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

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Abstract
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 patients with clinical symptoms, for example, the probability of presence of an ectopic pregnancy is higher than in symptom-free patients. Any additional tests should then be interpreted differently, depending on the pre-test probability. We present a diagnostic algorithm that uses probabilistic decision rules for the evaluation of women with suspected ectopic pregnancy with flexible cut-off levels for test positivity We compare it with a general algorithm that uses fixed cut-off levels. Fictitious cohorts, varying in prevalence of ectopic pregnancy were put together, using data obtained from a cohort of >800 women with suspected ectopic pregnancy. In the inflexible algorithm, ectopic pregnancy was diagnosed whenever it could be visualized at transvaginal sonography, or where serum human chorionic gonadotrophin (HCG) exceeded a rigid cut-off level; ectopic pregnancy was rejected if an intrauterine pregnancy was seen or when serum HCG decreased. In the flexible algorithm, a post-test probability was obtained after each test, using pre-test probabilities and test-based likelihood ratios. Ectopic pregnancy was diagnosed whenever the post-test probability for ectopic pregnancy exceeded 95%, whereas this diagnosis was rejected if the calculated post-test probability fell below 1%. For both algorithms, sensitivity and specificity as well as predictive values were calculated. At each prevalence, the inflexible algorithm was associated with a sensitivity of 93% and a specificity of 97%. In contrast, the sensitivity and specificity of the flexible, individualized algorithm depended on the prevalence of ectopic pregnancy. Consequently, predictive values varied strongly when the inflexible algorithm was used, whereas they were much more stable after using the flexible algorithm. For five possible valuations of false positive and false negative diagnoses, the flexible algorithm reduced the expected disutility, compared with the inflexible algorithm. It is concluded that clinicians should incorporate probabilistic decision rules in algorithms used for the diagnosis of ectopic pregnancy.