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CLINICAL RESEARCH: HEART FAILURE

Improving the Diagnosis of Acute Heart Failure Using a Validated Prediction Model

Brian Steinhart, MD^_*,,_*, Kevin E. Thorpe, MMath^,_**, Ahmed M. Bayoumi, MD^_*,,||,, Gordon Moe, MD^_*,,¶, James L. Januzzi, Jr, MD and C. David Mazer, MD^,,#

^_* Department of Medicine, Saint Michael's Hospital, Toronto, Ontario, Canada
Department of Anesthesia and Critical Care Medicine, Saint Michael's Hospital, Toronto, Ontario, Canada
Keenan Research Center/Li Ka Shing Knowledge Institute, Saint Michael's Hospital, Toronto, Ontario, Canada
Department of Medicine, Division of Emergency Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
^|| Department of Medicine, Division of General Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
^¶ Department of Medicine, Division of Cardiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
^# Departments of Anesthesia and Physiology, Faculty of Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
^_** Faculty of Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
Faculty of Medicine, Department of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

Manuscript received December 10, 2008; revised manuscript received March 30, 2009, accepted May 6, 2009.

^_* Reprint requests and correspondence: Dr. Brian Steinhart, 1-008c Shuter Wing, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada (Email: steinhartb{at}smh.toronto.on.ca).

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 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.

Key Words: acute heart failure • diagnosis • natriuretic peptide • prediction model • Bayesian theorem

Abbreviations and Acronyms   AHF = acute heart failure   BNP = B-type natriuretic peptide   CI = confidence interval   ED = emergency department   IDI = integrated discrimination improvement   LR = likelihood ratio   NRI = net reclassification improvement   NT-proBNP = N-terminal pro–B-type natriuretic peptide   ROC = receiver-operator characteristic

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