Rationalizing the management of pregnancies of unknown location: Diagnostic accuracy of human chorionic gonadotropin ratio‐based decision tree compared with the risk prediction model M4

INTRODUCTION: The objective was to compare the diagnostic accuracy of the decision tree analysis prediction model P1, which incorporates the human chorionic gonadotropin (hCG) ratio (hCG 48 hours/hCG 0 hour), and risk prediction model M4 in the management of women with pregnancy of unknown location (PUL). MATERIAL AND METHODS: A retrospective diagnostic accuracy study was performed on PUL data collected between August 2011 and September 2018. Women with a PUL were prospectively managed according to the P1 prediction model, which utilizes the hCG ratio and, if necessary, a day (D) 7 hCG. We compared the performance of P1 with the M4 model, a logistic regression mathematical model using initial hCG and hCG ratio, to classify PULs as low risk (failed PUL [failed] or intrauterine pregnancy) or high risk (ectopic pregnancy or persistent PUL). The reference standard was defined as the final PUL outcome. RESULTS: Transvaginal ultrasound was done in 3847 consecutive women for early pregnancy complications, 437 (11.3%) of whom were classified as PUL. Final analysis comprised 413 cases with complete data. Final PUL clinical outcomes were: 247 (59.8%) failed PUL, 94 (22.7%) intrauterine pregnancy, 49 (11.8%) ectopic pregnancy and 23 (5.5%) persistent PUL. The sensitivity of P1 and M4 in predicting high-risk PUL were 81.9% (95% confidence interval [CI] 71.1-90.0) and 80.6% (95% CI 69.5-88.9), respectively. The specificities were 74.5% (95% CI 69.5-79.1) and 75.6% (95% CI 70.7-80.1), respectively. CONCLUSIONS: P1 and M4 performed similarly with respect to diagnostic accuracy in predicting PUL outcome. P1 needs to be externally validated.