4.3.3.2.2 Diagnostic accuracy studies For diagnostic test accuracy studies, a positive result on the index test was found if the patient had values of the measured quantity above or below a threshold value, and different thresholds could be used. The thresholds were prespecified by the committee including whether or not data could be pooled across a range of thresholds. Diagnostic test accuracy measures used in the analysis were: area under the receiver operating characteristics (ROC) curve (AUC), and, for different thresholds (if appropriate), sensitivity and specificity. The threshold of a diagnostic test is defined as the value at which the test can best differentiate between those with and without the target condition. In practice this varies amongst studies. If a test has a high sensitivity then very few people with the condition will be missed (few false negatives). For example, a test with a sensitivity of 97% will only miss 3% of people with the condition. Conversely, if a test has a high specificity then few people without the condition would be incorrectly diagnosed (few false positives). For example, a test with a specificity of 97% will only incorrectly diagnose 3% of people who do not have the condition as positive. For this guideline, sensitivity or specificity was considered more important depending on the test threshold value being considered. For example at standard diagnostic thresholds of BNP and NT-proBNP sensitivity was prioritised as failing to diagnose people who have heart failure may delay the initiation of treatment and increase the risk of unplanned hospitalisations and mortality. While at much higher rule-in thresholds specificity was prioritised as minimising false positives is more important in this context. 