Literature DB >> 17486691

Prediction of ectopic pregnancy in women with a pregnancy of unknown location.

G Condous1, B Van Calster, E Kirk, Z Haider, D Timmerman, S Van Huffel, T Bourne.   

Abstract

OBJECTIVE: We have previously published on the use of mathematical Model M1 to predict ectopic pregnancy in women with no signs of intra- or extrauterine pregnancy. The aim of this study was to improve on the performance of this model for the detection of developing ectopic pregnancies in women with pregnancies of unknown location (PULs). We therefore generated and evaluated a new logistic regression model from simple hormonal data and compared it with Model M1.
METHODS: Data were collected prospectively from women classified as having a PUL. These women were followed until the diagnosis was established as: a failing PUL, an intrauterine pregnancy (IUP) or an ectopic pregnancy. A multinomial logistic regression model, Model M4, was developed on 201 training cases and it was tested prospectively on another 175 women with a PUL. M4 performance was evaluated using receiver-operating characteristics (ROC) curves and compared with Model M1 based on the human chorionic gonadotropin (hCG) ratio alone.
RESULTS: A total of 376 women with a PUL were recruited into this study: 201 in the training set (109 (54.2%) with a failing PUL, 76 (37.8%) with an IUP and 12 (6.0%) with an ectopic pregnancy; four with a persisting PUL were excluded from analysis) and 175 in the test set (94 (53.7%) with a failing PUL, 64 (36.6%) with an IUP and 15 (8.6%) with an ectopic pregnancy; two with a persisting PUL were excluded from analysis). The log serum hCG average ((hCG 0 h + hCG 48 h)/2) and the hCG ratio (hCG 48 h/hCG 0 h) were encoded as variables following multivariate analysis of the basic data. The new Model M4 contained the log of the hCG average, the hCG ratio and its quadratic effect. In the prediction of ectopic pregnancy, M4 gave an area under the ROC curve (AUC) of 0.900 and M1 gave an AUC of 0.842 (P = 0.0303).
CONCLUSIONS: Although Model M4 is superior to Model M1 when comparing the AUCs for prediction of developing ectopic pregnancies in a PUL population, in real terms this model did not result in substantially more pregnancies being classified correctly as developing ectopic pregnancies. Prospective multicenter studies are needed to assess the diagnostic performance of such models in different populations.

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Year:  2007        PMID: 17486691     DOI: 10.1002/uog.4015

Source DB:  PubMed          Journal:  Ultrasound Obstet Gynecol        ISSN: 0960-7692            Impact factor:   7.299


  17 in total

1.  The clinical performance of the M4 decision support model to triage women with a pregnancy of unknown location as at low or high risk of complications.

Authors:  S Bobdiwala; S Guha; B Van Calster; F Ayim; N Mitchell-Jones; M Al-Memar; H Mitchell; C Stalder; C Bottomley; A Kothari; D Timmerman; T Bourne
Journal:  Hum Reprod       Date:  2016-05-10       Impact factor: 6.918

2.  Ectopic pregnancy prediction in women with a pregnancy of unknown location: data beyond 48 h are necessary.

Authors:  J Zee; M D Sammel; K Chung; P Takacs; T Bourne; K T Barnhart
Journal:  Hum Reprod       Date:  2013-12-18       Impact factor: 6.918

Review 3.  Biomarkers for ectopic pregnancy and pregnancy of unknown location.

Authors:  Suneeta Senapati; Kurt T Barnhart
Journal:  Fertil Steril       Date:  2013-01-03       Impact factor: 7.329

4.  Performance of human chorionic gonadotropin curves in women at risk for ectopic pregnancy: exceptions to the rules.

Authors:  Christopher B Morse; Mary D Sammel; Alka Shaunik; Lynne Allen-Taylor; Nicole L Oberfoell; Peter Takacs; Karine Chung; Kurt T Barnhart
Journal:  Fertil Steril       Date:  2012-01       Impact factor: 7.329

5.  Pregnancy of unknown location: a consensus statement of nomenclature, definitions, and outcome.

Authors:  Kurt Barnhart; Norah M van Mello; Tom Bourne; Emma Kirk; Ben Van Calster; Cecilia Bottomley; Karine Chung; George Condous; Steven Goldstein; Petra J Hajenius; Ben Willem Mol; Thomas Molinaro; Katherine L O'Flynn O'Brien; Richard Husicka; Mary Sammel; Dirk Timmerman
Journal:  Fertil Steril       Date:  2010-10-14       Impact factor: 7.329

6.  Does a prediction model for pregnancy of unknown location developed in the UK validate on a US population?

Authors:  K T Barnhart; M D Sammel; D Appleby; M Rausch; T Molinaro; B Van Calster; E Kirk; G Condous; S Van Huffel; D Timmerman; T Bourne
Journal:  Hum Reprod       Date:  2010-08-17       Impact factor: 6.918

7.  The potential value of activin B and fibronectin for the triage of pregnancies of unknown location and prediction of first trimester viability.

Authors:  Maya Al-Memar; Shabnam Bobdiwala; Mayank Madhra; Srdjan Saso; Bavo De Cock; Ben Van Calster; Jeremy K Brown; Faizah Mukri; Cecilia Bottomley; Aris Papageorghiou; Dirk Timmerman; Andrew W Horne; Tom Bourne
Journal:  Australas J Ultrasound Med       Date:  2018-04-25

8.  Validation of a clinical risk scoring system, based solely on clinical presentation, for the management of pregnancy of unknown location.

Authors:  Kurt T Barnhart; Mary D Sammel; Peter Takacs; Karine Chung; Christopher B Morse; Katherine O'Flynn O'Brien; Lynne Allen-Taylor; Alka Shaunik
Journal:  Fertil Steril       Date:  2012-10-03       Impact factor: 7.329

9.  Prediction of location of a symptomatic early gestation based solely on clinical presentation.

Authors:  Kurt T Barnhart; Bruno Casanova; Mary D Sammel; Kelly Timbers; Karine Chung; J L Kulp
Journal:  Obstet Gynecol       Date:  2008-12       Impact factor: 7.661

10.  The term "pregnancy of unknown location" is here to stay.

Authors:  George Condous; Simon Winder; Shannon Reid
Journal:  Australas J Ultrasound Med       Date:  2015-12-31
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