into a clinical model yielded a 11% increase in C-index to 0.84 and 0.78 for 30-day and 180-day all-cause mortality, respectively, and cNRI of 0.86 95% CI [0.55-1.11] and 0.76 95% CI [0.57-0.87]. Prognostic gain was modest for the 30-day death/rehospitalization for cardiovascular or renal causes endpoint. Comparative time-dependent AUC analysis indicated that late measurements provided superior accuracy for the prediction of all-cause mortality over 180 days, with few exceptions including BUN and galectin-3. However, the predictive value of most biomarkers showed a diminishing pattern over time irrespective of moment of measurement. CONCLUSIONS: Multimarker models significantly improve risk prediction. Subsequent measurements, beyond admission, are needed for majority of biomarkers to maximize prognostic value over time, particularly in the long term.
RCT Entities:
AIM: The clinical value of single biomarkers at single time-points to predict outcomes in patients with acute heart failure (AHF) is limited. We performed a multimarker, multi-time-point analysis of biomarkers for the prediction of post-discharge clinical outcomes in high-risk AHF patients. METHODS AND RESULTS: A set of 48 circulating biomarkers were measured in the PROTECT trial which enrolled 2033 patients with AHF. Associations between baseline levels of biomarkers and outcomes (30-day all-cause mortality, 30-day death or rehospitalization for renal/cardiovascular causes and 180-day all-cause mortality) were evaluated. Prognostic accuracies of baseline, days 2 or 3, 7, and 14 biomarker measurements were estimated and compared utilizing a time-dependent area under the curve (AUC) analysis. Forty-four biomarkers were significantly associated with outcomes, but 42 had limited prognostic value (C-index < 0.70). However, multimarker models combining best-performing biomarkers from different clusters had a much stronger prognostic value. Combining blood ureanitrogen (BUN), chloride, interleukin (IL)-6, cTnI, sST-2 and VEGFR-1 into a clinical model yielded a 11% increase in C-index to 0.84 and 0.78 for 30-day and 180-day all-cause mortality, respectively, and cNRI of 0.86 95% CI [0.55-1.11] and 0.76 95% CI [0.57-0.87]. Prognostic gain was modest for the 30-day death/rehospitalization for cardiovascular or renal causes endpoint. Comparative time-dependent AUC analysis indicated that late measurements provided superior accuracy for the prediction of all-cause mortality over 180 days, with few exceptions including BUN and galectin-3. However, the predictive value of most biomarkers showed a diminishing pattern over time irrespective of moment of measurement. CONCLUSIONS: Multimarker models significantly improve risk prediction. Subsequent measurements, beyond admission, are needed for majority of biomarkers to maximize prognostic value over time, particularly in the long term.
Authors: Olga G Grushko; Steven Cho; Ashley M Tate; Robert S Rosenson; David J Pinsky; Jacob M Haus; Scott L Hummel; Sascha N Goonewardena Journal: Cardiovasc Drugs Ther Date: 2022-10-19 Impact factor: 3.947
Authors: Jasper Tromp; Mohsin A F Khan; IJsbrand T Klip; Sven Meyer; Rudolf A de Boer; Tiny Jaarsma; Hans Hillege; Dirk J van Veldhuisen; Peter van der Meer; Adriaan A Voors Journal: J Am Heart Assoc Date: 2017-03-30 Impact factor: 5.501
Authors: Claes Held; Harvey D White; Ralph A H Stewart; Andrzej Budaj; Christopher P Cannon; Judith S Hochman; Wolfgang Koenig; Agneta Siegbahn; Philippe Gabriel Steg; Joseph Soffer; W Douglas Weaver; Ollie Östlund; Lars Wallentin Journal: J Am Heart Assoc Date: 2017-10-24 Impact factor: 5.501
Authors: Licette C Y Liu; Mattia A E Valente; Douwe Postmus; Christopher M O'Connor; Marco Metra; Howard C Dittrich; Piotr Ponikowski; John R Teerlink; Gad Cotter; Beth Davison; John G F Cleland; Michael M Givertz; Daniel M Bloomfield; Dirk J van Veldhuisen; Hans L Hillege; Peter van der Meer; Adriaan A Voors Journal: Cardiovasc Drugs Ther Date: 2017-06 Impact factor: 3.727