Model Can Predict Likelihood of Acute Heart Failure
Study suggests model may be particularly useful for patients with intermediate probability
THURSDAY, Oct. 8 (HealthDay News) -- A mathematical model that considers clinical variables and levels of a biomarker can predict the likelihood of heart failure, particularly in patients judged as having intermediate probability, according to a study in the Oct. 13 issue of the Journal of the American College of Cardiology.
Brian Steinhart, M.D., from Saint Michael's Hospital in Toronto, and colleagues developed a model to predict acute heart failure using levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP) and clinical variables using data from 500 patients suspected of having acute heart failure from a previous clinical trial. The model was validated using data from a separate group of 573 patients with dyspnea from another trial.
The researchers found that the likelihood ratios of acute heart failure increased with increasing levels of NT-proBNP, from 0.11 at less than 300 pg/mL to 12.80 at 8,100 pg/mL or greater. A model using age, pre-test probability, and log NT-proBNP was able to appropriately reclassify 44 percent of patients who had been judged to have an intermediate probability of heart failure (21 to 79 percent) as either low or high probability with less than two percent inappropriate redirection.
"A diagnostic prediction model for acute heart failure that incorporates both clinical assessment and NT-proBNP has been derived and validated and has excellent diagnostic accuracy, especially in cases with indeterminate likelihood for acute heart failure," Steinhart and colleagues conclude.
One author received grants from Roche Diagnostics and Siemens, and the trials were funded by Roche Diagnostics.