Optimizing clinical use of biomarkers in high-risk acute heart failure patients

被引:85
作者
Demissei, Biniyam G. [1 ,2 ]
Valente, Mattia A. E. [1 ]
Cleland, John G. [3 ]
O'Connor, Christopher M. [4 ]
Metra, Marco [5 ]
Ponikowski, Piotr [6 ]
Teerlink, John R. [7 ,8 ]
Cotter, Gad [9 ]
Davison, Beth [9 ]
Givertz, Michael M. [10 ]
Bloomfield, Daniel M. [11 ]
Dittrich, Howard [12 ]
van der Meer, Peter [1 ]
van Veldhuisen, Dirk J. [1 ]
Hillege, Hans L. [1 ,2 ]
Voors, Adriaan A. [1 ]
机构
[1] Univ Groningen, Univ Med Ctr Groningen, Dept Cardiol, Hanzepl 1, NL-9713 GZ Groningen, Netherlands
[2] Univ Groningen, Univ Med Ctr Groningen, Dept Epidemiol, Groningen, Netherlands
[3] Univ London Imperial Coll Sci Technol & Med, London, England
[4] Duke Univ, Med Ctr, Durham, NC USA
[5] Univ Brescia, Brescia, Italy
[6] Med Univ, Clin Mil Hosp, Wroclaw, Poland
[7] Univ Calif San Francisco, San Francisco, CA 94143 USA
[8] San Francisco VA Med Ctr, San Francisco, CA USA
[9] Momentum Res, Durham, NC USA
[10] Brigham & Womens Hosp, 75 Francis St, Boston, MA 02115 USA
[11] Merck Res Labs, Rahway, NJ USA
[12] Univ Iowa, Abboud Cardiovasc Res Ctr, Carver Coll Med, Iowa City, IA 52242 USA
关键词
Acute heart failure; Prognosis; Risk stratification; Multimarker strategy; Time-dependent AUC analysis; NATRIURETIC PEPTIDE; ACUTE DYSPNEA; SOLUBLE ST2; MORTALITY; HOSPITALIZATION; STRATIFICATION; PREDICTION; OUTCOMES; ROLOFYLLINE; ANTAGONIST;
D O I
10.1002/ejhf.443
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
AimThe 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 resultsA 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 180days, 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. ConclusionsMultimarker 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.
引用
收藏
页码:269 / 280
页数:12
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