Literature DB >> 19185637

Incremental value of renal function in risk prediction with the Seattle Heart Failure Model.

Grigorios Giamouzis1, Andreas P Kalogeropoulos, Vasiliki V Georgiopoulou, Syed A Agha, Mohammad A Rashad, Sonjoy R Laskar, Andrew L Smith, Javed Butler.   

Abstract

BACKGROUND: Impaired renal function portends poor heart failure (HF) outcomes. The Seattle Heart Failure Score (SHFS), a multimarker risk assessment tool, however does not incorporate renal function. In this study, we assessed the incremental value of renal function over the SHFS in patients with advanced HF on contemporary optimal treatment.
METHODS: Blood urea nitrogen (BUN), serum creatinine (sCr), BUN/sCr ratio, and estimated glomerular filtration rate were assessed in survival models with SHFS as the base model among 443 patients with HF (52 +/- 12 years, male 68.5%, white 52.4%, ejection fraction 0.18 +/- 0.08). Incremental value of renal function was assessed by changes in the likelihood ratio chi(2) and the area under the receiver operating characteristic curves for 1-, 2-, and 3-year event prediction.
RESULTS: During a median follow-up of 21 months, 108 (24.5%) of 443 patients had an event (death [n = 92], urgent transplantation [n = 13], or ventricular assist device implantation [n = 3]). All renal parameters individually were associated with outcome (BUN, P < .001; sCr, P < .001; BUN/sCr ratio, P = .006; and estimated glomerular filtration rate, P = .006); however, only BUN was an independent predictor of events in multivariable analyses. Addition of BUN improved the predictive ability of SHFS (Deltalikelihood ratio chi(2) 5.03, P = .025); however, the increase in the area under the receiver operating characteristic curve was marginal (year 1, 0.786 to 0.791; year 2, 0.732 to 0.741; year 3, 0.745 to 0.754; all P > .2).
CONCLUSION: Among the various renal function parameters, BUN had the strongest association with outcomes in patients with advanced HF. However, the incremental value of renal function over the SHFS for risk determination was marginal.

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Year:  2008        PMID: 19185637     DOI: 10.1016/j.ahj.2008.10.007

Source DB:  PubMed          Journal:  Am Heart J        ISSN: 0002-8703            Impact factor:   4.749


  11 in total

1.  An Appraisal of Biomarker-Based Risk-Scoring Models in Chronic Heart Failure: Which One Is Best?

Authors:  Barbara S Doumouras; Douglas S Lee; Wayne C Levy; Ana C Alba
Journal:  Curr Heart Fail Rep       Date:  2018-02

Review 2.  The cardiorenal syndrome in heart failure: cardiac? renal? syndrome?

Authors:  Filippos Triposkiadis; Randall C Starling; Harisios Boudoulas; Gregory Giamouzis; Javed Butler
Journal:  Heart Fail Rev       Date:  2012-05       Impact factor: 4.214

Review 3.  Epidemiology and importance of renal dysfunction in heart failure patients.

Authors:  Gregory Giamouzis; Andreas P Kalogeropoulos; Javed Butler; Georgios Karayannis; Vasiliki V Georgiopoulou; John Skoularigis; Filippos Triposkiadis
Journal:  Curr Heart Fail Rep       Date:  2013-12

4.  From statistical significance to clinical relevance: A simple algorithm to integrate brain natriuretic peptide and the Seattle Heart Failure Model for risk stratification in heart failure.

Authors:  Omar F AbouEzzeddine; Benjamin French; Sultan A Mirzoyev; Allan S Jaffe; Wayne C Levy; James C Fang; Nancy K Sweitzer; Thomas P Cappola; Margaret M Redfield
Journal:  J Heart Lung Transplant       Date:  2016-01-15       Impact factor: 10.247

Review 5.  The emerging role of Galectin-3 and ST2 in heart failure: practical considerations and pitfalls using novel biomarkers.

Authors:  George Karayannis; Filippos Triposkiadis; John Skoularigis; Panagiotis Georgoulias; Javed Butler; Gregory Giamouzis
Journal:  Curr Heart Fail Rep       Date:  2013-12

6.  Relationship between blood urea nitrogen-to-creatinine ratio at hospital admission and long-term mortality in patients with acute decompensated heart failure.

Authors:  Azusa Murata; Takatoshi Kasai; Yuya Matsue; Hiroki Matsumoto; Shoichiro Yatsu; Takao Kato; Shoko Suda; Masaru Hiki; Atsutoshi Takagi; Hiroyuki Daida
Journal:  Heart Vessels       Date:  2018-02-07       Impact factor: 2.037

Review 7.  Prognostic implications of renal dysfunction in patients hospitalized with heart failure: data from the last decade of clinical investigations.

Authors:  Filippo Brandimarte; Muthiah Vaduganathan; Gian Francesco Mureddu; Giuseppe Cacciatore; Hani N Sabbah; Gregg C Fonarow; Steven R Goldsmith; Javed Butler; Francesco Fedele; Mihai Gheorghiade
Journal:  Heart Fail Rev       Date:  2013-03       Impact factor: 4.214

8.  Current status of mechanical circulatory support: a systematic review.

Authors:  Kyriakos Spiliopoulos; Gregory Giamouzis; George Karayannis; Dimos Karangelis; Stelios Koutsias; Andreas Kalogeropoulos; Vasiliki Georgiopoulou; John Skoularigis; Javed Butler; Filippos Triposkiadis
Journal:  Cardiol Res Pract       Date:  2012-08-26       Impact factor: 1.866

Review 9.  Prognostic scales in advanced heart failure.

Authors:  Wioletta Szczurek; Bożena Szyguła-Jurkiewicz; Łukasz Siedlecki; Mariusz Gąsior
Journal:  Kardiochir Torakochirurgia Pol       Date:  2018-09-24

10.  Heart failure 2012.

Authors:  Gregory Giamouzis; George Giannakoulas; Javed Butler; John A Elefteriades; Carsten Tschöpe; Filippos Triposkiadis
Journal:  Cardiol Res Pract       Date:  2012-12-20       Impact factor: 1.866

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