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    Patent Pending

    GWTG-Heart Failure Risk Score

    Predicts in-hospital all-cause heart failure mortality.
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    When to Use
    Pearls/Pitfalls
    Why Use

    Admitted patients with LV dysfunction (systolic or diastolic).

    • The GWTG-HF Risk Score estimates all-cause in-hospital mortality in admitted patients with heart failure.
    • The risk score can be used to guide medical therapy in high-risk patients.
    • Makes use of routinely collected clinical data to predict in-hospital mortality for patients hospitalized with HF.
    • Scores range from 0 to 100, with scores 0-33 having <1% probability of death to scores over 79 having >50% probability of death.
    • Can be used in patients with preserved or impaired LV systolic function.

    Improves quality of care provided to patients hospitalized with HF by optimizing treatment options based on risk score.

    mm Hg
    mg/dL
    mEq/L
    years
    beats/min
    About the Creator
    Dr. Gregg Fonarow
    Content Contributors
    • Chetana Pendkar, MBBS
    • Vijay Shetty, MBBS

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    Next Steps
    Evidence
    Creator Insights

    Advice

    • If the score identifies the patient to be high risk, early consultation to cardiology is crucial to appropriately treat these patients and reduce risk of readmission.
    • Appropriate planning and care coordination between multidisciplinary teams, and early and frequent follow-up in the cardiologist’s office is essential.
    • Evidence-based guideline directed medical therapy is meant to be used for both high and low risk patients with CHF. However, in the era of resource constraints, these limited resources may not be allocated to low risk patients.

    Formula

    Addition of lab and demographic values assigned point values.

    Facts & Figures

    Score interpretation:

    Total Score Predicted Mortality
    0-33 <1%
    34-50 1-5%
    51-57 5-10%
    58-61 10-15%
    62-65 15-20%
    66-70 20-30%
    71-74 30-40%
    75-78 40-50%
    ≥79 >50%

    Evidence Appraisal

    • A cohort of 39,783 patients admitted January 1, 2005, to June 26, 2007, to 198 hospitals participating in GWTG-HF was divided into derivation (27,850) and validation (11,933) samples using American Heart Association Get With the Guidelines–Heart Failure (GWTG-HF) program data.
    • Multivariable logistic regression identified predictors of in-hospital mortality in the derivation sample.
    • Analysis showed that age, systolic blood pressure, and BUN at the admission were most predictive variables of in-hospital mortality. Admission heart rate, serum sodium, presence of COPD, and non-black race also contributed modestly.
    • The score was validated in a separate cohort of 13,163 patients which was based on electronic health record data.
    • GWTG-HF was supported in part by GlaxoSmithKline.
    Dr. Gregg Fonarow

    From the Creator

    From Gregg Fonarow, MD, a contributing researcher to the GTWG-HF Risk Score for MDCalc:

    Why did you develop the GWTG-HF Risk Score? Was there a clinical experience that inspired you to create this tool for clinicians?
    In clinical practice, risk models may be useful to inform patient triage and treatment decisions. Patients hospitalized with heart failure provide a unique setting for such prognostic tools. Physicians often do not calibrate heart failure therapy to a patient’s risk for adverse outcomes, failing to deliver effective therapies to the highest risk patients, for whom the benefits of therapy are likely to be greatest. The ability to predict mortality risk could inform clinical decision-making, as a wide range of heart failure therapies are available, some of which are invasive and/or expensive. Objective prognostic information could guide the appropriate application of monitoring and treatment, potentially resulting in improvements in the quality of care delivered to and outcomes of patients hospitalized with heart failure. While other risk models have been developed, the objective of the GWTG-HF study was to derive and validate a predictive model for in-hospital mortality using readily available clinical data in a large contemporary and diverse population-based cohort of patients hospitalized with heart failure in almost 200 US hospitals.
    What pearls, pitfalls and/or tips do you have for users of GWTG-HF? Are there cases when it has been applied, interpreted, or used inappropriately?
    This information in this GWTG-HF Risk Model has been used to guide the development of a user-friendly and accessible risk score for in-hospital mortality for heart failure. This risk model has used by the more the 600 hospitals currently participating in GWTG-HF.
    What recommendations do you have for healthcare providers once they have applied the GWTG-HF Risk Score? What are the next steps? For example, scores range from 0-100 with corresponding ranges of mortality risk—might a clinician stratify their patient into low, intermediate, or high risk like in ADHERE and subsequently make treatment decisions based on that stratification?
    An accurate understanding of prognosis is fundamental to many clinical decisions in patients with HF. However, some studies have shown few than one fifth of clinicians caring for patients with heart failure believed they could accurately predict death, and, in fact, clinicians frequently incorrectly estimate risk in patients with heart failure.
    The GWTG-HF risk score can be used at the point of care to quantify patient risk, thus facilitating patient triage and encouraging the use of evidence-based therapy in the highest-risk patients. The score could be used to increase the use of recommended medical therapy in high-risk patients and reduce resource utilization in those at low risk.
    How would you compare GWTG-HF with other assessments for mortality in heart failure?
    The GWTG-HF risk score for in-hospital mortality has several strengths and differs from other HF mortality risk models in many respects. It was derived using a contemporary cohort of population-based patients with diverse demographic characteristics and a wide range of comorbidities and included patients regardless of LVEF. Thus, this model is widely applicable. Additionally, it includes a relatively small number of variables routinely collected at the time of admission. Use of available clinical information makes the GWTG-HF score less susceptible to missing data. Several existing predictive models for long-term mortality in HF include more than 20 variables some of which are not frequently obtained in the clinical care of heart failure. This renders the estimation of risk complex and difficult to incorporate into routine clinical practice.
    Any other comments on GWTG-HF?
    Determining whether prospective application of this risk prediction score will favorably affect patient care and clinical outcomes should be the topic of future studies.

    About the Creator

    Gregg C. Fonarow, MD, is a professor of medicine and the director of the Ahmanson-UCLA Cardiomyopathy Center. He also serves as the co-director UCLA Preventative Cardiology Program and co-chief of the UCLA Division of Cardiology. Dr. Fonarow has published over 800 research studies and clinical trials in heart failure management, preventative cardiology and outcomes research.

    To view Dr. Gregg Fonarow's publications, visit PubMed

    Content Contributors
    • Chetana Pendkar, MBBS
    • Vijay Shetty, MBBS