Researchers have developed the first mathematical model in cardiology and emergency medicine that enables clinicians to more quickly and reliably diagnose acute heart failure (AHF) in emergency room patients.
According to a news release from St. Michael’s Hospital, a teaching hospital affiliated with the University of Toronto, researchers there have developed “the first mathematical model in cardiology and emergency medicine to more quickly and reliably diagnose acute heart failure (AHF) in emergency room patients.”
Lead researcher Brian Steinhart, MD, said that the new model “aims to ensure early correct diagnosis and treatment, which allows for shorter emergency department stay for these patients and could lead to improved health outcomes and better access to precious emergency department resources.”
optimal way of using natriuretic peptides to enhance the diagnosis of AHF remains uncertain,” they 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 AHF.
In study results published in the Journal of the American College of Cardiology, the researchers noted that because the “
who were treated in the emergency department into three groups, based on the clinicians’ assessment of the patients’ probability for AHF: low (0% to 20%), intermediate (21% to 79%), and high (80% to 100%). After comparing the cohort to the actual (blinded adjudicated) AHF diagnosis, researchers calculated likelihood ratios 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.”
500 patients from the IMPROVE-CHF (Improved Management of Patients with Congestive Heart Failure) trial
For the study, physicians assigned
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.”
Journal of the American College of Cardiology article notes that, “when
According to Steinhart, the model “does not require extensive clinical information, which makes it relatively simple-to-use. When the result is greater than 80 percent probability for heart failure, it suggests that the physician should treat for AHF and when it is less than 20 percent, the physician should be looking elsewhere for diagnosis.”