The use of a new logistic regression model for predicting the outcome of pregnancies of unknown location

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Abstract
The aim of this study was to generate and evaluate new logistic regression models from simple demographic and hormonal data to predict the outcome of pregnancies of unknown location (PULs). Data were collected prospectively from 185 consecutive women classified as having a PUL by transvaginal scan; blood was taken at presentation and 48 h later to measure serum progesterone and HCG. These women were followed-up until the outcome was established: an intrauterine pregnancy (IUP), an ectopic pregnancy (EP) or a failing PUL. Three multi-categorical logistic regression models were tested. M1 was based on the HCG ratio (rate of change in HCG over 48 h), M2 was based on the average progesterone level (the mean of the progesterone level at 0 and 48 h) and M3 was based on the patient's age. A total of 102 failing PULs, 63 IUPs and 20 EPs were used in the training set to develop the new models. The best of these models, M3, gave a retrospective area under the receiver operating characteristic (ROC) curve of 0.984 for failing PUL, 0.995 for IUP and 0.920 for EP. All three models were tested prospectively on the test set of 196 cases. M1 outperformed M2 and M3 when tested prospectively. The area under the ROC curve (AUC) was 0.975 for failing PUL, 0.966 for IUP and 0.885 for EP. M1, for the detection of EP, had a sensitivity of 91.7%, a specificity of 84.2%, a positive likelihood ratio of 5.8, a positive predictive value of 27.5% and a negative predictive value of 99.4%. The logistic regression model M1, can predict which PULs will become failing PULs, IUPs and, most importantly, EPs based on the patient's HCG ratio alone.

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