Figure 4

Summary receiver characteristics (sROC) (a) curve- GeneXpert, (b) curve- MODS and (c) curve- WHO 2007 algorithm. Note: sROC = summary receiver operating characteristic curve, which is a plot of the true positive rate (sensitivity) against the false positive rate (1-specificity) of a diagnostic test at different thresholds [47]. This generates a composite statistic (AUC or the Index Q*) that provides an overall evaluation of the accuracy of a test (perfect discriminating ability of true positivity from false positivity). The three curves of the sROC represent the estimate and the 95% upper and lower bounds of the estimate. AUC = Area under the curve of a constructed sROC curve. An AUC close to 1.0 signifies that the test has almost perfect discrimination while an AUC close to 0.5 suggests poor discrimination. An AUC significantly less than 0.5 would indicate that the criteria for “normal” and “abnormal” should be reversed. SE (AUC) = standard error of the area under curve Q* = An index which corresponds to the upper most point on the sROC curve at which sensitivity equals specificity. The closer this value is to 1, the closer the test to perfect accuracy (perfect discriminating ability of true positivity from false positivity). When the value of the Q* index is close to 0.5, it signifies that the test has poor discrimination. SE (Q*) = the standard error of the index Q*.