Prognostic scores in heart failure — Critical appraisal and practical use

Ferrero, Paolo; Iacovoni, Attilio; D’Elia, Emilia; Vaduganathan, Muthiah; Gavazzi, Antonello; Senni, Michele

doi:10.1016/j.ijcard.2015.03.154

Cited by 42 publications

(44 citation statements)

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“…First, patients are assessed at admission or at the emergency service, which generally prioritizes the assessment of a very short- term risk (during hospitalization) or a medium-term risk (from 2 to 6 months after discharge), as the identification of high-risk patients contributes to a closer follow-up and more intensive therapy in this period when patients are more vulnerable. 18 For this reason, the variables of these models may be similar but are slightly different as compared with those of chronic HF scores, in emphasizing easy, rapidly accessible clinical, demographic and analytical factors. 18 …”

Section: Discussionmentioning

confidence: 99%

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Acute Heart Failure Registry: Risk Assessment Model in Decompensated Heart Failure

Delgado¹,

Rodrigues²,

Baptista³

et al. 2016

Arquivos Brasileiros De Cardiologia

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BackgroundHeart failure (HF) is a highly prevalent syndrome. Although the long-term prognostic factors have been identified in chronic HF, this information is scarcer with respect to patients with acute HF. despite available data in the literature on long-term prognostic factors in chronic HF, data on acute HF patients are more scarce.ObjectivesTo develop a predictor of unfavorable prognostic events in patients hospitalized for acute HF syndromes, and to characterize a group at higher risk regarding their clinical characteristics, treatment and outcomes.Methodscohort study of 600 patients admitted for acute HF, defined according to the European Society of Cardiology criteria. Primary endpoint for score derivation was defined as all-cause mortality and / or rehospitalization for HF at 12 months. For score validation, the following endpoints were used: all-cause mortality and / or readmission for HF at 6, 12 and 24 months. The exclusion criteria were: high output HF; patients with acute myocardial infraction, acute myocarditis, infectious endocarditis, pulmonary infection, pulmonary artery hypertension and severe mitral stenosis.Results505 patients were included, and prognostic predicting factors at 12 months were identified. One or two points were assigned according to the odds ratio (OR) obtained (p < 0.05). After the total score value was determined, a 4-point cut-off was determined for each ROC curve at 12 months. Two groups were formed according to the number of points, group A < 4 points, and group B = 4 points. Group B was composed of older patients, with higher number of comorbidities and predictors of the combined endpoint at 6, 12 and 24 months, as linearly represented in the survival curves (Log rank).ConclusionsThis risk score enabled the identification of a group with worse prognosis at 12 months.

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Section: Discussionmentioning

confidence: 99%

“…18 For this reason, the variables of these models may be similar but are slightly different as compared with those of chronic HF scores, in emphasizing easy, rapidly accessible clinical, demographic and analytical factors. 18 …”

Section: Discussionmentioning

confidence: 99%

Acute Heart Failure Registry: Risk Assessment Model in Decompensated Heart Failure

Delgado¹,

Rodrigues²,

Baptista³

et al. 2016

Arquivos Brasileiros De Cardiologia

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“…9 Indeed, there is a prevailing wisdom that higher-risk patients warrant more aggressive therapy via increased that a single risk estimate from the SHFM has declining predictive performance over time since assessment that is largely attributed to the model's modifiable components, especially hemoglobin, sodium, SBP, and EF. SHFM risk estimates provided poor discrimination of mortality risk beyond 12 months from assessment, suggesting that proper longitudinal use of the SHFM requires at least annual updating of the modifiable elements to provide valid risk estimates.…”

Section: Using the Shfmmentioning

confidence: 99%

Applying the Seattle Heart Failure Model in the Office Setting in the Era of Electronic Medical Records

Williams

Agarwal

2018

Circ J

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The Seattle Heart Failure Model (SHFM) is arguably the most popular and well-validated HF prognostic model. 4,8,9,12,13 Although developed in a small, clinical trial cohort limited to HF patients with an ejection fraction (EF) <35% and New York Heart Association (NYHA) Class IIIB or IV, the SHFM has been externally validated in both more and less healthy HF cohorts with variable predictive performance. 12,14-19 However, to our knowledge, the SHFM has not been validated among HF patients in the context of the office visit. The transportability of the SHFM to the office setting is questionable given large discrepancies in disease severity between the SHFM derivation cohort and HF patients in the office environment. Accordingly, in the present study we assessed several predictive properties of the SHFM in the office environment to evaluate its potential utility in this common setting. In addition to common metrics of predictive performance, such as discrimination and calibration, we also sought to determine the "warranty period" of a baseline risk estimate from the SHFM. Because several of the SHFM elements reflect transient, modifiable states, we hypothesized that the predictive value of a single baseline risk estimate would wane over time. Finally, risk thresholds are proposed for labeling HF patients as low and high risk in the office setting. Despite therapeutic advances, heart failure (HF) remains an extremely morbid condition associated with frequent distressing symptoms and a 5-year mortality rate approaching 50% following diagnosis. 1-4 Although aggregate experience informs the expected clinical trajectory, the subsequent clinical course of any single HF patient is difficult to predict, particularly in the office setting, where patients are more likely to be relatively stable and have no obvious signals serving as impetus to alter care. 1,5-8 Prognosis estimation can help guide the management of such patients, yet relying solely on clinical intuition for such a task can be challenging. 4-6,9,10 To make prognosis estimation more objective, several risk prediction models have been developed that transform a collection of prognostic determinants into a single numeric estimate of absolute mortality risk over a specified time frame. 4,9-11 Risk prediction models enable better placement of individuals along the risk continuum, allowing, in theory, more judicious and cost-efficient application of HF therapies. 6,11 Furthermore, electronic medical record (EMR) systems should make such models easier to apply in the future by serving as a repository for necessary data elements, providing an environment for behind-the-scenes calculations of quantitative risk estimates, and displaying quantitative model output with accompanying management recommendations as a form of clinical decision support. Background: Prediction models such as the Seattle Heart Failure Model (SHFM) can help guide management of heart failure (HF) patients, but the SHFM has not been validated in the office environment. This retrospective cohort study...

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“…Factors most consistently shown to relate to mortality among HF patients are age, sex, blood pressure, left ventricular ejection fraction, functional class and exercise capacity (the latter both implying frailty), natriuretic peptide levels, serum sodium level, diabetes and renal function. Despite the above, a major limitation of current all‐cause mortality risk scores in HF (Ferrero et al., ) lies in their non‐incorporation of current treatment changes such as widespread use of cardiac resynchronization therapy and introduction of sacubitril/valsartan combination.…”

Section: The Multifactorial Approachmentioning

confidence: 99%