Diagnostic protocols for the management of pregnancy of unknown location: a systematic review and meta-analysis

© 2018 Royal College of Obstetricians and Gynaecologists Background: There is no international consensus on how to manage women with a pregnancy of unknown location (PUL). Objectives: To present a systematic quantitative review summarising the evidence related to management protocols for PUL. Search strategy: MEDLINE, COCHRANE and DARE databases were searched from 1 January 1984 to 31 January 2017. The primary outcome was accurate risk prediction of women initially diagnosed with a PUL having an ectopic pregnancy (high risk) as opposed to either a failed PUL or intrauterine pregnancy (low risk). Selection criteria: All studies written in the English language, which were not case reports or series that assessed women classified as having a PUL at initial ultrasound. Data collection and analysis: Forty-three studies were included. QUADAS-2 criteria were used to assess the risk of bias. We used a novel, linear mixed-effects model and constructed summary receiver operating characteristic curves for the thresholds of interest. Main results: There was a high risk of differential verification bias in most studies. Meta-analyses of accuracy were performed on (i) single human chorionic gonadotrophin (hCG) cut-off levels, (ii) hCG ratio (hCG at 48 hours/initial hCG), (iii) single progesterone cut-off levels and (iv) the ‘M4 model’ (a logistic regression model based on the initial hCG and hCG ratio). For predicting an ectopic pregnancy, the areas under the curves (95% CI) for these four management protocols were as follows: (i) 0.42 (0.00–0.99), (ii) 0.69 (0.57–0.78), (iii) 0.69 (0.54–0.81) and (iv) 0.87 (0.83–0.91), respectively. Conclusions: The M4 model was the best available method for predicting a final outcome of ectopic pregnancy. Developing and validating risk prediction models may optimise the management of PUL. Tweetable abstract: Pregnancy of unknown location meta-analysis: M4 model has best test performance to predict ectopic pregnancy.