While at much higher rule-in thresholds specificity was prioritised as minimising false positives is more important in this context. Coupled forest plots of sensitivity and specificity with their 95% CIs across studies (at various thresholds) were produced for each test, using RevMan5.283 In order to do this, 2 2 tables (the number of true positives, false positives, true negatives and false negatives) were directly taken from the study if given, or else were derived from raw data or calculated from the set of test accuracy statistics. Diagnostic meta-analysis was conducted where appropriate, that is, when 3 or more studies were available per threshold. Test accuracy for the studies was pooled using the bivariate method for the direct estimation of summary sensitivity and specificity using a random-effects approach in WinBUGS Chronic Heart Failure Methods NICE 2018. All rights reserved. Subject to Notice of rights. ISBN: 978-1-4731-3093-7 The advantage of this approach is that it produces summary estimates of sensitivity and specificity that account for the correlation between the 2 statistics. Other advantages of this method The bivariate method uses logistic regression on the true positives, true negatives, false positives and false negatives reported in the studies. Overall sensitivity and specificity and confidence regions were plotted (using methods outlined by Novielli 2010.284 ) Pooled sensitivity and specificity and their 95% CIs were reported in the clinical evidence summary tables. For scores with fewer than 3 studies, each study s sensitivity and the paired specificity were reported where possible. Heterogeneity or inconsistency amongst studies was visually inspected in the forest plots and pooled diagnostic meta-analysis plots. The following criteria were used for evaluating AUCs: 0.50: worse than chance 0.50 0.60: very poor 0.61 0.70: poor 0.71 0.80: moderate 0.81 0.90: good 0.91 1.00: excellent or perfect test. 