Risk Prediction in Patients With Heart Failure

Rahimi, Kazem; Bennett, Derrick; Conrad, Nathalie; Williams, Timothy; Basu, Joyee; Dwight, Jeremy; Woodward, Mark; Patel, Anushka; McMurray, John J.V.; MacMahon, Stephen

doi:10.1016/j.jchf.2014.04.008

Cited by 334 publications

(266 citation statements)

References 30 publications

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“…For this analysis, “compatible CPMs” were defined by the following characteristics: (1) the index condition in the derivation cohort was similar to the index condition in the validation cohort (here AHF), (2) CPM predicts an outcome captured in the validation cohort (here mortality), (3) all variables in the CPM were captured in the validation data sets and can be assigned a value, and (4) CPMs were derived in patient samples from a single world region (here, North America). We identified compatible models by reviewing a recently published systematic review of CPMs for HF 6. For this analysis, we present a sample of the compatible CPMs developed in North America that predict mortality at 3 different time points (in‐hospital, 60 day, and 1 year) following hospitalization for HF.…”

Section: Methodsmentioning

confidence: 99%

“…In the case of heart failure, CPMs have been proposed to inform decisions for advanced therapies and palliative care4 and also the common and costly admission decision for patients with acute heart failure (AHF) in the emergency department 5. While many different CPMs exist for predicting mortality for HF,6 CPM performance is often significantly better for the population on which the model was derived compared with similar yet distinct “validation” populations 7…”

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confidence: 99%

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Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Wessler

Ruthazer

Udelson

et al. 2017

JAHA

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BackgroundHeart failure clinical practice guidelines recommend applying validated clinical predictive models (CPMs) to support decision making. While CPMs are now widely available, the generalizability of heart failure CPMs is largely unknown.Methods and ResultsWe identified CPMs derived in North America that predict mortality for patients with acute heart failure and validated these models in different world regions to assess performance in a contemporary international clinical trial (N=4133) of patients with acute heart failure treated with guideline‐directed medical therapy. We performed independent external validations of 3 CPMs predicting in‐hospital mortality, 60‐day mortality, and 1‐year mortality, respectively. CPM discrimination decreased in all regional validation cohorts. The median change in area under the receiver operating curve was −0.09 (range −0.05 to −0.23). Regional calibration was highly variable (90th percentile of absolute difference between smoothed observed and predicted values range <1% to >50%). Calibration remained poor after global recalibrations; however, region‐specific recalibration procedures significantly improved regional performance (recalibrated 90th percentile of absolute difference range <1% to 5% across all regions and all models).ConclusionsAcute heart failure CPM discrimination and calibration vary substantially across different world regions; region‐specific (as opposed to global) recalibration techniques are needed to improve CPM calibration.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Wessler

Ruthazer

Udelson

et al. 2017

JAHA

View full text Add to dashboard Cite

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“…Furthermore, although their performance seems acceptable at the population level, they do not reliably predict one-year outcome of an individual patient [26]. The discriminatory ability of the models for prediction of HF hospitalisation appears to be even lower than that for estimation of the risk of death [20,21]. Finally, no evidence from randomised clinical trials exists to support the superiority of any of these models in qualification for HTx or any other specific therapy.…”

Section: Risk Stratification In Heart Failurementioning

confidence: 95%

“…Understandably, the abundance of those variables: 1) derives from complex pathophysiological pathways of HF development as well as from the fact that advanced HF affects function of other critical organs, and 2) denotes the need for more comprehensive means of assessment of prognosis in these patients because no single parameter is sufficient on its own. Thus, different risk scores, encompassing various numbers of predictive variables, have been proposed for risk stratification in HF [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. A recent meta-analysis reported as many as 117 models, using 249 different variables [20].…”

Section: Risk Stratification In Heart Failurementioning

confidence: 99%

“…Other strong predictors of death in HF, consistently reported across different models, include diabetes, BNP/NT-proBNP concentration, weight or body mass index, and exercise capacity [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Of all prognostic variables, LVEF and NYHA class belong to the most useful and powerful predictors of long-term HF outcomes.…”

Section: Genetic Testingmentioning

confidence: 99%

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Prognostic factors in heart failure — are they all equally important?

2017

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Heart Failure With Reduced Ejection Fraction

Murphy

Ibrahim

Januzzi

2020

JAMA

495

325

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ImportanceWorldwide, the burden of heart failure has increased to an estimated 23 million people, and approximately 50% of cases are HF with reduced ejection fraction (HFrEF).ObservationsHeart failure is a clinical syndrome characterized by dyspnea or exertional limitation due to impairment of ventricular filling or ejection of blood or both. HFrEF occurs when the left ventricular ejection fraction (LVEF) is 40% or less and is accompanied by progressive left ventricular dilatation and adverse cardiac remodeling. Assessment for heart failure begins with obtaining a medical history and physical examination. Also central to diagnosis are elevated natriuretic peptides above age- and context-specific thresholds and identification of left ventricular systolic dysfunction with LVEF of 40% or less as measured by echocardiography. Treatment strategies include the use of diuretics to relieve symptoms and application of an expanding armamentarium of disease-modifying drug and device therapies. Unless there are specific contraindications, patients with HFrEF should be treated with a β-blocker and one of an angiotensin receptor–neprilysin inhibitor, angiotensin-converting enzyme inhibitor, or angiotensin receptor blocker as foundational therapy, with addition of a mineralocorticoid receptor antagonist in patients with persistent symptoms. Ivabradine and hydralazine/isosorbide dinitrate also have a role in the care of certain patients with HFrEF. More recently, sodium-glucose cotransporter 2 (SGLT2) inhibitors have further improved disease outcomes, significantly reducing cardiovascular and all-cause mortality irrespective of diabetes status, and vericiguat, a soluble guanylate cyclase stimulator, reduces heart failure hospitalization in high-risk patients with HFrEF. Device therapies may be beneficial in specific subpopulations, such as cardiac resynchronization therapy in patients with interventricular dyssynchrony, transcatheter mitral valve repair in patients with severe secondary mitral regurgitation, and implantable cardiac defibrillators in patients with more severe left ventricular dysfunction particularly of ischemic etiology.Conclusions and RelevanceHFrEF is a major public health concern with substantial morbidity and mortality. The management of HFrEF has seen significant scientific breakthrough in recent decades, and the ability to alter the natural history of the disease has never been better. Recent developments include SGLT2 inhibitors, vericiguat, and transcatheter mitral valve repair, all of which incrementally improve prognosis beyond foundational neurohormonal therapies. Disease morbidity and mortality remain high, with a 5-year survival rate of 25% after hospitalization for HFrEF.

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Risk Prediction in Patients With Heart Failure

Abstract: There are several clinically useful and well-validated death prediction models in patients with heart failure. Although the studies differed in many respects, the models largely included a few common markers of risk.

Cited by 334 publications

References 30 publications

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Prognostic factors in heart failure — are they all equally important?

Heart Failure With Reduced Ejection Fraction

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