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    Patent Pending

    Acute Decompensated Heart Failure National Registry (ADHERE) Algorithm

    Predicts in-hospital mortality in patients with heart failure (HF).
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    When to Use
    Pearls/Pitfalls
    Why Use

    Inpatients with acute decompensated heart failure.

    • The ADHERE Algorithm estimates in-hospital mortality in admitted patients with acute decompensated HF.
    • This model uses 3 variables (BUN, SBP, creatinine) to classify patients, but it does not allow more precise characterization of individual risk.
    • The ADHERE Algorithm can NOT predict intermediate- and long-term mortality risks.
    • The analysis of the ADHERE cohort also identified heart rate and age as significant independent predictors of risk according to the regression model, but these variables were omitted from the algorithm in order to simplify the model as a bedside tool.
    • The data used reflect individual hospitalization episodes, not individual patients, and multiple hospitalizations of the same patient may be entered into the registry as separate records.
    • Study results were based on a registry, the accuracy of which can be influenced by differences in disease assessment, treatment, and documentation patterns at participating institutions.

    Risk stratification, triage and optimization of medical management.

    About the Creator
    Dr. Gregg Fonarow
    Content Contributors
    • Chetana Pendkar, MBBS
    • Vijay Shetty, MBBS

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    Evidence
    Creator Insights

    Advice

    • If the algorithm 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

    lace index algorithm

    Evidence Appraisal

    • The Acute Decompensated Heart Failure National Registry (ADHERE) of patients hospitalized with a primary diagnosis of ADHF in 263 hospitals in the United States was studied and the baseline characteristics and main outcomes of the 33,046 hospitalization episodes (October 2001-February 2003) were used to develop the model and the 32,229 hospitalization episodes (March-July 2003) were used to validate the model.
    • BUN, systolic BP, heart rate, and age were found to be the most significant predictors of mortality.
    • ADHF patients were categorized into low, intermediate, and high risk for in-hospital mortality using vital sign and laboratory data obtained on hospital admission, with mortality ranging from 2.1% to 21.9%.
    • The CART (classification and regression tree) method was used for analyzing the study cohorts, which is an empirical, statistical technique based on recursive partitioning analysis (separating populations by multiple variables). 39 variables were evaluated by the CART method.
    Dr. Gregg Fonarow

    From the Creator

    Why did you develop the ADHERE Algorithm? Was there a clinical experience that inspired you to create this tool for clinicians?
    There are close to 1 million hospitalizations for heart failure each year in the United States. Despite the public health burden of hospitalization for heart failure, models for the risk stratification of patients during admission for acute decompensated heart failure previously had not well established. Clinical risk prediction tools may be helpful in guiding medical decision making.
    What pearls, pitfalls and/or tips do you have for users of ADHERE? Are there cases when it has been applied, interpreted, or used inappropriately?
    The ADHERE registry reflects patients cared for by thousands of clinicians at hundreds of hospitals across the country and thus has an excellent chance to adjust for this variation and create a risk prediction model that is robust for most situations. However, this model may not apply to patients who are cared for in settings that deviate substantially from those in ADHERE. In addition, each patient’s actual risk may be influenced by many factors not measured or considered in this model. Therefore, this model enhances, not replaces, physician assessment. This use of this, or other, risk models have been recommended in the ACC/AHA 2013 Heart Failure Guidelines (Class IIA).
    What recommendations do you have for healthcare providers once they have applied the ADHERE algorithm? What are the next steps?
    Patients estimated to be at a lower risk may be managed with less intensive monitoring and therapies available on a telemetry unit or hospital ward, whereas a patient estimated to be at a higher risk may require more intensive management. The finding that indices of renal status are 2 of the 3 most important variables in providing the best mortality risk discrimination underscores the importance of renal function in hospitalized heart failure patients.
    How would you compare ADHERE with other assessments for mortality in heart failure?
    The ADHERE Risk Model was designed to assess in-hospital mortality risk and be highly adaptable to bedside use based on variables readily available upon presentation. Other risk models have addressed 7 day or 30 day mortality risk or utilized more detailed point scores or formulas.
    Any other comments on ADHERE?
    The mortality for patients hospitalized with heart failure provides a compelling indication to apply tools, such as the risk tool derived and validated in ADHERE, to improve the evaluation and, potentially, management and outcomes of these patients.

    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