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. 2003 May 21;41(10):1797-804.
doi: 10.1016/s0735-1097(03)00309-7.

Clinical assessment identifies hemodynamic profiles that predict outcomes in patients admitted with heart failure

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Free article

Clinical assessment identifies hemodynamic profiles that predict outcomes in patients admitted with heart failure

Anju Nohria et al. J Am Coll Cardiol. .
Free article

Abstract

Objectives: This study was designed to determine the relevance of a proposed classification for advanced heart failure (HF). Profiles based on clinical assessment of congestion and perfusion at the time of hospitalization were compared with subsequent outcomes.

Background: Optimal design of therapy and trials for advanced HF remains limited by the lack of simple clinical profiles to characterize patients.

Methods: Prospective analysis was performed for 452 patients admitted to the cardiomyopathy service at the Brigham and Women's Hospital with a diagnosis of HF. Patients were classified by clinical assessment into four profiles: profile A, patients with no evidence of congestion or hypoperfusion (dry-warm, n = 123); profile B, congestion with adequate perfusion (wet-warm, n = 222); profile C, congestion and hypoperfusion (wet-cold, n = 91); and profile L, hypoperfusion without congestion (dry-cold, n = 16). Other standard predictors of outcome were included and patients were followed for the end points of death (n = 117) and death or urgent transplantation (n = 137) at one year.

Results: Survival analysis showed that clinical profiles predict outcomes in HF. Profiles B and C increase the risk of death plus urgent transplantation by univariate (hazard ratio [HR] 1.83, p = 0.02) and multivariate analyses (HR 2.48, p = 0.003). Moreover, clinical profiles add prognostic information even when limited to patients with New York Heart Association (NYHA) class III/IV symptoms (profile B: HR 2.23, p = 0.026; profile C: HR 2.73, p = 0.009).

Conclusions: Simple clinical assessment can be used to define profiles in patients admitted with HF. These profiles predict outcomes and may be used to guide therapy and identify populations for future investigation.

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