Implementation of probabilistic decision rules improves the predictive values of algorithms in the diagnostic management of ectopic pregnancy

Current algorithms for the diagnosis of ectopic pregnancy do not take into account the heterogeneity in patient profiles. Such heterogeneity can lead to differences in the pre-test probability of ectopic pregnancy. In patient s with clinical symptoms, for example, the probability of presence of an ec topic pregnancy is higher than in symptom-free patients. Any additional tes ts should then be interpreted differently, depending on the pre-test probab ility. We present a diagnostic algorithm that uses probabilistic decision r ules for the evaluation of women with suspected ectopic pregnancy with flex ible cut-off levels for test positivity. We compare it with a general algor ithm that uses fixed cut-off levels, Fictitious cohorts, varying in prevale nce of ectopic pregnancy were put together, using data obtained from a coho rt of >800 women with suspected ectopic pregnancy. In the inflexible algori thm, ectopic pregnancy was diagnosed whenever it could be visualized at tra nsvaginal sonography, or where serum human chorionic gonadotrophin (HCG) ex ceeded a rigid cut-off level; ectopic pregnancy was rejected if an intraute rine pregnancy was seen or when serum HCG decreased. In the flexible algori thm, a posttest probability was obtained after each test, using pretest pro babilities and test-based likelihood ratios. Ectopic pregnancy was diagnose d whenever the post-test probability for ectopic pregnancy exceeded 95%, wh ereas this diagnosis was rejected if the calculated post-test probability f ell below 1%, For both algorithms, sensitivity and specificity as well as p redictive values were calculated. At each prevalence, the inflexible algori thm was associated with a sensitivity of 93% and a specificity of 97%, In c ontrast, the sensitivity and specificity of the flexible, individualized al gorithm depended on the prevalence of ectopic pregnancy. Consequently, pred ictive values varied strongly when the inflexible algorithm was used, where as they were much more stable after using the flexible algorithm. For five possible valuations of false positive and false negative diagnoses, the fle xible algorithm reduced the expected disutility, compared with the inflexib le algorithm. It is concluded that clinicians should incorporate probabilis tic decision rules in algorithms used for the diagnosis of ectopic pregnanc y.