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Diagnostic protocols for the management of pregnancy of unknown location: a systematic review and meta‐analysis
Author(s) -
Bobdiwala S,
Saso S,
Verbakel JY,
AlMemar M,
Van Calster B,
Timmerman D,
Bourne T
Publication year - 2019
Publication title -
bjog: an international journal of obstetrics and gynaecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.157
H-Index - 164
eISSN - 1471-0528
pISSN - 1470-0328
DOI - 10.1111/1471-0528.15442
Subject(s) - medicine , ectopic pregnancy , meta analysis , receiver operating characteristic , pregnancy , obstetrics , logistic regression , gynecology , medline , genetics , political science , law , biology
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.