New bedside tool gauges mortality risk in heart failure patients
For the first time, UCLA researchers have developed a new evaluation tool that can predict mortality risk in patients hospitalized with heart failure. The new tool -- used right at the bedside -- will help clinicians quickly decide upon hospital admission which patients are at a greater mortality risk that may require higher monitoring and earlier, more intensive intervention.
Published in the February 2, 2005 edition of JAMA, the new tool utilizes the combination of three simple measures obtained through laboratory blood tests and by measuring vital signs. Heart failure is a condition that affects five million Americans and is the leading cause of hospitalization for those over age 65.
"The new tool is a first for the treatment of acute heart failure, and offers a simple quick way for clinicians to assess mortality risk upon hospital admission and quickly decide on a treatment strategy," said Dr. Gregg C. Fonarow, lead study author, The Eliot Corday Chair in Cardiovascular Medicine and Science, professor of cardiology, David Geffen School of Medicine at UCLA and director, Ahmanson-UCLA Cardiomyopathy Center.
Using data from a national registry of over 100,000 heart failure patients called the Acute Decompensated Heart Failure National Registry (ADHEREŽ), researchers developed a risk model after analyzing 33,046 hospitalizations. The model was developed using a relatively new statistical technique know as Classification and Regression Tree Analysis (CART). The validity of the model was then tested using data from an additional 32,229 hospitalizations.
Researchers evaluated 39 possible factors as survival indicators upon hospital admission and found that the best single predictor for mortality was a high blood urea nitrogen level, (above 43 mg/dL), followed by a low systolic blood pressure (above 115 mm Hg) and a high serum creatinine (higher than 2.75 mg/dL).
"This validated risk tree provides clinicians with a practical, easy tool to use at the bedside" said Fonarow. "We were surprised that the risk tool using only three variables was able to dramatically distinguish between low, intermediate, and high risk heart failure patients."
The overall mortality risk for patients hospitalized with acute heart failure was 4.1 percent. The model determined mortality risk levels starting from low risk at 2.1 percent, up to 21.9 percent for patients at the highest mortality risk.
Fonarow adds that two of the top mortality risk indicators -- blood urea nitrogen level and serum creatinine -- involve renal or kidney function, which emphasizes the importance of this area in heart failure patients that may warrant further study.
The new risk evaluation tool is now ready for clinical use, according to Fonarow, and can be applied at hospitals across the country. In addition, the new model may provide a more effective way to design clinical trials for evaluating heart failure therapies since researchers now have the ability to easily categorize patients by high and low mortality risk.
The study was funded by Scios, Inc., a biopharmaceutical company and member of the Johnson and Johnson Family of Companies. The ADHERE Registry, also funded by Scios, Inc., collects observational data from across the United States in order to track and study the medical management of patients hospitalized with acute heart failure. ADHERE is overseen by an independent scientific advisory committee of nationally recognized heart failure experts, including the study authors.
Additional study authors include: Dr. Kirkwood F. Adams, Jr., Division of Cardiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Dr. William T. Abraham, Department of Cardiology, The Ohio State University Medical Center, Columbus, Ohio; Dr. Clyde W. Yancy, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, Texas; and W. John Boscardin, Ph.D., Department of Biostatisics, University of California, Los Angeles.
Source: Eurekalert & othersLast reviewed: By John M. Grohol, Psy.D. on 21 Feb 2009
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