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

Authors
  • Bobdiwala, S
  • Saso, S
  • Verbakel, JY
  • Al-Memar, M
  • Van Calster, B
  • Timmerman, D
  • Bourne, T
Publication Date
Aug 01, 2018
Source
UPCommons. Portal del coneixement obert de la UPC
Keywords
Language
English
License
Unknown
External links

Abstract

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 01/01/1984 to 31/01/2017. 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, that 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 (SROC) 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 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 AUCs (95% CI) for these four management protocols were: (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.

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