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Hum Reprod. 2010 Oct;25(10):2434-40. doi: 10.1093/humrep/deq217. Epub 2010 Aug 17.

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

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Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, University of Pennsylvania, 3701 Market Street, Suite 800, Philadelphia, PA 19104, USA.



A logistic regression model (M4) was developed in the UK to predict the outcome for women with a pregnancy of unknown location (PUL) based on the initial two human chorionic gonadotrophin (hCG) values, 48 h apart. The purpose of this paper was to assess the utility of this model to predict the outcome for a woman (PUL) in a US population.


Diagnostic variables included log-transformed serum hCG average of two measurements, and linear and quadratic hCG ratios. Outcomes modeled were failing PUL, intrauterine pregnancy (IUP) and ectopic pregnancy (EP). This model was applied to a US cohort of 604 women presenting with symptomatic first-trimester pregnancies, who were followed until a definitive diagnosis was made. The model was applied before and after correcting for differences in terminology and diagnostic criteria.


When retrospectively applied to the adjusted US population, the M4 model demonstrated lower areas under the curve compared with the UK population, 0.898 versus 0.988 for failing PUL/spontaneous miscarriage, 0.915 versus 0.981 for IUP and 0.831 versus 0.904 for EP. Whereas the model had 80% sensitivity for EP using UK data, this decreased to 49% for the US data, with similar specificities. Performance only improved slightly (55% sensitivity) when the US population was adjusted to better match the UK diagnostic criteria.


A logistic regression model based on two hCG values performed with modest decreases in predictive ability in a US cohort for women at risk for EP compared with the original UK population. However, the sensitivity for EP was too low for the model to be used in clinical practice in its present form. Our data illustrate the difficulties of applying algorithms from one center to another, where the definitions of pathology may differ.

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