The new tool 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.

The new tool utilises the combination of three simple measures obtained through laboratory blood tests and by measuring vital signs. "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, professor of cardiology at the David Geffen School of Medicine at UCLA.

Using data from a national registry of over 100,000 heart failure patients, researchers developed a risk model after analysing 33,046 hospitalisations. 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 hospitalisations.

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.”

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 categorise patients.; Source: University of California, Los Angeles (UCLA)