Final answer:
At the optimal cutpoint, the number of participants without heart failure correctly identified as not having the condition based on their BNP values is referred to as true negatives. This can be determined from study data using specificity metrics.
Step-by-step explanation:
The optimal cutpoint in a medical diagnostic test is a value that discriminates between those with and without a condition - in this case, heart failure (HF), with the highest possible sensitivity and specificity. The number of participants without HF who would be correctly diagnosed as not having HF based on their Brain Natriuretic Peptide (BNP) values at the optimal cutpoint refers to the true negative count.
To find this number, first, the optimal cutpoint for BNP has to be determined by analyzing the receiver operating characteristic (ROC) curve. Once the cutpoint is set, those with BNP values below this cutpoint would be classified as not having HF. The correct identification of non-HF participants as negative cases is known as the test's specificity. To know the exact number, you would need data from the study that includes individuals’ BNP values and their actual HF status.