Improving the diagnosis of acute heart failure using a validated prediction model

We sought to derive and validate a prediction model by using N-terminal pro-B-type natriuretic peptide (NT-proBNP) and clinical variables to improve the diagnosis of acute heart failure (AHF).

The optimal way of using natriuretic peptides to enhance the diagnosis of AHF remains uncertain.

Physician estimates of probability of AHF in 500 patients treated in the emergency department from the multicenter IMPROVE CHF (Improved Management of Patients With Congestive Heart Failure) trial recruited between December 2004 and December 2005 were classified into low (0% to 20%), intermediate (21% to 79%), or high (80% to 100%) probability for AHF and then compared with the blinded adjudicated AHF diagnosis. Likelihood ratios were calculated and multiple logistic regression incorporated covariates into an AHF prediction model that was validated internally by the use of bootstrapping and externally by applying the model to another 573 patients from the separate PRIDE (N-Terminal Pro-BNP Investigation of Dyspnea in the Emergency Department) study of the use of NT-proBNP in patients with dyspnea.

Likelihood ratios for AHF with NT-proBNP were 0.11 (95% confidence interval [CI]: 0.06 to 0.19) for cut-point values <300 pg/ml; increasing to 3.43 (95% CI: 2.34 to 5.03) for values 2,700 to 8,099 pg/ml, and 12.80 (95% CI: 5.21 to 31.45) for values > or =8,100 pg/ml. Variables used to predict AHF were age, pre-test probability, and log NT-proBNP. When applied to the external data by use of its adjudicated final diagnosis as the gold standard, the model appropriately reclassified 44% of patients by intermediate clinical probability to either low or high probability of AHF with negligible (<2%) inappropriate redirection.

A diagnostic prediction model for AHF 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 AHF.