Key result
Novel AHF prediction model appropriately reclassifies ~44% of intermediate-risk patients with minimal error.
Why the study?
The optimal way of using natriuretic peptides to enhance the diagnosis of AHF remained uncertain, motivating the derivation and validation of a prediction model combining NT-proBNP and clinical variables.
Does a prediction model incorporating age, clinical pre-test probability, and log NT-proBNP improve the diagnosis of acute heart failure in emergency department patients presenting with dyspnea?
Observational (n=1,073)
Blinded adjudicated outcome
Yes
Does a prediction model incorporating age, clinical pre-test probability, and log NT-proBNP improve the diagnosis of acute heart failure in emergency department patients presenting with dyspnea?
A validated prediction model combining age, clinical pre-test probability, and NT-proBNP accurately reclassifies 44% of emergency department patients with an intermediate clinical probability of acute heart failure.
Answers are grounded in this paper and cite their sources.
NT-proBNP model may aid ED AHF diagnosis; leaves open whether it improves outcomes in prospective validation.
Objectives 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). Background The optimal way of using natriuretic peptides to enhance the diagnosis of AHF remains uncertain. Methods 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. Results 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. Conclusions 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.
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Steinhart et al. (2009) conducted an observational in Acute heart failure (AHF) (n=1,073). AHF prediction model (age, pre-test probability, log NT-proBNP) vs. Blinded adjudicated AHF diagnosis was evaluated on Appropriate reclassification of patients with intermediate clinical probability to low or high probability of AHF (LR 12.80 (for NT-proBNP >=8,100 pg/ml), 95% CI 5.21-31.45). An AHF prediction model combining age, pre-test probability, and log NT-proBNP appropriately reclassified 44% of intermediate-probability patients with <2% inappropriate redirection.
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