Multivariate Risk Scores and Patient Outcomes in Advanced Heart Failure

Ketchum, Eric S.; Levy, Wayne C.

doi:10.1111/j.1751-7133.2011.00241.x

Cited by 30 publications

(17 citation statements)

References 88 publications

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“…This multi-variable statistical analysis lead to the formation of scores used in clinical practice, providing estimation of risk for mortality ( e.g. Heart Failure Survival Score [46], Get With the guidelines score [47], Seattle Heart Failure Model [48], EFFECT [49]), re-hospitalizations [50] and morbidity [51].…”

Section: Prediction Of Adverse Eventsmentioning

confidence: 99%

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

179

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Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

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Section: Prediction Of Adverse Eventsmentioning

confidence: 99%

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

179

View full text Add to dashboard Cite

show abstract

“…Reclassification was mainly achieved by the identification of additional high-risk individuals. In a recent review, Ketchum and Levy24 suggested that risk scores had an increasing role to play among patients with advanced heart failure whose survival has improved due to therapeutic and technological advances. They suggested that risk scores could be used to assist the selection of patients for transplantation, left ventricle assist devices and implantable cardioverter defibrillators.…”

Section: Do Biomarkers Add Value?mentioning

confidence: 99%

Almanac 2012: cardiovascular risk scores. The national society journals present selected research that has driven recent advances in clinical cardiology

Pell

2012

Heart

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“…27 Moreover, the SHFM has been shown to overestimate survival among sicker patients. 28,29 Like most of the previously developed prognostic models, 30 our model was not validated in a separate cohort of patients. It was internally validated using the bootstrap method.…”

Section: Limitationsmentioning

confidence: 99%

Amino‐Terminal Pro‐B‐Type Natriuretic Peptide for Risk Prediction in Acute Decompensated Heart Failure

Scrutinio¹,

Guida

Passantino³

et al. 2012

Congestive Heart Failure

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Amino-terminal pro-B-type natriuretic peptide (NT-proBNP) has been associated with prognosis in heart failure. The aim of this study was to assess whether NT-proBNP enhances risk prediction in acute decompensated heart failure (ADHF). The authors enrolled 453 patients hospitalized for ADHF. The primary outcome was 12-month mortality. Two separate multivariable predictive models were built by using standard variables-only and NT-proBNP concentrations. The models were internally validated using the bootstrapping method. Twelve-month mortality was 28.3%. There was no statistical evidence of overfit. Compared with the standard variables-only model, the NT-proBNP model had a better predictive performance as judged by the Nagelkerke R(2) (0.410 vs 0.374) and the Brier score (0.136 vs 0.141), which are measures of overall performance; the Akaike Information Criterion (399.2 vs 415.0), which is a tool for model selection; and the C index (0.844 vs 0.831), which is a measure of the discriminative ability. Both models were well calibrated, as judged by the Hosmer-Lemeshow chi-square test. Both models predicted 12-month mortality significantly better than the Enhanced Feedback for Effective Cardiology Treatment risk score. In conclusion, the NT-proBNP model improved risk prediction compared with the standard variables-only model.

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Multivariate Risk Scores and Patient Outcomes in Advanced Heart Failure

Cited by 30 publications

References 88 publications

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Almanac 2012: cardiovascular risk scores. The national society journals present selected research that has driven recent advances in clinical cardiology

Amino‐Terminal Pro‐B‐Type Natriuretic Peptide for Risk Prediction in Acute Decompensated Heart Failure

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